climate - research paper
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Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems$
Bruce M Campbell1,2, James Hansen2, Janie Rioux3, Clare M Stirling4, Stephen Twomlow5 and Eva (Lini) Wollenberg6
Available online at www.sciencedirect.com
ScienceDirect
Actionsonclimatechange(SDG13),includinginthefoodsystem,
are crucial. SDG 13 needs to align with the Paris Agreement,
given that UNFCCC negotiations set the framework for climate
change actions. Food system actions can have synergies and
trade-offs, as illustrated by the case for nitrogen fertiliser. SDG
13 actions that reduce emissions can have positive impacts on
other SDGs (e.g. 3, 6, 12, 14, 15); but such actions should not
undermine the adaptation goals of SDG 13 and SDGs 1, 2, 5 and
10. Balancing trade-offs is thus crucial, with SDG 12 central:
responsible consumption and production. Transformative
actions in food systems are needed to achieve SDG 13 (and other
SDGs), involving technical, policy, capacity enhancement and
finance elements. But transformative actions come with risks, for
farmers, investors, development agencies and politicians. Likely
short and long term impacts need to be understood.
Addresses 1 CGIAR Research Program on Climate Change, Agriculture, and Food
Security (CCAFS), International Center for Tropical Agriculture (CIAT), c/
o University of Copenhagen, Rolighedsvej 21, Frederiksberg C DK-1958,
Denmark 2 International Research Institute for Climate and Society, Columbia
University, Lamont Campus, PO Box 1000, Palisades, NY 10964-8000,
USA 3 Green Climate Fund, 175 Art Center-daero, Yeonsu-gu, Incheon 22004,
Republic of Korea 4 International Maize and Wheat Improvement Center (CIMMYT), Apdo.
Postal 6-641, 06600 México, D.F., Mexico 5 International Fund for Agricultural Development, Via Paolo di Dono, 44
00142 Rome, Italy 6 University of Vermont (UVM), Burlington, VT, USA
Corresponding author: Campbell, Bruce M (b.campbell@cgiar.org)
Current Opinion in Environmental Sustainability 2018, 34:13–20
This review comes from a themed issue on Sustainability science
Edited by Ken E Giller, Ira Martina Drupady, Lorenza B Fontana &
Johan A Oldekop
For a complete overview see the Issue
Available online 14th August 2018
Received: 12 January 2018; Accepted: 15 June 2018
https://doi.org/10.1016/j.cosust.2018.06.005
1877-3435/ã 2018 CGIAR Research Program on Climate Change, Agriculture and Food Security (CGIAR-CCAFS). Published by Elsevier
B.V. This is an open access article under the CC BY license (http://
creativecommons.org/licenses/by/4.0/).
$ This article was presented at the AGRI2017 congress. Main results of t
Biology, volume 45, 2018.
www.sciencedirect.com
Introduction Climate change is regarded by many as a defining challenge
of our times [1] and thus it is not surprising that one of the
SDGs (13) concerns ‘urgent action to combatclimate change
and its impacts’. Meta-analysis of impacts of climate change
shows 70% of studies with declines in crop yields by 2030s,
with half the studies having 10–50% declines [2]. Climate
extremes may exceed critical thresholds for agriculture;
effective mechanisms to reduce production risk will be
needed [3]. Climate change is already affecting food sys-
tems, and agriculture is one of the sectors expected to be
most impacted by climate change [4]. Impacts on food
systems are expected to be widespread, complex, and geo-
graphically and temporally variable [5 �� ]. Globally, agricul-
ture and related land use change contribute nearly a quarter
of annual GHG emissions, �10–12 Gt CO2e yr�1 [6]. Con- siderableemissionsreductionwillbeneededinfoodsystems
if the global warming target is not to be exceeded [7 �� ]. Thus
achieving SDG 13 will require many actions for adaptation
andmitigation infoodsystems.Amajorchallengeisthatfood
systems are linked to many SDGs and there are likely to be
trade-offs amongst SDGs through food system actions [8,9];
with trade-offs particularly challenging in developing coun-
tries where climate change vulnerability will be highest.
This paper examines SDG 13 and how it links to food
system actions, with particular attention to agriculture in
developing countries. It argues for the need for SDG
13 being closely aligned with the Paris Agreement and
other UNFCCC agreements. Particular attention needs
to be paid to the trade-offs and synergies amongst SDGs,
as shown in a case study of nitrogen fertiliser. A transfor-
mative approach is essential in food systems if the climate
change challenge is to be addressed, while also addressing
other SDGs. Transformation will have many elements:
technical, policy, capacity enhancement and finance; and
both the likely short and long term impacts of transfor-
mative actions need to be understood if negative impacts
to particular stakeholder groups are to be avoided.
SDG 13 — strengths and limitations; and links to food systems SDG 13 considers both adaptation and mitigation, and
includes foci on: strengthening resilience; integrating cli-
mate change measures into national policies and planning;
his congress will be published in the journal Current Opinion on Plant
Current Opinion in Environmental Sustainability 2018, 34:13–20
14 Sustainability science
monitoring progresstowardsclimate financialcommitments;
and, improving capacity on climate change, especially in
Least Developed Countries (LDCs) and small island devel-
oping States (SIDS), and amongst women, youth and mar-
ginalized communities (Table 1, first column).
SDG 13 largely covers processes towards outcomes (see
indicators in Table 1, second column) rather than out-
comes themselves, and lacks a mitigation target. Many
SDGs — unlike SDG 13 — do include indicators that
capture what needs to be ultimately achieved by those
SDGs. For example:
� SDG 1 (no poverty): Proportion of population below the international poverty line.
Table 1
SDG 13 targets and indicators (abbreviated) and potential contributio
SDG targets SDG indicators
13.1 Strengthen resilience and adaptive
capacity to climate-related hazards and
natural disasters in all countries
Number of deaths, missing p
directly affected persons attri
per 100 000 population
Number of countries that ado
national disaster risk reducti
Proportion of local governme
implement local disaster risk
strategies
13.2 Integrate climate change measures
into national policies, strategies and
planning
Number of countries that ha
the establishment or operatio
integrated policy/strategy/pla
increases their ability to ada
change, and foster climate r
GHG development in a mann
threaten food production
13.3 Improve education, awareness-raising
and human and institutional capacity on
climate change mitigation, adaptation,
impact reduction and early warning
Number of countries that ha
mitigation, adaptation, impac
early warning into curricula
Number of countries that ha
the strengthening of institutio
individual capacity-building t
adaptation, mitigation and te
and development actions
13.a Implement the commitment
undertaken by developed-country Parties
to a goal of mobilizing $100 billion
annually by 2020 to address needs of
developing countries
Amount mobilized per year b
2025 accountable towards th
commitment
13.b Promote mechanisms for raising
capacity for effective climate change-
related planning and management in
LDCs and small island developing States,
including focusing on women, youth and
local and marginalized communities
Number of least developed c
island developing states that
specialized support, and am
including finance, technology
building, for mechanisms for
for effective climate change-
and management, including
women, youth and local and
communities
Current Opinion in Environmental Sustainability 2018, 34:13–20
� SDG 2 (zero hunger): Prevalence of moderate or severe food insecurity in the population.
� SDG 12 (responsible consumption and production): Global food loss index.
� SDG 14 (life below water): Average marine acidity (pH) measured at agreed suite of representative sam-
pling stations.
The main negotiating forum for climate change is the
UNFCCC, and the SDGs were agreed prior to the
UNFCCC Paris Agreement, so it is not surprising that
the Paris Agreement is more comprehensive than SDG
13. The Paris Agreement specifies the 2 �C goal, com- munication of nationally determined contributions
(NDCs), need for transparency in reporting, agreements
n by food system actors
Food system actions and monitoring
ersons and
buted to disasters
‘Directly affected’ implies goals of reducing the
number of people falling into food insecurity after a
climate related hazard, and limiting the impacts on
national food production
pt and implement
on strategies
Indicator linked to the Sendai Framework for
Disaster Risk Reduction, which calls for integration
of disaster risk reduction across sectors including
food security and nutrition. Key to document how
effectively disaster risk reduction is integrated into
agriculture strategies and food security
management.
nts that adopt and
reduction
Sendai Framework calls for local government to
integrate disaster risk reduction across sectors
including food security and nutrition.
ve communicated
nalization of an
n which
pt to the climate
esilience and low
er that does not
Key to establish and operationalize agriculture and
food security policies/strategies/plans that
address adaptation and mitigation of climate
change; and/or climate change policies/strategies/
plans that address agriculture and food security.
Important to assess whether action has occurred in
priority countries for mitigation and adaptation.
ve integrated
t reduction and
Key to ensure that agriculture/food related
curricula integrate climate change
ve communicated
nal, systemic and
o implement
chnology transfer,
Important to build capacity in the agriculture and
food security sectors to deal with climate change,
but also to build capacity in other sectors (e.g.
finance and environment) to deal with climate–
agriculture issues.
etween 2020 and
e $100 billion
(see below)
ountries and small
are receiving
ount of support,
and capacity-
raising capacities
related planning
focusing on
marginalized
Important to track the degree to which climate
change funds (from goal 13.a) are allocated to
LDCs and SIDS; how these are earmarked against
different sectors; whether they are earmarked for
adaptation and/or mitigation; and how they focus
on women, youth and local and marginalized
communities. The Green Climate Fund (GCF) has
the ambition that 50% of its funds go to LDCs, SIDS
and Africa, that 50% goes to adaptation and 50%
to mitigation.
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Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems Campbell et al. 15
on mobilizing climate finance, adaptation goals, and
avoiding and compensating for loss and damage. SDG
13 therefore needs to be closely aligned with UNFCCC
agreements.
From the SDG 13 indicators, we can derive some of the
actions and monitoring needed by food system actors to
combat climate change (Table 1, third column) but this is
a limited set. More detail can be gained by examining
country NDCs, but even here ambition levels may be
insufficient to address climate change [10], and few reflect
the transformative actions needed (see below).
Trade-offs among SDGs A goal of the SDGs and 2030 Agenda is to increase policy
coherence and reduce trade-offs among sectoral policies
[11,12]. To implement the SDGs in an integrated way,
SDG 13 policy and action should be guided by their
interactions with other SDGs and the institutions imple-
menting them. Actions on SDG 13 have interactions with
many SDGs, as discussed in this section and with a
specific case study on nitrogen fertiliser in the next
section. Climate acts as a dynamic driver of the sustain-
ability of food systems and the conditions affecting it:
water, land, oceans, and hazards [5 �� ,13
� ]. The impacts of
Figure 1
6
3
14
15
12 1
13
GOOD HEA AND WELL
CLEAN WATER AND SANITATION
LIFE BELOW WATER
Transform
CLIMA ACTIO
RESPONSIBLE CONSUMPTION AND PRODUCTION
LIFE
ON LAND
Strong synergies
Reduced food waste
Reduced environmental
footprint
Food consumption shifts
Developed economies
Agricultu food sy
actio
Mitigation actions
Some trade-o
Relationships of climate change actions in the food system to sustainable d
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climate on food systems in turn affect poverty, health,
economics, infrastructure, equity and gender relations
[5 �� ]. Climate change is also driven by food systems,
energy, and unsustainable consumption and production,
creating feedback effects. From a development perspec-
tive, achieving adaptation and mitigation in food systems
will require success in other SDGs as enabling conditions
of SDG 13, such as sustainable production and consump-
tion (12), food security (2), poverty reduction (1), educa-
tion (4), gender equity (5), water (6), life on land (15) and
energy (7). Geographic, technical and governance con-
texts affect the specific nature of the interactions [11].
Major synergies occur between adaptation in SDG 13 and
food security, poverty, and equity (Figure 1, right side).
Synergies can also be expected to increase between
mitigation in SDG 13 with efficiencies in energy, water
and nutrient inputs in agriculture (Figure 1, left side).
Reducing loss in the food supply chain to support sus-
tainable production and consumption could reduce emis-
sions between 15 and 30% [14].
A major trade-off is potentially the goal of forest conser-
vation under SDG 15, which should limit agricultural
expansion. The major sources of remaining arable land
1
2
7
5
10
LTH -BEING
NO POVERTY
ation
ZERO HUNGER
GENDER EQUALITY
REDUCED INEQUALITIES
TE N
PARTNERSHIPS FOR THE GOALS
Less developed economies
Risk reduction
Yield increases
Reduced production
losses
Better functioning
marketsre and stem ns
Adaptation actions
Strong synergies
ffs
Current Opinion in Environmental Sustainability
evelopment goals.
Current Opinion in Environmental Sustainability 2018, 34:13–20
16 Sustainability science
are in countries such as Brazil and DR Congo. Deforesta-
tion and agriculture production need to be decoupled, as
has occurred to some degree in Brazil. Also, investments
in mitigation in the food sector may reduce equity, if
mitigation finance targets larger farmers and high emis-
sion countries at the expense of others.
Some interactions have mixed effects; 14% of global
emissions come from livestock and a shift in diet aligned
with WHO guidance that would reduce livestock con-
sumption could reduce emissions technically up to
1.37 CO2 yr �1
in 2030 [15]. Yet livestock are fundamental
to the adaptive capacity of tens of millions of smallholder
farming households, through meat and milk production,
manure for crop production, transport and traction.
Although potential interactions can be anticipated, to
mobilize change and achieve ambitious targets in SDG
13 for food systems, better information about these inter-
actions and the actual impacts of climate action and
responses to climate will be necessary [16,17]. Spatial
and temporal monitoring of targets and their interactions
will be needed [18].
Country priorities will vary, with developing countries
focusing on production, food security and adaptation, and
developed countries focusing more on the environmental
impacts of food systems and mitigation.
Figure 2
12
6 CA
Too little
Optimum
Too much
Fertiliser -N
Impact of fertiliser nitrogen (N) use on the achievement of Sustainable Deve
levels of fertiliser N are consumed.
Current Opinion in Environmental Sustainability 2018, 34:13–20
Case study: nitrogen fertiliser and the SDGs A specific case demonstrates some of the interactions
amongst SDGs. Global N fertiliser consumption has
increased by almost 100 Tg N yr �1
between 1961 and
2013 [19]. Further increases in crop production require
that fertiliser is managed sustainably to avoid negative
trade-offs that could undermine the multiple SDGs that
N impacts (Figure 2). The most obvious trade-off is the
need to increase N to meet SDG 2 whilst reducing N to
support SDGs 6, 13, 14 and 15. The key is judicious N
consumption, and thus SDG 12 is central: responsible
consumption and production.
Too little N
Wide variation exists in fertiliser use. For example, Sub-
Saharan Africa accounts for less than 2% of world fertiliser
N consumption (mean rate, excluding South Africa:
7 kg N ha �1 ) while China consumes ca. 30% of world
consumption (565 kg N ha �1 ). In some regions of Latin
America and Asia and across most of Sub-Saharan Africa
too little fertiliser N use results in soil nutrient mining and
low yields. Improved access to fertiliser N will be critical
to ending poverty (SDG 1) and hunger (SDG 2) and
improving health (SDG 3).
Too much N
The opposite of this is that too much N fertiliser results in
significant N losses, contributing to groundwater
1 NOPOVERTY 2 ZERO HUNGER 3
GOOD HEALTH AND WELL-BEING
RESPONSIBLE CONSUMPTION AND PRODUCTION
13 CLIMATEACTION 14 LIFE
BELOW WATER 15 LIFE ON LAND
LEAN WATER ND SANITATION
Current Opinion in Environmental Sustainability
lopment Goals and for situations where too little, too much or optimal
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Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems Campbell et al. 17
contamination, eutrophication of freshwater and estua-
rine ecosystems, atmospheric pollution, and soil acidifi-
cation and degradation. Nitrogen run-off and leaching are
responsible for toxic aquatic algal blooms, fish death and
loss of biodiversity, which undermine the realisation of
SDGs 6, 14 and 15. Fertiliser N is also responsible for
more than 30% of agricultural-related N2O emissions with
agriculture being the major source (ca. 60%) of global
N2O emissions. Approximately 70% of fertiliser-related
N2O emissions derive from countries with emerging
economies such as China and India where fertiliser con-
sumption rates have grown rapidly due to fertiliser N
subsidies whilst crop yield responses to N have stagnated
[20,21]. By contrast, effectively targeted policies have
resulted in a decline or reversal of growth in fertiliser
N use in Western Europe and Australia whilst crop yields
have continued to improve [22]. Well-targeted policies in
the Netherlands have reduced fertiliser use to the same
level as in 1960s whilst yields have doubled [21].
Optimal N
Precision N management offers a means of achieving the
SDGs through better N management on both large and
small farms. For example, a range of precision N tools and
techniques can support best fertiliser management on
smallholder farms, such as chlorophyll meters, the leaf
colour chart or optical sensors (e.g. GreenSeeker) for
guiding in-season N management. Similarly, decision
support software (e.g. Nutrient Expert, Crop Manager)
is being used to refine N management practices, and such
tools have become increasingly important in geographies
where blanket fertilizer recommendations have been the
Figure 3
Visioning and ex- ante analysis
Incentives
Consumers driving new
responses by private sector
De-risking private finance
Dealing with up-front
investments
Insurance incentivizing technology
uptake Productive
social safety nets Strong rural
differentiation Prioritisatio
of options
Capacity enhancement
Context-sp transformative tailored to geo
socio-economic and agro-ec
Capacity, and enabling policy and institutions
Credit and insurance
Expanded private sector activity
and PPPs
Prioritisation and pathways of
change
Elements of a theory of change to drive agricultural transformation under cl
and issues.
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norm. As broadcast-N application is a major source of
nutrient loss, drilling of fertiliser N or fertigation using
drip irrigation can precisely place N near the root zone
thereby reducing losses. In Indo-Gangetic plains of India,
both the Nutrient Expert and GreenSeeker-based nutri-
ent management have increased the partial factor pro-
ductivity of nitrogen in wheat compared with state-
recommended and farmers’ fertilizer practice. Through
on-farm comparison in over 4000 farmers’ fields across
Indo-Gangetic plains of India, CIMMYT found that
‘nutrient expert’-based management reduced GHG
intensity of rice, wheat and maize production by 5–
35% (average 13%).
Transforming food systems to tackle food security under climate change What will it take to increase agricultural productivity (e.g.
especially in sub-Saharan Africa), enhance food security,
get rural communities out of poverty, build resilience to
climate change and other stresses, reduce agricultural
emissions and other agricultural environmental impacts,
and improve diets and health outcomes? What will it take
to balance the trade-offs amongst SDGs, as demonstrated
by the N case study? The challenges are immense and call
for nothing short of a transformation in food systems, with
highly specific actions depending on context. Food sys-
tems are indeed transforming in many places [23 �� ], but
many scholars argue that transformation will have to be
much greater in the coming years, from the perspective of
food security [24], climate change [25] and environmental
sustainability [13 � ]. We propose a theory of change
embracing eight closely linked elements (Figure 3).
n Scaling up
Effective research and
innovation systems
Big data and ICT
Close collaboration
amongst extension,
meteorological and emergency
response agencies
Two-way extensions
Empowering farmers
ecific actions, graphy, conditions ology
Promoting local action
Strong farmer organisations
and networking
Climate-informed advisories and early warning
Digital agriculture
Climate-resilient and low-emission
practices and technologies
Current Opinion in Environmental Sustainability
imate change, showing the eight key elements, and associated trends
Current Opinion in Environmental Sustainability 2018, 34:13–20
18 Sustainability science
Element #1: expanded private sector activity and public–
private partnerships (PPPs)
The current levels of development and climate finance
will be insufficient to tackle the challenges ahead and
thus private sector investment needs to be stimulated,
including, for example, through climate finance that de-
risks private finance [26,27]. However, there is seldom
perfect alignment between private and public interests.
With continuing urbanization in many developing coun-
tries, wealthier populations and changing consumer
demands the food sector is going to become more
dynamic, with the private sector — both small and large
enterprises — likely to rise to the challenge of the chang-
ing demands.
Element #2: credit and insurance
Efforts to increase availability and access to credit and
insurance need to be greatly scaled up, as credit and risk
are factors holding back investment by smallholders in
climate-resilient technologies and practices [28]. Insur-
ance, and in particular index-based insurance with its
lower transaction costs and rapid pay-outs, can be key
to unlocking credit, as well as providing the usual protec-
tive functions. Many climate-smart investments require
up-front investments (e.g. establishing trees in agrofor-
estry systems) — innovative finance and credit can offset
such up-front investments.
Element #3: strong local organisations and networking
Local institutions and networks are important in fostering
climate action [29,30 � ]. Farmers’ groups, producer groups,
water use associations, women’s groups and other such
groups need a strong voice to demand the needed services
from service providers, and to negotiate with often pow-
erful private sector players.
Element #4: climate-informed advisories and early
warning
Knowledge is key to building adaptive capacity and
helping farmers, their service providers and value chain
actors deal with climate variability [31]. Farmers in most
developing countries are faced by poor extension, with
too few extensionists at farm level, and messages often
being top-down generic messages not relevant in many
contexts. Farmer advisories can be linked to climate
forecasts, to help them select varieties, and plan for
planting, field management operations and harvesting
[32,33 � ]. Appropriate climate-informed advisories can
stimulate production, reduce input costs, reduce post-
harvest losses and reduce emissions (e.g. through better
timing of fertilizer applications). There needs to be a
continuum between ‘normal’ variability-related advi-
sories on the one hand and early warning and emergency
response for extreme events on the other [34]. Close
collaboration and coordination between national meteo-
rological services, national extension services and emer-
gency response agencies, can increase production, build
Current Opinion in Environmental Sustainability 2018, 34:13–20
resilience and enhance social protection. Key will be
functioning extension advisory services and national
meteorological services accountable for the products they
deliver.
Element #5: digital agriculture
Big data and ICT is transforming society [35] and is likely
to revolutionize extension, as data from millions of farm-
ers is combined with data from other sources (e.g. remote
sensing, crop models, sensors) to better tailor information
and services. ICT can also promote two-way extension,
with farmers getting answers for specific questions they
ask, giving feedback to extension messages so that exten-
sion can be further tailored and improved, and contribut-
ing to early warning systems (e.g. by providing informa-
tion on pest outbreaks). Facilitating access to smart
phones and improving connectivity to internet could
be a crucial to drive food system transformations in
developing countries.
Element #6: climate-resilient and low-emission practices
and technologies
Agricultural practices and technologies, including for
post-harvest operations, will be a key part of the trans-
formational agenda. There are numerous practices and
technologies that will assist in adaptation, with many also
having emission-reducing potential [36]. These include,
for example, agroforestry, that diversifies livelihoods and
landscapes and builds carbon stocks; aquaculture, that
meets the rising demand for animal protein and has the
ability to diversify farmer incomes, and enhance resil-
ience and nutrition; improved feed in dairy, which
enhances animal resilience and health, diversifies liveli-
hoods and reduces emission intensities; and responsible
and sustainable fertiliser N management (as described in
the case study). Many appropriate practices and technol-
ogies already exist, and the challenge is getting them
widely used — the other seven elements of this transfor-
mation theory of change are intended to address the
scaling challenge. Effective research and innovation sys-
tems are also needed — to continuously improve prac-
tices and technologies.
Element #7: prioritisation and pathways of change
Given the strong differentiation already in rural areas,
and the asset differences amongst, for example, men and
women, old and young, and peri-urban and distant farm-
ers, a transformational agenda will have different effects
on different kinds of stakeholders, thus the need to
recognise different pathways for change [29]. For exam-
ple, some farmers will be unable to respond to market-
led development. Therefore well-designed social pro-
tection programs, involving cash and in-kind transfers to
very poor and vulnerable households, can protect and
rebuild productive assets and hence protect livelihood
opportunities in the face of extreme climate events [37].
Adaptive social protection innovations, such as
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Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems Campbell et al. 19
integration with credit, production inputs, agricultural
extension and risk finance, increase the responsiveness
of such programs to climate shocks. Choices of practices
and technologies, types of credit and insurance, means of
extension, and so on, should all be driven by careful
prioritisation approaches [38], given the social and envi-
ronmental variation in rural areas, and differing national
contexts.
Element #8: capacity, and enabling policy and
institutions
Each of the above elements of a transformational agenda
is ultimately dependent on an enabling policy and insti-
tutional environment, including capacity enhancement of
key actors, to provide the conditions and incentives to
help businesses expand and invest, incentivize the
uptake of insurance and credit, expand markets and
availability of inputs, foster strong farmer and other local
groups, greatly expand extension, connectivity and avail-
ability of mobile devices, create incentives for technolog-
ical advances, help reduce food loss and waste, and
contribute to shaping consumption patterns and
improved diets. While many of the policy and institu-
tional advances will be at national levels, supra-national
policies and institutions are also important (e.g. related to
trade, development, climate change) [39]. Policy actions
also need to tackle undesirable trade-offs amongst SDG
goals. These include environmental trade-offs, for exam-
ple improved profitability of agricultural systems can
drive deforestation and thus the need for forest gover-
nance policies to complement market policies in agricul-
ture [40]. Transformative actions come with risks, for
farmers, investors, development agencies and politicians.
Likely short and long term impacts therefore need to be
understood, for example, through visioning and ex-ante
analysis [41], and short-term negative impacts that may
cause resistance to beneficial longer-term outcomes need
to be dealt with.
Conclusions Transformative actions in the food system to achieve
SDG 13 and UNFCCC agreements are crucial, but
actions need to be carefully considered given the possi-
bility of trade-offs between adaptation and mitigation,
and amongst other SDGs. SDG 12 is considered to be
central: responsible consumption and production [39].
Transformative actions will have many elements,
including:
(1) Expanded private sector activity and public–private
partnerships; (2) Credit and insurance; (3) Strong local
organisations and networking; (4) Climate-informed advi-
sories and early warning; (5) Digital agriculture; (6) Cli-
mate-resilient and low-emission practices and technolo-
gies; (7) Prioritisation and pathways of change; (8)
Capacity, and enabling policy and institutions.
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Acknowledgements This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements (https://ccafs.cgiar.org/donors). The views expressed in this document cannot be taken to reflect the official opinions of these organizations. Thanks to all the CCAFS’ colleagues for many comments and suggestions.
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www.sciencedirect.com
- Urgent action to combat climate change and its impacts (SDG 13): transforming agriculture and food systems
- Introduction
- SDG 13—strengths and limitations; and links to food systems
- Trade-offs among SDGs
- Case study: nitrogen fertiliser and the SDGs
- Too little N
- Too much N
- Optimal N
- Transforming food systems to tackle food security under climate change
- Element #1: expanded private sector activity and public–private partnerships (PPPs)
- Element #2: credit and insurance
- Element #3: strong local organisations and networking
- Element #4: climate-informed advisories and early warning
- Element #5: digital agriculture
- Element #6: climate-resilient and low-emission practices and technologies
- Element #7: prioritisation and pathways of change
- Element #8: capacity, and enabling policy and institutions
- Conclusions
- References and recommended reading
- Acknowledgements
attachment_4.pdf
Do Markets and Trade Help or Hurt the Global Food System Adapt to
Climate Change?
Food Policy - Viewpoint section
Authors:
Molly E. Brown, Department of Geographical Sciences, University of Maryland, mbrown52@umd.edu
Edward R. Carr, International Development, Community and Environment Department, Clark University,
edcarr@clarku.edu
Kathryn L. Grace, Department of Geography, Environment and Society, University of Minnesota Twin
Cities, klgrace@umn.edu
Keith Wiebe, International Food Policy Research Institute, k.wiebe@cgiar.org
Christopher C. Funk, Climate Hazards Group, University of California Santa Barbara,mailto:
cfunk@usgs.gov
Witsanu Attavanich, Department of Economics, Kasetsart University, Thailand, witsanu.a@ku.ac.th
Peter Backlund, School of Global Environmental Sustainability, Colorado State University,
peter.backlund@colostate.edu
Lawrence Buja, Climate Science and Applications Program, National Center for Atmospheric Research
(NCAR), southern@ucar.edu
Abstract
Rapidly expanding global trade in the past three decades has lifted millions out of people out of poverty.
Trade has also reduced manufacturing wages in high income countries and made entire industries
uncompetitive in some communities, giving rise to nationalist politics that seek to stop or reverse
further trade expansion in the United States and Europe. Given complex and uncertain political support
for trade, how might changes in trade policy affect the global food system’s ability to adapt to climate
change? Here we argue that we can best understand food security in a changing climate as a double
exposure: the exposure of people and processes to both economic and climate-related shocks and
stressors. Trade can help us adapt to climate change, or not. If trade restrictions proliferate, double
exposure to both a rapidly changing climate and volatile markets will likely jeopardize the food security
of millions. A changing climate will present both opportunities and challenges for the global food
system, and adapting to its many impacts will affect food availability, food access, food utilization and
food security stability for the poorest people across the world. Global trade can continue to play a
central role in assuring that global food system adapts to a changing climate. This potential will only be
realized, however, if trade is managed in ways that maximize the benefits of broadened access to new
markets while minimizing the risks of increased exposure to international competition and market
volatility. For regions like Africa, for example, enhanced transportation networks combined with greater
© 2017 published by Elsevier. This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/
Version of Record: https://www.sciencedirect.com/science/article/pii/S030691921630481X Manuscript_f6906d74d0fb6d75bb5c69b16d8c3ca1
national reserves of cash and enhanced social safety nets could reduce the impact of ‘double exposure’
on food security.
1
Introduction
Global trade has grown at twice the rate of the global economy since the 1990s, lifting
hundreds of millions of people out of poverty, enhancing competitiveness, expanding
economies and improving living standards (WTO, 2016). These benefits have not been felt
by everyone. Trade has also reduced manufacturing wages in high income countries and
made entire industries uncompetitive in some communities, giving rise to nationalist
politics that seek to stop or reverse further trade expansion (Karabarbounis and Neiman,
2014; Timmer et al., 2014).
Increasingly anti-trade rhetoric and protectionist agendas heard in Europe and the United
States are emerging as low income countries seek better integration into the global
economy (Henson and Loader, 2001; Murina and Nicita, 2015). This demand for
participation is particularly acute amongst those countries that suffer a lack of access to
sufficient food, since imports can help lower local commodity prices. Over the next decade
the food security of hundreds of millions of people will rely heavily on the evolution of
global trade.
In the contemporary trade context, a changing climate will present both opportunities and
challenges for the global food system. Climate change may affect people and processes in
ways that reduce food security by increasing vulnerable people’s ‘double exposure’. Double
exposure results when both economic and climate-related shocks and stressors act together
to increase overall vulnerability (O’Brien and Leichenko, 2000). Our perspective is that
trade openness can reduce both individual and institutional vulnerabilities by i) enhancing
future food security and ii) reducing the cost of response to climate change-induced food
availability shocks – if countries have the necessary physical and institutional infrastructure
in place (Brown et al., 2015).
Food Security and Global Food Systems
Food security is defined as a situation in which “all people at all times have physical, social,
and economic access to sufficient, safe, and nutritious food to meet their dietary needs and
food preferences for an active and healthy life” (FAO, 2012, 1996). Broadly speaking, food
security is comprised of three pillars: food availability, food access, food utilization, as well
as the overall stability of each pillar (Pinstrup-Andersen, 2009). Food availability is the
existence of food in a particular place at a particular time. Availability addresses the “supply
2
side” of food security, which is determined by food production, transportation, food stocks,
storage, and trade (Devereux, 1988).
Once food is present, then the question becomes whether or not a person or group has
access to it. Integral to this food security component are issues ranging from the
affordability of food to the social roles and responsibilities that govern the allocation of
available food within a society (across a range of scales, including intra-nation and intra-
household) (Higgins et al., 2015; Ploeg et al., 2012). Utilization, or the ability to use and
obtain nourishment from food, includes a food’s nutritional value and how the body
assimilates its nutrients, and touches on climate-sensitive variables such as food safety,
sanitation and health (Crimmins et al., 2016).
Finally, the stability of these pillars also shapes food security outcomes. When stable, food
availability, access, and utilization do not fluctuate to the point of adversely affecting food
security status, either on a seasonal or annual basis or as a result of unpredictable events
(FAO, 2012). For example, in 2012, almost the entire United States experienced severe
drought, yet food prices exhibited very little fluctuation. Extreme weather, political unrest,
or a change in economic circumstances may affect food security by introducing instabilities
in one or more components (Sen, 1990).
Access, availability, utilization, and the stability of these three pillars take shape in the
context of a global food system (Vermeulen et al., 2012). This system connects producers
and consumers through markets that operate at different scales. On one hand, these
interconnections can facilitate increased production by providing the income and capital
needed to spur new investments in agricultural production or transportation infrastructure
that increase the movement of food from producers to consumers.
These investments can lower the cost of such production and transportation, reducing the
price of food and facilitating greater access and choice to most people within this system
(WTO, 2015). On the other hand, for some populations there are situations in which the
global food system can produce challenges. For example, the increased interconnectedness
of food producers and consumers globally can result in the transmission of price shocks
produced by distant production crises to people who previously were insulated from such
events, such as seen in the food price spikes of 2008 and 2011 (Anderson et al., 2014;
Baltzer, 2013).
3
Climate and the Food System
Climate change, identified by changes over an extended period in the average and/or
variability of properties such as temperature and precipitation, is already affecting major
agricultural regions in the world (Walthall et al., 2012). The Intergovernmental Panel on
Climate Change (IPCC) finds that human activities have resulted in large changes in Earth’s
climate over the last few centuries, and much larger changes are projected in the coming
decades due to increases in greenhouse gas (GHG) emissions (Crimmins et al., 2016; O’Neill
et al., 2014; Rosenzweig et al., 2014; Teixeira et al., 2013; Stocker et al. 2013).
These changes have multiple implications for the global food system. The effect of global
climate change on food production (and therefore availability) is well-documented, but is
also highly specific to both place and the crop or animal commodity in question (Challinor
et al., 2007; Rosenzweig et al., 2014; Sivakumar, 2006; Wang et al., 2009). The effects of
changes in climate on crops tend to be gradual until a threshold is reached (IPCC 2013). As
the planet warms, more regions may experience temperature-related yield stagnation and
even declines, affecting overall food production. Climate change risks can extend beyond
agricultural production to other elements of food systems (Vermeulen et al., 2012).
Processing, packaging, and storage are very likely to be affected by temperature increases
that could increase costs and spoilage. An example is the cooling of fruits and vegetables
following harvest to extend shelf life (Kurlansky, 2013), which entails higher energy costs
(Moretti et al., 2010).
Packaging and logistics companies in some countries now collaborate with farmers and
organizations that seek to reduce food waste to develop packaging that provides ventilation
and temperature control (Verghese et al., 2013). Climate change could also make utilization
more difficult by increasing food safety risks throughout various stages of the food supply
chain (Jacxsens et al., 2010; Tirado et al., 2010). For example, increased temperatures are
known to cause an increase in diarrheal diseases (which can lead to malnutrition); bacterial
foodborne diseases grow and reproduce faster at elevated temperatures (Bandyopadhyay
et al., 2012; Tirado et al., 2010).
The impacts of climate change on access are less well understood. Much of the information
we have on availability is tied to prices. While the price of food is also an important factor in
shaping food access, it is hardly the only factor, and in many cases, may not be the most
4
important factor. Instead, the roles and responsibilities that dictate who has access to food
and why can produce food insecurity in places where prices are low, or result in
distributions of food that offset the worst food security outcomes in situations where food
prices spike (Bellemare, 2015).
Further, even from a market-centric perspective, ports, riverine barge systems, and roads in
regions experiencing sea-level rise and changing frequency of climate extremes such as heat
waves and drought due to climate change may impede the movement of food from places
with surpluses to places with deficits (Attavanich et al., 2013). Such impacts can shape
availability and utilization of food in particular places, and also have an impact on access
when this infrastructure results in local shortages.
Coupled climate, crop, and economic models
Framed as a product of double exposure, it is critical to evaluate food security outcomes as
the product of linked economic and environmental changes now and into the future if we
are to build relevant, productive policies that address future food security (O’Brien and
Leichenko, 2000). To this end, coupled climate, crop, and economic models have been used
in recent analyses that use scenarios of both high and low GHG emissions to better
understand the likely impact of economic and environmental changes on food security
(Antle and others, 2015).
Although changes in climate due to anthropogenic factors have been analyzed by models for
several decades, when considering food security as the outcome of double exposure, it is not
enough to focus only on climatic variables such as rainfall and temperature. Instead, we
need scenarios that frame what society (and its attendant economy) may look like by 2050
or 2100, for example, and then use these to estimate the probable impacts of the future
climate on that future society. Some future societies and economies may be more vulnerable
to climate change than others. In the coming decades, some societal changes are likely to be
more important for food security outcomes than climate. Factors such as population
growth, changes in income, and the affordability of food, will all strongly affect how much
food each person can afford to consume.
Technological change in agricultural production and in food processing are particularly
difficult to predict and can have a profound effect on food availability and access (Meléndez
5
and Uribe, 2012). Linking models that project agriculturally relevant parameters from
climate models to crop models and then to economic models that can project the likely price
of food in the future (among other indicators) help us understand the relative importance of
these changes to food security in coming decades (Antle and others, 2015; Rosenzweig et
al., 2014).
For example, climate models show that over a wide range of scenarios, global temperatures
are expected to increase throughout this century. These are likely to be accompanied by
longer, more frequent, and more intense temperature extremes and heat waves and
increases in regional extreme precipitation events (Stocker et al., 2013). Coupled climate,
crop and economic models show that these changes will have consequences for the average
and variance of global crop yields, crop production patterns, food prices and effects on food
processing, storage, transportation, and retailing (Attavanich and McCarl, 2014; Attavanich
et al., 2013; Teixeira et al., 2013; Wiebe et al., 2015). Despite the significant challenges
posed by climate change, coupled climate, crop, and economic models show that
technological and socioeconomic changes could compensate for changes in climate,
resulting in food security outcomes similar to those we experience today (IAC, 2004).
Advantages of Trade for Food Security in a Changing Climate
Trade is a key way that sufficient calories and nutritious food can be made more available
and accessible to those experiencing the greatest climate change impacts. Advances in
technology and management practices and the globalization of the food system, including
international trade and market connectivity, have enabled widespread diffusion of new
technologies and regional agricultural specialization and intensification, resulting in the
production of sufficient calories for everyone on the planet (Flynn et al., 2009; Garnett et al.,
2013; MacDonald et al., 2015). Today, and probably in the foreseeable future, the problem
of food security is principally one of distribution of food among nations, regions and
households (Figure 1) rather than insufficient overall production.
Moving food to where it is needed involves the means to physically transport foodstuffs, the
absence of trade barriers, and the financial wherewithal to purchase adequate nutrition.
Trade, as a major driver of economic growth, employment and poverty reduction, often
enhances food availability and its stability with differing effects under differing
socioeconomic trajectories, such as those described by the shared socioeconomic pathways
6
(SSPs) produced by climate impacts and vulnerability researchers, which describe
alternative ways in which global socioeconomic conditions could change over the next
century (O’Neill et al., 2014). Under SSP1 and SSP5, world markets would be highly
connected and trade would flow easily between countries and regions (Table 1). Under
these scenarios, markets are likely to be able to facilitate the movement of food from areas
of surplus to areas of deficit. This is likely to reduce food availability challenges created by
changes in climate.
Table 1. Shared socioeconomic pathways, trade, market and food security
Shared
Socio-
Economic
Pathways Assumptionsa
Global
Population
by 2100b
Trade and Market
Connectivityc
Food Security
Outcomesd
SSP1 Low challenges to both
mitigation and adaptation 6.9 billion Moderate
international trade
with connected
markets
Relatively food secure,
stresses and shocks in
availability are
compensated with trade
SSP2 Medium challenges to both
mitigation and adaptation 9.0 billion Moderate
international trade
with semi-open
globalized economy
Relatively food secure,
stresses and shocks in
availability are anticipated
SSP3 High challenges to both
mitigation and adaptation 12.6 billion Strongly constrained
international trade
with de-globalizing,
regional security
Low food security for all,
the poor worse off, better
food security for those
with higher incomes
SSP4 Adaptation challenges dominate
mitigation challenges 9.3 billion Moderate
international trade
with globally
connected elites
Low food security,
portions of the population
worse off due to within-
and between-country
inequality
SSP5 Mitigation challenges dominate
adaptation challenges 7.4 billion High international
trade and strong
globalization with
connected market
Relatively food secure,
increasing number of
shocks, problems with
availability are
compensated with trade
Source: aO’Neill et al., (2014), bSamir and Lutz, (2014), cO’Neill et al., (2015), and dBrown et al. (2015)
On the other hand, SSPs 2, 3, and 4 all present different futures under somewhat
constrained market connectivity. Under SSP2, stresses and shocks in availability are
anticipated, and the semi-open globalized economy may not be open enough to facilitate the
robust trade links needed for markets to effectively respond to these shocks. Under SSPs 3
and 4 this pattern is accentuated. These SSPs present a world where the wealthy enjoy
strong trade connections through which they can access goods and resources, but the global
7
poor have few connections to markets and between one another. As a result, markets would
be unable to respond fully to shocks and stresses on availability such that food can
effectively move into deficit areas to address shortages.
Under SSP3, poor market connectivity also exists among the wealthy of the world, though
effects on food availability would almost certainly be less severe than among the poor
because greater incomes allow for greater food access. Under SSP4, high within-country
inequality could create market-based challenges that diminish food availability for
segments of the population within a country. For example, the consumption of meat and
other resource-intensive foods under this scenario would divert food away from poorer
populations, and low-functioning markets would inhibit trade to areas of deficit created by
this pattern of consumption.
Trade also improves household food access by moderating price increases under climate
change (Brown and Kshirsagar, 2015; Wiebe et al. 2015; Lybbert and Sumner, 2012). For
example, extending earlier AgMIP research with five global economic models to observe the
effects of alternative socioeconomic and climate scenarios, Wiebe et al. (2015) found that
relative to a world where the climate remains fixed under current conditions, low-
emissions/high-international-cooperation scenarios with moderate-to-high levels of global
trade exhibit smaller price increases compared with high-emissions/low-international-
cooperation scenarios with restricted levels of global trade . Less trade generally means
higher prices, which lead to more food insecure people.
Disadvantages of Trade for Food Security in a Changing Climate
There are also a number of disadvantages of international trade for poor and remote
households that are felt today, and a few which may become more important as the climate
continues to change. These include vulnerability to international price shocks that affect
local food affordability, and lack of competitiveness in the global marketplace that leads to
the inability of local governments to import sufficient food from the international market,
and isolation due to poor infrastructure.
Most arguments suggesting that access to international markets has been beneficial to low
income countries and agricultural exporters are based on data aggregated at the national or
regional level. When we shift analysis to lower levels of spatial scale, we can see that while
8
access to markets provides opportunities, it also introduces new sources of volatility into
places that would not otherwise feel the effects of a distant market or climate stress. For
example, when global food prices were high in 2008, food costs in Burkina Faso increased
sharply, despite above-average domestic agricultural production that year (FAO, 2016).
How donors and states address this downside of trade is critical to the long-term viability of
the national and global food system.
International trade helps countries attain access to food in an aggregate sense, though it will
not by itself increase the within-country availability for isolated people, ensure food access
for the poor and socially marginalized, or deal with the health impacts of poor food
utilization (Handa and Mlay, 2006). Lack of infrastructure in many food insecure nations in
Africa means that there is virtually no formal trade between land-locked countries in north-
central Africa and those in more-developed eastern and southern Africa. High transport
costs sustains elevated local producer prices by restricting imports and reducing
competition from less expensive alternatives, but this also reduces access to food for the
poorest households (Lee et al., 2012).
Increased internal trade within the African continent would promote broader economic and
political integration, resulting in lower food costs and higher producer prices through the
reduction in necessary transportation and storage costs (Buys et al., 2006). Since many
parts of eastern and southern Africa experience inverse relationships to El Niños and La
Niñas, increased trade would help mitigate increases in climate variability (Ubilava, 2016).
Although poor roads may cause the isolation of rural communities, poor infrastructure may
also be due to the lack of goods to trade. Poor institutions and limited endowments of
productive assets, resulting in little agricultural surplus and few raw materials, result in few
transportation links. Built infrastructure has long been recognized as an important element
to development and strengthening of local markets to provide affordable food as well as
income (Briceño-Garmendia et al., 2004).
International trade helps improve the aggregate welfare of society at the global scale by
connecting areas of resource surplus and deficit and lowers demand for land resources on a
global level by maximizing production in regions most suited to a crop (Fader et al., 2013).
But local and regional markets cannot always ensure food availability within a nation or
access to food when incomes are low.
9
Although there may be enough food for everyone worldwide in an aggregate sense, a
particular country may not have sufficient foreign exchange reserves to afford food imports
(FAO, 2003). If a region is unable to compete and fails to invest in local infrastructure, a
drought or other extreme event that affects local production may result in severe food
deficits in a country, as was seen in Zimbabwe in 2008 (Funk and Budde, 2009).
Food affordability depends on the amount of disposable income an individual or family has
relative to food prices. For many of the rural poor in developing countries, income depends
at least in part on agriculture, which is itself vulnerable to climate variability and change.
Low-income households, whose food budgets represent a large portion of their incomes, are
more vulnerable to rapid changes in prices than middle- and high-income households
because they do not have the economic reserves to increase their food budget (Grosh et al.,
2008). Therefore, while smoothing out local production deficits with trade is an important
tool to safeguard consumption by the poor, governments and development organizations
must also prepare to respond to occasions when markets translate distant shocks into local
food insecurity. Increasing local grain reserves might be one way to help insulate national
markets.
Care is needed in responding to these concerns, however. During the 2008 food price crisis,
export restrictions imposed by major rice-exporting countries were largely responsible for
world rice price tripling in four months during a time of record production (Anderson et al.,
2014; Bellemare, 2014). Restricting trade (on either imports or exports) may protect the
domestic population from the impacts of regional and global economic shocks in the short-
term (Carr, 2011; Do et al., 2013), but over the long term, when trade is restricted,
producers cannot properly respond with production changes, prices are higher, technology
uptake is lower, adaptation is more difficult, and climate effects on food security are worse
(Brown et al., 2015).
Conclusions
In the past few years, fragile food economies in countries that experience El Niño-related
droughts face substantial risks (Funk et al., 2016); risks that could be moderated by better
functioning markets, international trade, and economic growth that raises incomes in these
vulnerable communities (OCHA, 2016). Efforts to evaluate and model these outcomes
10
through the lens of ‘double exposure’ suggests that trade could play a critical role in
ensuring future food security for the widest number of people on the planet.
Trade can benefit agricultural producers and long-term food security in low income
countries by supporting producer income through sales of surplus production, and by
improving productivity by providing lower-priced or more varied production inputs, such
as seed, fertilizer, pesticides, and machinery (Peterman et al., 2014). Low income countries
often lack the physical, financial, and government infrastructure necessary for their farmers
to compete with other producers around the world who have better infrastructure and
superior access to markets (Nkendah, 2010). Fostering efficient and open markets and
linking producers to them is important for long-term food security, but also requires
effective government policies and support for small producers from actors along the food
chain (Reardon et al., 2003).
The rise of anti-establishment, populist parties in the United States, the United Kingdom,
and Europe who advocate anti-trade policies that will restrict movement of labor, goods, or
services could threaten this progress (Burgoon, 2013). Protectionist policies are attractive
in an era of slowing growth since the benefits of trade are diffuse while the costs are
concentrated. The impact of protectionist policies on the functioning of the global food
system, in the name of national security, political advantage or reducing imperfect
competition, should be considered carefully. The burden of information necessary to
evaluate the impact of trade interventions increases substantially when considering the
added complexity of a changing climate in the agriculture sector (Krugman, 1987).
As the world seeks to maintain its food system in a changing climate while feeding more
people who demand better, more nutritious food, trade policies should be pursued that
reduce the impact of double exposure on the global food insecure. The basic arguments
supporting trade enhancement and climate mitigation are similar – but sometimes
politically difficult – large diffuse benefits are obtained at the expense of concrete
concentrated costs. Since the 1950s, continuous market integration and economic growth
has helped dramatically increase global wealth and reduce food insecurity. Our analysis of
coupled models suggests that deviating from that trajectory will magnify the impact of
climate change on food security.
11
Acknowledgments
The US Department of Agriculture’s Office of the Chief Economist and the CGIAR Research
Program on Policies, Institutions, and Markets (PIM) supported this work.
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Climate Change Impacts on Global Agricultural Trade Patterns: Evidence from the Past 50 Years
Suborna Barua and Ernesto Valenzuela
Federation Business School, Federation University Australia
Contact author: Suborna Barua Federation Business School, Federation University Australia, 1 Northways Road, Churchill, VIC 3842, Australia Email: s.barua@federation.edu.au, Phone: +61468571709
Suborna Barua is a PhD candidate, and Ernesto Valenzuela is a Senior Lecturer at the Federation Business School, Federation University Australia,
Presented at the Sixth International Conference on Sustainable Development 2018 26-28 September 2018, Columbia University, New York, USA.
Published in the conference proceedings on 01 November 2018
Climate Change Impacts on Global Agricultural Trade Patterns: Evidence from the Past 50 years
Abstract
Climate variability affects the specialization and portfolio of production and trade in agricultural markets. Previous studies suggest that climate change has a negative impact on economic growth and production patterns, and in particular it significantly alters yields and commodity prices. This paper investigates the impacts of climate change on agricultural trade using detailed estimates of temperature and precipitation for 67 countries from 1962 to 2014. This study controls for national income, comparative advantage in land, technology and productivity, climatic zone differences, estimates of agricultural nominal rates of assistance, and trade membership. Utilizing Prais-Winsten Panel Corrected regressions, the study produces aggregate and sectoral estimates at the global, regional, and income level. Findings suggest that climate change, over the period considered, has a significant impact on agricultural exports at all levels. Rising temperature significantly reduces agricultural exports from Asia and Africa, while it benefits Australia-New Zealand. Exports of grains, oil seeds, livestock, and dairy and eggs, are found significantly vulnerable to temperature changes. Developing economies show a larger reduction in agricultural exports due to increases in temperature. The findings provide a detailed evidence of how agricultural export patterns are vulnerable to variations in climatic conditions, and they could be used in further projections considering climate change as a determinant of agricultural production and trade. Keywords: Climate change, Temperature, Precipitation, Agricultural trade, Regional trade, Developing countries JEL Codes: F14, F18, Q17, Q18, Q54
1. Introduction
The scientific evidence on climate change shows a significant increase in the historical levels of temperature and precipitation, and puts forward the likelihood of future increases accompanied by extreme events like floods and droughts (IPCC 2007, 235-432; 2014, 39-73; Hansen et al. 2010, 1-29; Trenberth 2011, 123–138). The impacts of climate change span across all ecosystems and human activities, and the link between climate change and economic indicators has received consideration in function of eventual resulting costs or benefits (Pearce et al. 1996, 179-224; Watson et al. 1996, 95-586; McCarthy et al. 2001, 75-486; Dell et al. 2014, 740-791; Acevedo et al. 2017, 117-177). Some recent studies suggest that climate change distorts overall international trade patterns (Jones and Olken 2010, 455-458; Li et al. 2015, 55-57). However, considering the greater sensitivity of agricultural productive activities to climate change, it is likely that agricultural trade will be affected the most. The relationship between climate change and agricultural trade is not trivial. Given the evidence on the vulnerability of agricultural yield and prices to climate change, adverse changes in agriculture-based economies could not only threaten domestic food-security but also their market share and competitiveness; which in turn could demean their economic development significantly. Against this backdrop, understanding the climate change impacts on agricultural trade is imperative; albeit empirical studies on such impacts remain limited. There are several considerations in addressing this link. Firstly, agriculture includes a range of sub-sectors and climate change could affect them differently. Second, there could be a heterogeneous geographical sensitivity in production. Moreover, impacts could be differential across developed and developing countries (i.e., agricultural trade impacts might be higher for agriculture-based economies than diversified economies).
This study aims to comprehensively explore the heterogeneous impact of climate change on agricultural trade. In particular, this paper assesses temperature and precipitation variations on total and sectoral agricultural exports at a global, regional, and economic level. This study controls for national income, comparative advantage in land, technology and productivity, climatic zone differences, estimates of agricultural nominal rates of assistance, and trade membership.
The following section discusses the literature on climate change and economics. This is followed by a description of the methodology, methods and databases used. The results are then discussed distinguishing between aggregate and sectoral levels, global and regional levels, and income classification. Lastly, concluding remarks are presented. 2. Climate and Economics
The economic impacts of climate change could be direct (i.e., on growth, income, and productivity), and indirectly channelled through ecosystems, health, and wellbeing. Lee (1957, 5-157) discussed the wider effects of climate on crop production, animal production, human health and efficiency, and industry. Investigations of the relationship between climate change and economics have gained momentum as a consequence of potential effects exposed by advanced geo-physical modelling. A recent study by the OECD indicates that the cost of climate change could range from 0.6% to 4.4% of GDP by 2060, with developing countries absorbing the blunt of these costs (OECD 2015, 17-126). Hsiang (2010, 15367- 1572) suggested similar findings for Caribbean and Central American countries. Similar negative effects on income have been found by Dell et al. (2009, 1999-203) at both country and city level. The negative economic impacts of temperature variability on growth have been shown by Dell (2012, 74-90) using a large global level data. A recent study by Heal and Park (2014, 18-23) suggests that warmer-than-average years strongly relate with lower output per capita for hot-countries, and higher output per capita for cold-countries.
2
Cross-border activities are directly affected by climate-induced variability impacting economic growth and productivity. International trade of goods and services could be affected through the following channels: (i) changes in total and sectoral comparative advantage, (ii) increased transportation costs due to vulnerabilities of supply and distribution channels, and damage to infrastructure, and (iii) implementation of climate-related policies (WTO-UNEP, 2009; World Bank, 2010). Projection estimates of aggregate trade impacts of climate change are proposed by Dellink, et al. (2017, 18-50). They conclude that trade alone will face limitations as an adjustment mechanism to likely future changes in climate, with economies relying primarily on climate-specific or geophysical sources (i.e., developing countries) experiencing greater effects.
While projection-based estimates are numerous, empirical evidence of the past climate change impacts remains limited. Jones and Olken (2010, 455-458) found that temperature increases have a large negative impact on exports from poor countries but no impact on export from rich countries, while precipitation change has no impact on US imports but it affects its exports positively. They also estimated SITC product-wise impacts and found that temperature increases have a significant negative impact on as many as 20 different export products. Li et al. (2015, 55-57) using product-city level data suggest that temperature has a significant negative impact on both exports and imports, while precipitation has a moderate positive impacts on exports.
Given the weather dependence of agricultural production systems, agriculture has been singled out as the most sensitive production activity to climate change. A vast number of studies have examined the impacts of climate change on agriculture, looking at yield effects and price forecasting. Different simulation-based studies forecast that global warming would significantly reduce production, yields, and economic surpluses in the US agricultural sector across crops, regions and farms (Adams, 1989, 1272-1279), Adams et al. (1995, 147- 167) (Kaiser et al. 1993, 387-398). Similar modelling used by Mendelsohn et al. (1994, 753– 771) also suggests a substantial loss in the values of US agricultural land and farms due to warming. In a more detailed approach, Schlenker and Roberts (2009, 15594–15598) predict the nonlinear impacts of temperature increases on the US Corn and Soybean yields and find that yields increase with temperature increases up to a threshold, however, yields experience a much steeper fall when they rise over that threshold. The vulnerability of US agricultural crop yields is also discussed by Feng et al. (2010, 14257–14262). Antle (1995, 741-746) suggests that most of the warming impacts are felt by tropical and sub-tropical countries, mainly the developing ones. Mendelsohn and Dinar (1999, 277–293) provide evidence that developing countries, like India and Brazil, are likely to experience significant crop yield shocks from warming. Schlenker and Lobell (2010, 1-80) also suggests large shocks in several crop yields in developing countries. Using a unique farmer-managed field level data, Welch et al. (2010, 14562–14567) finds that rice yields in six major tropical and subtropical Asian countries show large but contrasting responsiveness to minimum and maximum temperature levels. Using an extensive FAO panel data of all countries in the world, Lobell et al. (2011, 616-620) shows that yields of major agricultural crops like maize, rice, wheat, and soybean are significantly reduced by changes in temperature and precipitation both at the global and regional levels. Research on agricultural sensitivity has been complemented by studies looking at how climate change induces reduction in non- agricultural productivity, industrial output, industrial value added, and labour supply., Hsiang 2010, 15367-1572; Dell et al. 2012, 74-90; Lee et al. 2014, 504-513).
Climate variability may significantly change countries’ agricultural trade competitiveness. Reilly et al. (1994, 24-36) advocate that a country’s net economic effect of climate change will depend as much on its role in agricultural trade as on the impacts of the changed climate on crop yields. Juliá and Duchin (2007, 393–409) find that, using projected estimates of agricultural trade impacts, adaptation is critical to satisfy future demand, access to food may decrease in some regions of the world, and that relying solely on trade as a mechanism for the adjustment of agriculture to likely future changes in climate is concerning.
3
Stevanović et al. (2016, 1-9) forecast that by 2100, climate change impacts on global agricultural may reach an annual loss of 0.3% of global GDP, with a magnification of these effects in the presence of trade-restrictive policies. Jones and Olken (2010, 457-458) assess the impact of climate shocks on exports by using a simplified formulation of the change in exports dependent on changes in temperature and precipitation. Looking at US imports for four agricultural SITC 2-digit sectors, and 18 manufacturing products, they find that temperature increases negatively affect the exports of cereals and preparations, dairy and eggs, although positively affect exports of dyes and hides. This limited provision of agricultural sub-sectoral trade impacts of climate change is prevalent in the literature, with a vacuity of studies addressing the link between climate change and agricultural trade in a comprehensive manner.
To fil this void, we evaluate temperature and precipitation variations on total and sectoral agricultural exports at a global, regional and economic level; while controlling for income, comparative advantage in land, technology and productivity, climatic zone differences, estimates of agricultural nominal rates of assistance, and trade membership.
3. Methodology Using the 2-digit SITC category, we extract world exports data for six agricultural
sub-sectors for 102 countries from the United Nations Comtrade database for the period from 1962 to 2014. The agricultural product categories are: (i) grains, (ii) oil-seeds-nuts- kernels (iii) fruits and vegetables, (iv) tropical crops, (v) livestock, and (vi) dairy products and eggs. Detail on the sub-sectoral definitions is provided in appendix table A1. We generate world’s ‘total agricultural exports’ (agriexp) by summing up these selected sub-sectoral agricultural exports for each country. The exports data is deflated to remove inflationary effects. We use historical temperature and precipitation data, with yearly averages of monthly near surface temperature and precipitation constructed from the National Center for Environmental Prediction (NCEP), NOAA/National Center for Atmospheric Research, USA real analysis1. The NCEP Real analysis is a spatially resolved dataset compiled from ground stations, satellites, aircraft, and marine observational systems. Country averages were constructed by identifying 2.5 x 2.5 degree grid cells within each country, and then averaging over all grid cells. To control for economy size, domestic demand and national income, we use GDP per capita (GDPpc) while for resource and factor endowment we incorporate arable land per capita (aralandpc) from the World Bank’s World Development Indicators (WDI) database. To capture technological progress, innovation and improvement in productivity of factors of production, we use data on agricultural Total Factor Productivity (TFP) growth (tfpgr_ag) published by the US Department of Agriculture. To capture farm- level policies, we use nominal rates of assistance to total agriculture (nra_ag) collected from the World Bank’s estimates of distortions to agricultural incentives (Anderson and Valenzuela 2008; Anderson and Nelgen 2013). In consideration to trade policies, we construct a dummy variable with respect to GATT-WTO membership accession (1 from the year of accession and 0 before that) for each country. Because the formation of GATT-WTO and its accession by the countries is considered as an event that largely changed the course of international trade, the accession dummy inclusion also captures this structural break in trade history. Detail on the variables and the data summary are provided in appendix table A5 and A6. In addition to the control variables, we further control for ‘time’ fixed effects (years) and ‘climate-zone’ fixed effects in our analysis. Dell et al.’s (2014, Appendix Table 1) review of related empirical literature suggests that many studies have not controlled for fixed-effects, while others have done it using different cross-section unit levels (e.g., city, country, region). Controlling for fixed-effects across climate zones enables us to produce
1 We acknowledge Dr. David Newth from Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) for his generous temperature and precipitation data support contribution.
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consistent estimates by accounting for fixed-heterogeneity biases due to the different nature of climate faced by different countries and regions. Following Belda et al. (2014, 3-4), we classify countries into four major climate zones (CZ): (i) Tropical (torp), (ii) Sub-tropical (subtorp), (iii) Temperate (temp), and (iv) Polar and Subpolar (pol_subpol).
Using the variables identified above, we formulate the following basic relationship: 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐴𝐴𝐴𝐴𝐸𝐸 = 𝑓𝑓(𝐺𝐺𝐺𝐺𝐺𝐺𝐸𝐸𝐴𝐴, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝑎𝑎𝐸𝐸𝐴𝐴, 𝐴𝐴𝑓𝑓𝐸𝐸𝐴𝐴𝐴𝐴_𝐴𝐴𝐴𝐴, 𝑎𝑎𝐴𝐴𝐴𝐴_𝐴𝐴𝐴𝐴, 𝑊𝑊𝑊𝑊𝑊𝑊_𝑚𝑚𝑚𝑚𝑚𝑚, 𝐴𝐴𝑚𝑚𝑚𝑚𝐸𝐸, 𝐸𝐸𝐴𝐴𝑚𝑚𝐴𝐴, 𝐴𝐴𝐴𝐴𝐴𝐴𝑚𝑚𝑚𝑚𝑐𝑐𝐸𝐸𝑎𝑎𝑚𝑚)
Subsequently, we propose the following estimation structure:
lnagriexpit = αi + β1lnGDPpc + β2aralandpcit + β3tfpgr_agit + β3nra_agit + β4WTO_memit + β6tempit + β7precit + β8tropdumit + β9subtropdumit + β10pol_subpoldumit + ∑ 𝑌𝑌𝑖𝑖
𝑛𝑛 𝑖𝑖=1 + µit ------- (Eq.1)
lnagriexpits = αi + β1lnGDPpc + β2aralandpcit + β3tfpgragit + β3nraagit + β4WTOmemit + β6tempit + β7precit + β8tropdumit + β9subtropdumit + β10pol_subpoldumit + ∑ 𝑌𝑌𝑖𝑖
𝑛𝑛 𝑖𝑖=1 + µit ------- (Eq. 2)
Where, lnagriexp is natural log converted agricultural exports, i denotes country, t
denotes year, s for sub-sector, 𝑌𝑌𝑖𝑖 denotes year fixed-effects, 𝛼𝛼 is constant, and 𝜇𝜇 captures residuals.
We estimate equation (1) for the World and equation (2) for the six agricultural sectors separately. We further estimate equation (1) individually for eight geographical regions: (i) USA and Canada, (ii) Central America, (iii) Latin America (iv) Western Europe/EU-15, (v) ETE-Central Asia, (vi) Africa, (vii) Asia and (viii) Australia and New Zealand. Finally, we produce estimates for High-income countries (HICs) and Developing countries (DVCs) (upper middle-income, lower middle-income, and lower income) separately. A list of countries by region and income groups (i.e. economic status following the 2014 definition of the World Bank lending groups) is shown in appendices table A2 and A3.
Given the unavailability of a full set of data for all variables across all regions and years, we check for model correctness and appropriate estimation method selection for each sample group (method selection results are given in appendix table A7). Based on these tests, we elect to carry out our estimations using Prais-Winsten Panel Corrected standard errors regressions. We produce estimates for 67 countries.
4. Results and discussion
This section firstly discusses world aggregate results, followed by sub-sectoral results. Results by region and economic level are discussed afterwards. 4.1 World aggregate level
When considering global exports of total agriculture, we find that GDP per capita is significant and positive, consistent with the development-led export hypothesis of trade. Arable land per capita is also significant and positive, consistent with the factor-endowment postulate of trade. We find that agricultural policy interventions, as measured by nominal rates of assistance, has significant and negative effects on agricultural exports, which provides empirical support of the general distortion and anti-trade properties of farm-policy interventions, discussed by Anderson (2009, 10). WTO accession and total factor productivity growth appear to have no significant effect. Climate zone fixed-effects show
5
significant and positive signs as expected. Having controlled for all these factors, we find that increases in temperature have a significant negative effect on agricultural exports. A one degree Celsius increase in temperature reduces agriculture exports by about 1.6%. However, the effect of precipitation increases appears insignificant.
In light of these results, it is plausible that the negative effects of climate change, i.e. increases in temperature, over the last five decades, may have offset the positive effects of trade liberalization through WTO agreements and the growth in total factor productivity through technological developments and improvements in the quality of resources (e.g., agricultural inputs).
4.2 Sub-sectoral estimations
When considering sub-sectoral estimations, GDP per capita is consistently significant and positive for all six agricultural sub-sectors. Arable land per capita also shows a similar sensitivity, except for fruits and vegetables and topical crops. TFP shows significant and positive effects for livestock and oil-seeds-nuts-kernels. Otherwise, the effects of TFP growth is nullified, which supports the result on aggregate agricultural exports. WTO accession shows no significant effect on any of the agricultural sub-sectors, consistent with the results on aggregate total exports. Conforming to the aggregate exports effects, agricultural NRA appears to have significant distortion effects in all sub-sectoral agricultural exports except for dairy and eggs.
[Table - 1: Sub-sectoral estimation result on world agricultural exports]
With respect to the climate variables, we find significant and negative effects of
increased temperature on the exports of four major agricultural sub-sectors: grains, oil- seeds-nuts-kernels, livestock, and dairy and eggs. It is relevant to consider that these four sub-sectors are the major sources of global food supply. Therefore, our findings indicate that increased temperature has large negative effects on the exports of products which are main sources of global food-supply, alongside the confirmation of no effects on tropical crops and fruits and vegetables. Results suggest that a one percentage-point increase in temperature reduces exports of grain by 6.7%, oil-seeds-nuts-kernels by 7.6%, livestock by 7.0%, and dairy and eggs by 5.7%. The magnitude of these effects appears large and roughly of similar size. On the other hand, precipitation shows significant and negative effects only for the exports of oil seeds, nuts and kernels. The insignificance of the other five sub-sectors is in line with the lack of sensitivity of total agricultural exports to precipitation changes. Overall, we find that oil-seeds-nuts-kernels are susceptible to both temperature and precipitation changes. Our results on cereals and dairy and eggs are consistent with earlier findings of Jones and Olken (2010, 455-458).
4.3 Regional estimations
The regional estimation results are shown in Table 2. GDP per capita appears significant and positive for four regions: Central America, Latin America, EU-15, and Africa. However, it appears negative for US-Canada and Australia-New Zealand. This might be due to the fact that the world has seen accelerated economic growth driven by non-agricultural sectors, i.e. manufacturing and industrial sectors; thereby, diverting resources from agricultural to non-agricultural sectors leading to reduction in relative agricultural share in production and exports. The argument can further be validated by looking at the results of arable land per capita. While arable land per capita appears significant and positive for two regions, consistent to the previous results; it is negative for four regions - Latin America,
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Central America, EU-15, and Africa. Given the natural progression of the world economy, this exhibit negative sensitivity appears meaningful. Our data shows that arable land per capita has been decreasing while at the same time, due to technological improvements and innovations in crop varieties have led to increase in total agricultural production and trade (See figure A8 and A9 in appendix). From 1961 to 1999, about 71% of growth in agricultural crops production has been due to yield growth while the other 29% comes from the harvestable land expansion (Bruinsma (ed.) 2003, 124-127). These facts essentially indicate a practical negative relationship between agricultural exports and arable land per capita. Note that in our estimations we explore the effective and robust relationship between arable land per capita and agricultural exports by separating out technology and innovation effects through TFP growth. The overall explanation also supplements the earlier discussion on the competitive use of resources for non-agricultural economic activities. We find a very limited evidence on the significance of TFP growth; significant and positive only for the EU-15 region. Regional effects of WTO accession appear mixed as its effects are negative in Asia and Latin America while positive for Central America and Africa, consistent with the literature. Agricultural farm-policies reflected through NRA shows evidence of significant distortion of agricultural exports for Asia and Latin America - the two major agricultural suppliers in the global market. [Table - 2: Regional estimation result on world agricultural exports]
With respect to the climate variables, we find that the two major agricultural
producing regions, namely, Asia and Africa, experience significant large negative effects on trade with increases in temperature. While a one degree-Celsius increases in temperature reduces agricultural exports by 4.8% for Africa, the effect is more than three-times larger at 14.4% for Asia. This puts into evidence the vulnerability of agricultural exports from these two regions to increases in temperature. While most of the economies in these regions are largely dependent on agriculture, most of the world’s developing and least developed countries also belong to these two regions. There is an abundant body of literature identifying that people’s lives, resources, and possessions in these two regions are most vulnerable to rapidly increasing temperature, and extreme weather, causing agricultural lands to become barren, or permanently lost with rising sea-levels, altering agricultural production and trade patterns (IPCC 2007, 235-432:2014, 39-73). In contrast, while rising temperature distorts agriculture in those regions, it can benefit temperate countries. We find that a degree-Celsius increase in temperature increases agricultural exports for Australia- New Zealand by as much as 8.3%.
We also find significant and negative effects of precipitation for Asia and Latin America, while positive in ETE-Central Asia. We find that a 1 mm increase in precipitation decreases agricultural exports by 0.1% in both Asia and Latin America. High levels of precipitation may cause significant losses in production and yield through untimely and heavy rainfall of long duration, increased frequency and duration of flooding, and widespread diseases of agricultural produce. We find that Asia is susceptible to increases in both temperature and precipitation levels. 4.4 Estimations by economic levels Estimations of grouped countries according to economic levels are presented in Table 3. GDP per capita and arable land per capita are more or less consistent to the arguments provided earlier. TFP growth appear to have large positive effects only in high income countries, suggesting that developed economies have effectively gained from productivity improvement, mainly through technological development and innovation in agriculture. Agriculture in poorer economies often is largely labour-intensive, and cannot
7
readily adopt advanced technology and innovation due to economic limitations. WTO accession shows mixed results, similar to the previous estimates. Consistent to the earlier evidence and arguments, agricultural NRA shows significant and negative effects on agricultural exports regardless of economic status. It is important to note that its effect is about twice larger for developing countries than high-income countries.
[Table - 3: Results by economic levels on world agricultural exports]
We find consistent estimates of temperature and precipitation effects on agricultural
exports. Increases in temperature reduces agricultural exports for both high-income and developing economies. One degree Celsius increase in temperature reduces agriculture exports by 5.6% in developed economies, and by 12.6% in developing. Analysing further the developing economies we find that the lower middle-income and lower income countries are more susceptible to increased temperature. We find that a one degree-Celsius temperature increase reduces agricultural exports by a magnitude of 22.8% in lower middle-income countries and 39.1% in lower income countries. This indicates that middle-income countries experience no significant effects, while poor economies face a higher risk on their agricultural exports. We also find that increases in precipitation levels have negative effects on the exports of developing economies. A significant and negative effect is prevalent in lower middle-income and lower-income economies. These results are consistent with previous studies on the higher vulnerability of poor economies to climate change (e.g., Mendelsohn et al., 2006, Mendelsohn 2008; Jones and Olken, 2010, 455-458; Dell et al., 2012, 74-90).
5. Conclusion
We find that climate variations have significant impacts on agriculture exports globally. At a sub-sector level, we find that grains, oil seeds, livestock, and dairy and eggs, are significantly vulnerable to increases in the level of temperature. At regional levels, we identify that exports from the two major agriculture producing regions – Asia and Africa are most susceptible to rising temperature, while Australia-New Zealand benefit from it. Our findings provide further evidence that exports from both developed and developing economies are affected by the effects of increasing temperature, however, developing economies are considerably more vulnerable than high-income countries. We also find that changes in the levels of precipitation have no significant effects at the global level, however, precipitation is significant and negative for oil seeds at the sub-sectoral level, and Asia and Latin America.
Our results are wide-ranging as we control for climate-zone geo-differential fixed- effects, gravity effects, land endowment, WTO membership and agricultural nominal rates of assistance. Based on the exposed significant evidence of diverse sensitivities of trade to climate change, in order to build agricultural supply resilience to climate variations it is necessary to devise differentiated policy frameworks across sub-sectors, regions and economic levels.
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Table - 1: Sub-sectoral estimation result on world agricultural exports Total
Agriculture (Equation 1)
Agricultural Sub-sectors (Equation 2)
Explanatory Variables Grains
Oil seeds, nuts and kernels
Fruits and vegetables
Topical crops Livestock
Dairy and eggs
GDP pc (log) 0.855*** (0.056) 1.097*** (0.109)
0.563*** (0.120)
0.921*** (0.087)
0.838*** (0.074)
1.195*** (0.104)
1.639*** (0.168)
Arable land pc 0.401*** (0.096) 1.135*** (0.136)
1.660*** (0.263)
-0.155 (0.104)
-0.269* (0.150)
1.044*** (0.127)
0.639*** (0.166)
TFP growth 0.023 (0.197) 0.501
(0.545) 1.014* (0.533)
-0.049 (0.283)
0.102 (0.300)
0.772* (0.425)
0.393 (0.580)
WTO Mem -0.010 (0.081) 0.290
(0.237) 0.010
(0.240) 0.090
(0.107) 0.171
(0.121) 0.152
(0.199) 0.183
(0.242)
NRA Agri -0.164*** (0.048) -0.408*** (0.124)
-0.384** (0.154)
-0.181** (0.080)
-0.204*** (0.058)
-0.289*** (0.109)
-0.164 (0.146)
Temp -0.016** (0.008) -0.067*** (0.023)
-0.076*** (0.026)
-0.005 (0.011)
-0.009 (0.010)
-0.070*** (0.020)
-0.057** (0.025)
Precip 0.000 (0.000) -0.002 (0.001)
-0.002** (0.001)
0.000 (0.000)
0.000 (0.000)
0.000 (0.001)
-0.001 (0.001)
Trop Dummy 2.943*** (0.311) 1.992*** (0.692)
4.616*** (0.978)
2.957*** (0.481)
3.958*** (0.420)
1.538** (0.636)
1.477* (0.802)
Sub-trop Dummy
2.211*** (0.272)
2.607*** (0.485)
3.808*** (0.791)
2.769*** (0.395)
1.930*** (0.350)
2.657*** (0.481)
1.738*** (0.619)
Temp Dummy 1.675*** (0.171) 1.467*** (0.238)
2.479*** (0.511)
2.473*** (0.223)
1.139*** (0.178)
2.538*** (0.247)
1.564*** (0.278)
Constant 12.518*** (0.590) 8.751*** (1.065)
10.152*** (1.325)
9.565*** (0.903)
10.595*** (0.820)
6.362*** 1.009
2.688* (1.636)
R2 0.956 0.696 0.607 0.886 0.909 0.764 0.600
No. of Observations 2436 2423 2420 2435 2409 2342 2370
No. of Countries 67 67 67 67 67 67 67
Significance level: ***1%, **5%, *10%. Figures in parenthesis indicate HAC adjusted standard errors. Estimates for climate-zone fixed effects reported, however, for year fixed effects not reported. Source: Authors’ estimations
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Table - 2: Regional estimation result on world agricultural exports
Explanatory Variables (Equation 1)
US & Canada
Latin America
Central America EU-15
ETE -Central
Asia Asia Africa
Australia & New
Zealand
GDP pc (log) -2.088** (0.879) 0.953*** (0.313)
1.642*** (0.225)
1.893*** (0.092)
-0.110 (0.141)
0.022 (0.088)
1.053*** (0.057)
-2.676*** (0.810)
Arable land pc
0.252 (0.329)
-2.345*** (0.404)
-1.145** (0.524)
-0.542*** (0.193)
1.202** (0.510)
2.854*** (0.372)
-4.028*** (0.607)
0.025 (0.148)
TFP growth 0.335 (0.487) -0.067 (0.451)
0.605 (0.571)
0.273** (0.113)
-0.086 (0.267)
0.620 (0.648)
-1.955 (1.894)
-0.320 (0.860)
WTO Mem - -0.220* (0.123) 0.796*** (0.119) -
0.082 (0.156)
-0.349*** (0.116)
0.647*** (0.202) -
NRA Agri -0.143 (0.321) -0.416** (0.166)
-0.204 (0.168)
0.070 (0.050)
0.108 (0.091)
-0.261** (0.123)
-0.430 (0.280)
0.287 (0.597)
Temp -0.014 (0.042) -0.014 (0.013)
-0.005 (0.020)
0.003 (0.009)
-0.038 (0.025)
-0.048*** (0.012)
-0.144*** (0.039)
0.083*** (0.025)
Precip 0.005 (0.003) -0.001** (0.001)
0.000 (0.001)
0.000 (0.000)
0.004* (0.002)
-0.001*** (0.000)
-0.002 (0.003)
-0.001 (0.002)
Trop Dummy - 1.354*** (0.318)
0.642 (0.429) - -
1.158*** (0.247)
1.725*** (0.134) -
Sub-trop Dummy -
3.502*** (0.335) - - -
-1.174*** (0.312) - -
Temp Dummy
1.911*** (0.489) - -
2.179*** (0.065)
0.923*** (0.252) - - -
Constant 41.646*** (7.978) 13.321*** (2.457)
8.168*** (1.997)
1.645* (0.878)
20.083*** (1.584)
19.912*** (0.7088)
17.494*** (1.092)
46.196*** (7.404)
R2 0.998 0.983 0.999 0.984 0.979 0.973 0.486 0.974
No. of Observation s
102 251 58 629 246 564 498 88
No. of Countries 2 5 2 14 12 14 16 2
Significance level: ***1%, **5%, *10%. Figures in parenthesis indicate HAC adjusted standard errors. Estimates for climate-zone fixed effects reported, however, for year fixed effects not reported. Source: Authors’ estimations
12
Table - 3: Results by economic levels on world agricultural exports
Explanatory Variables (Equation 1)
Developed Economies
(HICs)
Developing Economies (DVCs)
All DVCs UMICs LMICs LICs
GDP pc (log) 1.766*** (0.089) 1.231*** (0.107)
-1.937*** (0.217)
0.889*** (0.299)
1.221** (0.521)
Arable land pc 1.250*** (0.106) 0.142
(0.519) 4.560*** (0.506)
-2.842*** (0.821)
-0.570 (2.247)
TFP growth 0.686*** (0.231) 0.211
(0.756) 0.970
(0.904) -0.335 (1.598)
0.643 (4.115)
WTO Mem 0.017 (0.138) 0.100
(0.169) 0.545** (0.241)
-0.739** (0.339)
3.298*** (0.951)
NRA Agri -0.284*** (0.078) -0.544** (0.215)
0.422 (0.277)
-0.104 (0.383)
-2.199** (0.965)
Temp -0.056*** (0.014) -0.126*** (0.021)
-0.020 (0.027)
-0.228*** (0.049)
-0.391*** (0.103)
Precip -0.001 (0.001) -0.003*** (0.001)
0.001 (0.001)
-0.008*** (0.001)
-0.020*** (0.007)
Trop Dummy - 1.162** (0.471) 0.760
(0.844) -0.062 (1.064)
0.225 (0.735)
Sub-trop Dummy 0.289 (0.433) 1.591*** (0.366)
2.541*** (0.770)
-0.823 (0.950) -
Temp Dummy 1.925*** (0.166) 0.853** (0.359)
0.747 (0.654) - -
Constant 1.089 (0.777) 8.667*** (0.754)
28.533*** (1.682)
15.567*** (1.981)
18.829*** (3.983)
R2 0.872 0.648 0.696 0.627 0.671
No. of Observations 1,166 1,257 477 593 187
No. of Countries 30 37 13 17 7
Significance level: ***1%, **5%, *10%. Figures in parenthesis indicate HAC adjusted standard errors. Estimates for climate-zone fixed effects reported, however, for year fixed effects not reported.
Source: Authors’ estimations
13
APPENDIX Table A1: Agricultural sectors considered
SITC Code Sector Breakdowns
00 Live animals
001 - Live animals other than Fish and aquatic or marines: Bovine animals, live Sheep and goats, live Swine, live Poultry, live (i.e., fowls of the species Gallus domesticus, ducks, geese, turkeys and guinea- fowls), Horses, asses, mules and hinnies, Live animals, n.e.s.
01 Meat and meat preparations
011 - Meat of bovine animals, fresh, chilled or frozen 012 - Other meat and edible meat offal, fresh, chilled or frozen (except meat and meat offal unfit or unsuitable for human consumption) 016 - Meat and edible meat offal, salted, in brine, dried or smoked; edible flours and meals of meat or meat offal 017 - Meat and edible meat offal, prepared or preserved, n.e.s.
02 Dairy products and birds' eggs
022 - Milk and cream and milk products other than butter or cheese 023 - Butter and other fats and oils derived from milk 024 - Cheese and curd 025 - Eggs, birds', and egg yolks, fresh, dried or otherwise preserved, sweetened or not; egg albumin
04 Cereals and cereal preparations (Grains)
041 - Wheat (including spelt) and meslin, unmilled 042 - Rice 043 - Barley, unmilled 044 - Maize (not including sweet corn), unmilled 045 - Cereals, unmilled (other than wheat, rice, barley and maize) 046 - Meal and flour of wheat and flour of meslin 047 - Other cereal meals and flours 048 - Cereal preparations and preparations of flour or starch of fruits or vegetables
05 Vegetables and fruits
054 - Vegetables, fresh, chilled, frozen or simply preserved (including dried leguminous vegetables); roots, tubers and other edible vegetable products, n.e.s., fresh or dried 056 - Vegetables, roots and tubers, prepared or preserved, n.e.s. 057 - Fruit and nuts (not including oil nuts), fresh or dried 058 - Fruit, preserved, and fruit preparations (excluding fruit juices) 059 - Fruit juices (including grape must) and vegetable juices, unfermented and not containing added spirit, whether or not containing added sugar or other sweetening matter
06 Sugars, sugar preparations and honey 061 - Sugars, molasses and honey 062 - Sugar confectionery
07 Coffee, tea, cocoa, spices, and manufactures thereof
071 - Coffee and coffee substitutes 072 - Cocoa 073 - Chocolate and other food preparations containing cocoa, n.e.s. 074 - Tea and maté 075 – Spices
22 Oil-seeds, nuts, kernels and oleaginous fruits
221- Oil-seeds, oil nuts, and oil kernels 222 - Oil-seeds and oleaginous fruits of a kind used for the extraction of "soft" fixed vegetable oils (excluding flours and meals) 223 - Oil-seeds and oleaginous fruits, whole or broken, of a kind used for the extraction of other fixed vegetable oils (including flours and meals of oil-seeds or oleaginous fruit, n.e.s.)
Source: UN Comtrade Database
14
Table A2: Regions and Countries considered
North America South America Western Europe/EU-15 ETE and Central Asia Africa Asia Oceania Central America Canada Argentina Austria Belarus Botswana Bahrain Australia Costa Rica
United States of America Bolivia Belgium Bulgaria Cameroon Bangladesh New Zealand El Salvador Brazil Denmark Croatia Côte d’Ivoire Cambodia Guatemala Chile Finland Cyprus Egypt China Honduras Colombia France Czech Republic Ethiopia Georgia Mexico Ecuador Germany Czechoslovakia Ghana Hong Kong Nicaragua Paraguay Greece Estonia Kenya India Panama Peru Ireland Hungary Madagascar Indonesia Uruguay Italy Latvia Malawi Iran Venezuela Luxembourg Lithuania Mauritius Israel Netherlands Malta Morocco Japan Portugal Norway Mozambique Kazakhstan Spain Poland Nigeria Korea Sweden Romania Senegal Kuwait United Kingdom Russia South Africa Kyrgyzstan Slovakia Tanzania Malaysia Slovenia Tunisia Nepal Switzerland Uganda Oman Ukraine Zambia Pakistan Zimbabwe Philippines Qatar Saudi Arabia Singapore Sri Lanka Thailand Turkey Vietnam
Source: Authors’ definitions .
15
Table A3: List of Countries by Economic Level (Income Class)
High Income Upper Middle Income Lower Middle
Income Low
Income Australia Kuwait Argentina Bangladesh Ethiopia
Austria Latvia Belarus Bolivia Madagascar
Bahrain Lithuania Botswana Cambodia Malawi
Belgium Luxembourg Brazil Cameroon Mozambique
Canada Malta Bulgaria Côte d’Ivoire Nepal
Chile Netherlands China Egypt Senegal
Croatia New Zealand Colombia El Salvador Tanzania
Cyprus Norway Costa Rica Ghana Uganda
Czech Republic Oman Ecuador Guatemala Zimbabwe
Czechoslovakia Poland Georgia Honduras
Denmark Portugal Iran India
Estonia Qatar Kazakhstan Indonesia
Finland Saudi Arabia Malaysia Kenya
France Singapore Mauritius Kyrgyzstan
Germany Slovakia Mexico Morocco
Greece Slovenia Panama Nicaragua
Hong Kong Spain Paraguay Nigeria
Hungary Sweden Peru Pakistan
Ireland Switzerland Romania Philippines
Israel United Kingdom Russia Sri Lanka
Italy United States of America South Africa Tunisia
Japan Uruguay Thailand Ukraine
Korea Turkey Vietnam
Venezuela Zambia
Source: World Bank Definition of Country Lending Groups, 2014
16
Table A4: List of Countries by Climatic Zone Tropical Sub-tropical Temperate Polar
Cambodia Argentina Australia Canada Cameroon Bahrain Austria Finland Colombia Bangladesh Belarus Norway
Costa Rica Bolivia Belgium Russia Côte d’Ivoire Brazil Botswana Sweden
Ecuador Egypt Bulgaria El Salvador Honduras Chile
Ethiopia Hong Kong China Ghana Israel Croatia
Guatemala Kuwait Cyprus India Mexico Czech Republic
Indonesia Morocco Czechoslovakia Kenya Oman Denmark
Madagascar Pakistan Estonia Malawi Paraguay France
Malaysia Peru Georgia Mauritius Qatar Germany
Mozambique Saudi Arabia Greece Nepal South Africa Hungary
Nicaragua Tunisia Iran Nigeria Zambia Ireland
Panama Zimbabwe Italy Philippines Japan
Senegal Kazakhstan Singapore Korea Sri Lanka Kyrgyzstan Tanzania Latvia Thailand Lithuania Uganda Luxembourg
Venezuela Malta Vietnam Netherlands
New Zealand Poland Portugal Romania Slovakia Slovenia Spain Switzerland Turkey Ukraine United Kingdom United States of America Uruguay
Source: Authors’ definitions
17
Table A5: List of Variables and Data Sources
Variable (notation) Explanation/Controlling for Source*
Agricultural Exports (agriexp)
Country-wise annual aggregate agricultural exports (US$ corrected for inflationary process)
United Nations Comtrade Database
GDP per capita (GDPpc) Economic activity/level (US$) WDI (the World Bank)
Arable land per capita (aralandpc)
Resource and factor endowments (hectare) WDI (the World Bank)
Total Factor Productivity in agricultural sector (tfpgr_ag)
Productivity improvement in all factors of production (land, labour and capital) (per cent growth rate)
United States Department of Agriculture (USDA)
Agricultural Nominal Rate of Assistance (NRA_ag) Farm policy interventions
Anderson and Valenzuela (2008); Anderson and Nelgen (2013), World Bank
Temperature (temp)
Yearly average of monthly near surface temperature (degree Celsius) Yearly average of monthly precipitation (millimetres); real analysis.
NCEP, NOAA/NCAR, USA1 NCEP, NOAA/NCAR, USA1 Precipitation (prec)
Climate Zone (dummies)
Clime Zone-wise fixed effects for: Topical, Subtropical, Temperate, and polar and subpolar zones.
Belda et al. (2014)
*Sources: World Development Indicators (WDI), agdistortions World Bank, National Center for Environmental Prediction (NCEP), National Center for Atmospheric Research (NOAA/NCAR), World Trade Organization (WTO).
18
Table A6: Descriptive Statistics Obs Mean Std. Dev. Min Max Total Agricultural Exports
World 4,272 4.23 9.25 0.00 118.00
Grains 4,196 0.99 3.01 0.00 41.20
Oil-seeds-nuts- kernels
4,08 0 0.31 1.59 0.00 26.00
Fruits and vegetables
4,21 7 0.97 2.22 0.00 22.90
Tropical crops 4,186 0.79 1.68 0.00 24.20
Livestock 3,985 0.85 1.98 0.00 18.90
Dairy and eggs 4,051 0.45 1.22 0.00 11.20
US & Canada 108 34.20 27.40 4.10 118.00 Latin America 527 3.81 7.86 0.00 65.20 Central America 347 1.63 2.52 0.20 18.00 EU-15 770 9.29 10.80 0.11 55.50 ETE-Central Asia 569 1.58 2.34 0.00 17.50 Africa 746 0.77 0.95 0.00 5.88
Asia 1,100 1.97 3.51 0.00 29.90
Australia-New Zealand 105 9.41 5.30 2.63 24.90
HIC 2,008 6.48 12.10 0.00 118.00
UMIC 930 3.84 6.72 0.00 65.20
LMIC 1,020 1.35 2.13 0.00 24.80
LIC 314 0.31 0.37 0.01 2.95 GDP Per Capita
World 4,559 10424.68 13720.58 83.33 87772.69
US & Canada 106 29401.01 8626.60 14428.35 46405.25 Latin America 530 3433.29 1904.98 598.08 9853.53 Central America 368 2955.63 1952.24 820.20 8521.89 EU-15 779 27465.43 13154.04 4262.56 87772.69 ETE-Central Asia 508 15385.49 16530.99 1123.41 69094.75 Africa 953 1259.90 1486.29 113.71 7116.59
Asia 1,225 7257.30 10505.92 83.33 62168.77
Australia-New Zealand 90 24444.96 6013.29 13952.13 37867.77
HIC 1,882 22443.29 14325.19 584.35 87772.69
UMIC 1,126 3510.99 1856.91 83.33 8864.74
LMIC 1,157 1024.51 653.95 234.17 3953.42
LIC 394 378.55 205.61 113.71 850.06 Source: Authors’ estimations
19
Table A6: Descriptive Statistics (Continued) Obs Mean Std. Dev. Min Max
Arable Land per Capita
World 4,781 0.32 0.38 0.00 3.50
US & Canada 104 1.22 0.53 0.48 2.21
Latin America 520 0.35 0.24 0.03 1.10
Central America 364 0.22 0.12 0.05 0.61
EU-15 704 0.27 0.14 0.06 0.60
ETE-Central Asia 635 0.35 0.22 0.00 0.89
Africa 1,040 0.31 0.17 0.03 0.90
Asia 1,311 0.17 0.28 0.00 2.20
Australia-New Zealand 103 1.68 1.12 0.10 3.50
HIC 1,997 0.34 0.51 0.00 3.50
UMIC 1,128 0.35 0.32 0.03 2.20
LMIC 1,188 0.26 0.17 0.03 0.90
LIC 468 0.30 0.14 0.08 0.88
Agricultural TFP Index
World 4,965 99.90 25.33 6.11 236.73
US & Canada 104 98.33 23.69 70.27 145.36
Latin America 520 97.40 22.88 52.97 178.78
Central America 364 109.58 29.56 39.61 206.08
EU-15 728 93.83 24.06 50.88 167.84
ETE-Central Asia 762 99.51 18.04 56.76 164.45
Africa 1,040 101.89 19.94 53.73 185.47
Asia 1,343 100.48 31.66 6.11 236.73
Australia-New Zealand 104 98.21 20.46 68.44 143.45
HIC 2,019 96.24 27.47 6.11 236.73
UMIC 1,236 100.67 24.60 39.61 202.26
LMIC 1,242 102.94 24.51 53.73 209.87
LIC 468 105.60 15.73 81.41 171.58
Source: Authors’ estimations
20
Table A6: Descriptive Statistics (Continued) Obs. Mean Std. Dev. Min. Max.
Temperature
World 5,353 16.98 7.50 2.80 29.26
US & Canada 106 6.27 2.93 2.80 10.10
Latin America 530 20.32 5.19 6.87 26.25
Central America 371 23.93 1.54 18.49 25.99
EU-15 795 9.81 2.77 3.58 15.30
ETE-Central Asia 954 9.41 3.33 2.87 18.84
Africa 1,060 23.18 2.73 16.26 29.26
Asia 1,431 19.24 7.35 4.23 28.17
Australia-New Zealand 106 15.91 5.59 9.20 22.57
HIC 2,332 12.65 6.58 2.80 28.06
UMIC 1,272 17.92 6.81 3.97 26.41
LMIC 1,272 22.24 5.13 5.71 28.35
LIC 477 21.55 6.09 4.23 29.26
Precipitation
World 5,353 82.57 54.94 2.11 307.33
US & Canada 106 52.30 7.26 40.49 67.27
Latin America 530 117.85 50.52 31.14 278.80
Central America 371 161.96 52.16 45.48 279.97
EU-15 795 68.46 16.89 35.45 127.71
ETE-Central Asia 954 56.64 14.50 27.16 116.15
Africa 1,060 69.02 37.92 2.11 255.40
Asia 1,431 85.87 72.37 2.23 307.33
Australia-New Zealand 106 88.78 48.18 22.36 164.86
HIC 2,332 64.40 38.06 2.23 243.58
UMIC 1,272 98.10 67.39 11.09 307.33
LMIC 1,272 103.29 63.40 2.11 271.73
LIC 477 74.71 22.78 28.94 155.06
Source: Authors’ estimations
21
Table A7: Diagnostics for the estimations (P-values reported)
Heteroskedasticity Cross
Section Dependence
Autocorrelation (AR1) Chosen Method
Test Name Modified Wald Pesaran Wooldridge
World 0.000 0.000 0.017 PCSE (AR1)
Asia 0.000 0.000 0.000 PCSE (AR1)
ETE-Central Asia 0.000 0.031 0.002 PCSE (AR1)
EU 15 0.000 0.000 0.000 PCSE (AR1)
Latin America 0.000 0.000 0.001 PCSE (AR1)
Africa 0.000 0.071 0.299 PCSE
US & Canada 0.856 0.000 0.061 PCSE (AR1)
Central America 0.999 0.888 0.190 PCSE (Independent) Australia & New Zealand 0.943 0.000 0.123 PCSE
High Income Countries 0.000 0.000 0.000
PCSE (AR1)
Developing Countries 0.000 0.000 0.002
PCSE (AR1)
Upper Middle Income Countries 0.000 0.000 0.000
PCSE (AR1)
Lower Middle Income Countries 0.000 0.000 0.342
PCSE
Lower Income Countries 0.001 0.002 0.077
PCSE (AR1)
Grains 0.000 0.000 0.000 PCSE (AR1)
Oil seeds, nuts and kernels 0.000 0.000 0.000 PCSE (AR1)
Fruits and vegetables 0.000 0.000 0.000 PCSE (AR1)
Tropical Crops 0.000 0.000 0.000 PCSE (AR1)
Livestock 0.000 0.000 0.000 PCSE (AR1)
Dairy and Eggs 0.000 0.000 0.000 PCSE (AR1)
Grains 0.000 0.000 0.000 PCSE (AR1)
Source: Authors’ estimations
22
Figure A8: Average Arable Land per Capita by Decade;
Figure A9: Average Annual Total Factor Productivity Growth by Decade
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“Climate change and agricultural development in West Africa: Role of renewable energy and trade openness”
AUTHORS Essossinam Ali https://orcid.org/0000-0002-7614-7426
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Essossinam Ali (2021). Climate change and agricultural development in West
Africa: Role of renewable energy and trade openness. Environmental
Economics, 12(1), 14-31. doi:10.21511/ee.12(1).2021.02
DOI http://dx.doi.org/10.21511/ee.12(1).2021.02
RELEASED ON Monday, 08 February 2021
RECEIVED ON Friday, 01 January 2021
ACCEPTED ON Saturday, 06 February 2021
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Environmental Economics, Volume 12, Issue 1, 2021
http://dx.doi.org/10.21511/ee.12(1).2021.02
Abstract
The design, implementation, and evaluation of energy policies in combating climate change are becoming increasingly evident to strengthen economic growth driven by the agricultural sector in most developing countries. The study analyzes the direct and indirect effects of renewable energy consumption (REC) on agriculture value-added (AgVA), CO2 emissions, and trade openness in the short- and long-run in the West African countries. The second-generation panel unit root tests, the panel cointegra- tion methods, and Panel Vector Error Correction Model are used with World Bank data from 1990 to 2015. A panel Granger causality test was also used to determine the direction of causality between variables. Findings show a unidirectional relationship between AgVA, CO2 emissions, and REC; between REC, gross fixe capital formation (GFCF) and trade openness. Moreover, the bidirectional hypothesis is verified between agricultural development and trade openness. However, the null hypothesis is found between AgVA and GFCF, on the one hand, and GFCF and CO2 emissions, on the other hand. These results suggest that fostering renewable energy policy and revisiting trade policy toward reducing environmental pollution will enable agricultural devel- opment and boost the regional economy.
Essossinam Ali (Togo)
Climate change and
agricultural development
in West Africa: Role
of renewable energy
and trade openness
Received on: 1st of January, 2021 Accepted on: 6th of February, 2021 Published on: 8th of February, 2021
INTRODUCTION
The role of renewable energy (RE) in economic development and its environmental benefits in terms of climate risk management has increased interest in recent debates around the world (Bayale et al., 2021; Frangou et al., 2018; Rafindadi & Ozturk, 2017; Liu et al., 2017a). According to Liu et al. (2017a) and Heidari and Pearce (2016), RE can be a key instrument in climate change adaptation and mitigation. It is well recognized that CO
2 emissions using RE technology are less than
traditional energy supply sources (Liu et al., 2017a; Ben Jebli & Ben Yousef, 2015; Heidari & Pearce, 2016). Increasing investment in RE production and consumption could be more economically beneficial and more viable than non-renewable energy use (Frangou et al., 2018; Kahia et al., 2017; Rafindadi & Ozturk, 2017). For example, Frangou et al. (2018) have estimated that saving from renewable energy con- sumption (REC) could be ranged from 3% to 23% on energy costs in the case study of Greece. According to Miketa and Merven (2013), the share of RE technologies will increase from 22% to 52% of current electricity generation in West Africa, with 46% of adding capacity by 2030. The use of RE as part of sustainable development goals is said to contribute significantly to poverty reduction and countries’ develop-
© Essossinam Ali, 2021
Essossinam Ali, Faculty of Economic and Management Sciences, Department of Economics, University of Kara, Togo.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
www.businessperspectives.org
LLC “СPС “Business Perspectives” Hryhorii Skovoroda lane, 10, Sumy, 40022, Ukraine
BUSINESS PERSPECTIVES
JEL Classification O13, Q27, Q56
Keywords renewable energy, agriculture, CO2 emissions, trade openness, panel cointegration, West Africa
Conflict of interest statement:
Author(s) reported no conflict of interest
15
Environmental Economics, Volume 12, Issue 1, 2021
http://dx.doi.org/10.21511/ee.12(1).2021.02
ments. The REC could boost the rural households’ economies and enhance job creation opportunities, while the non-access to energy would severely affect economic growth (Schwerhoff & Sy, 2017; Inglesi- Lotz, 2016; Birol, 2007).
Despite many studies in energy economics, there are still debates on the effect and relationship direc- tion between REC and economic growth. Four hypotheses are often discussed:
(I) the neutrality assumption, which does not support any relationship between energy use and eco- nomic growth;
(II) the unidirectional assumption between economic growth and energy consumption;
(III) the growth hypothesis, which strongly agrees that energy consumption leads to economic growth; and
(IV) feedback assumption, which assumes that energy consumption positively affects economic growth, and vice versa (Brini et al., 2017; Ben Jebli & Ben Yousef, 2017).
The specific energy policy could depend not only on the type of hypothesis but also on the geographical position of the region and the main drive of countries’ economies. In the context of globalization, trade openness that eases RE technology transfer can also lead to new empirical insights and policy impli- cations. While the agricultural sector remains the main wagon of West African countries’ economies (more than 35% of GDP), not much attention has been given to investigating the relationship between REC, CO
2 emissions, and agricultural development in the energy economics of the region. Moreover,
while trade openness can be a catalyst in RE transfer, it can be a source of pollution in the context of globalization in regions with non-binding environmental laws such as West African countries, and therefore, more investigations are needed.
This study fills this research gap in the empirical literature by analyzing the dynamicі between climate change captured by CO
2 emissions and agricultural development in West African countries while high-
lighting the role of renewable energy and trade openness. Specifically, this research assesses the short and long-run effects of REC and trade openness on agricultural development in West African countries. It also investigates the causality direction between REC, CO
2 emissions, and agricultural development
in the short- and long-run in the study areas. The results will help foster the design and implementation of an energy policy that encourages the creation and promotion of small-scale enterprises that work to develop renewable energy technology in the region. This study is also in line with the Sustainable Development Goals (SDG) agenda that seeks to ensure efficiency in terms of affordability and accessibil- ity of energy by 2030. The study will increase agricultural productivity in the region through energy use while combating climate change by reducing CO
2 emissions.
1. LITERATURE REVIEW
Natural resources such as energy must be managed so that future generations could benefit, while its consumption is expected to meet the needs of the population as defined sustainable development by Brundtland (1987). Indeed, the theory of sus- tainable development might find its origin after Meadows et al. (1972) demonstrate that economic growth could be limited if resources are not ra-
tionally or efficiently used. Since then, the theo- ry of green growth and sustainable development in the production process has gained researchers’ attention in the context of climate change and ag- ricultural development (Zaccour & Oubraham, 2018; Reilly, 2012). It is societies’ and policymakers’ responsibility to think about the effects of today’s actions on future generations. This refers to the concept of energy efficiency. The use of RE is then highly motivated for agricultural sustainability in
16
Environmental Economics, Volume 12, Issue 1, 2021
http://dx.doi.org/10.21511/ee.12(1).2021.02
a green growth perspective (Chel & Kaushik, 2011; Adelaja & Hailu, 2008). It is an example of agricul- tural land and other factors such as environment, biomass, and water resources. For instance, agri- culture, which is the main source of production and livelihood of most of the population, contrib- utes significantly to the CO
2 emissions (Liu et al.,
2017a; Ben Jebli & Ben Youssef, 2015). This leads to the rethinking of sustainability in the agricultur- al production process, hence the green economy concept. The green economy concept includes the trade-off between RE and non-renewable energy consumption that would have a strong link with economic growth (Bayale et al., 2021; Lyytimäki, 2018; Sutherland et al., 2015). Martinho (2018) and Sutherland et al. (2015) showed that agri- culture has an important role in the green econ- omy as a key sustainable development strategy. Energy security relies on the affordability of en- ergy but must consider the technological change (Proskuryakova, 2018).
The economic advantage that offers REC and the volatile nature of energy prices affecting glob- al economies led to countries’ setting up the roadmaps of 100% of REC (Sadiqa et al., 2018; Hohmeyer & Bohm, 2015). REC must be encour- aged in developing countries, given the rapid population growth and urbanization that could increase energy demand, which is forecasted to increase by 100% on the global scale and more than 120% in some countries, such as Russia and Brazil by 2050 (Resch et al., 2008). This is evi- dent in West African countries where the popu- lation is expected to increase by 2.5% per annum by 2050, increasing CO
2 emissions from human
activities and electricity generation technologies from non-renewable energy sources. Despite the contribution of energy to economic growth, the International Renewable Energy Agency (IRENA, 2018) has reported that the current state of energy consumption is alarming in West African coun- tries. For instance, the average rate of electricity access per capita ranges between 9% and 72%, with most countries less than 30% (IRENA, 2018) com- pared to 98% in North Africa (IRENA, 2015). Also, the International Energy Agency (IEA) (2014) has reported that West Africa is the only region in the world where the number of people living without electricity is increasing. Investing in RE technol- ogy adoption in the region could help reduce the
energy gap and boost economic growth driven by the agriculture sector.
Adelaja and Hailu (2008) found that using wind energy in the Michigan agricultural sector could increase farm net revenue by 50%. Similarly, Paramati et al. (2018) found that REC has positive- ly impacted economic activities, including agricul- ture, more than non-renewable energy. Whatever it comes from a renewable or non-renewable source, the energy constitutes a key input in the agricultural production process. During crop production and processing, RE use can directly or indirectly play an important role (transport of agricultural inputs, harvesting, packing, seeding and irrigation, poultry production, and transfor- mation of animals’ derived products). It can also help manage post-harvest losses (grain drying, food transformation, storage, and conservation). As a result, agriculture and its value chain actors could be highly vulnerable to the variability of fuel prices on the international market (Farajian et al., 2018; Martinho, 2016) if based production more on non-renewable energy consumption. Turkey’s case study by Bayrakci and Koçar (2012) showed a high trade surplus estimated at EUR 1.5 billion because of REC in agriculture.
Renewable energy use is an encouraging option in combating climate change induced by an in- crease in CO
2 emissions from fossil fuel consump-
tion. For example, agriculture is seen as the main source of CO
2 emissions. Simultaneously, using
RE will reduce CO 2
emissions and increase ag- ricultural value-added higher than the conven- tional energy impact (Wesseh & Lin, 2016). The countries’ dependency on fossil fuel imports and the increase in power costs because of rising de- mand are other reasons that support RE policy redesign, especially in Sub-Saharan Africa, where 90% of the population still do not have access to electricity (IRENA, 2018). For instance, the use of fossil fuels, pesticides, and synthetic fertilizer in agriculture could increase production costs and destroy progressively agricultural ecosystems and increase environmental damage with decreasing farm profitability in the long run because of the degradation of natural soil nutrients. Chel and Kaushik (2011) found that the use of wind ener- gy could reduce CO
2 emissions by approximately
0.9 tons per year. For sustainable agriculture, the
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use of RE could combat pollution, encourage the use of clean technologies, and facilitate the use of water pumps for irrigation (Chel & Kaushik, 2011).
Whether theoretically or empirically, the energy policy and recommendations for economic de- velopment among decision-makers are not unan- imous. The results depend on whether one con- siders the short- or long-run patterns, the area of study, or the methods adopted for the typical study. Using the Granger causality test, Ben Jebli and Ben Youssef (2017) found the unidirectional causality between economic growth and REC and agricul- tural value-added, non-REC and agricultural val- ue-added, and between CO
2 emissions and RE in
the short run. Moreover, the feedback hypothesis was found between agricultural value-added and CO
2 emissions, while the U-shaped environmen-
tal curve hypothesis was not verified. The bidirec- tional assumption was not supported in the case study of OECD countries (Alp, 2016). Using the vector autoregressive model, Johansen cointegra- tion, and Granger causality test, Alp (2016) found heterogeneous results between countries within the OECD region. There was no evidence of the relationship between energy consumption and economic development in eleven countries, while the growth hypothesis that strongly agrees on the energy consumption and economic growth nexus was satisfied within 6 countries only (Alp, 2016).
The heterogeneity characteristic of the effects of RE on economic growth was also found by Bhattacharya et al. (2016). A significant and positive effect of RE on economic development was found for 57% of the selected countries (Bhattacharya et al., 2016). Also, using quarterly time-series data, Rafindadi and Ozturk (2017) point out that REC, capital, and labor productivity could strengthen the German economy. The bidirectional effects between RE and economic growth were found. Considering the multivariate panel framework, Kahia et al. (2017) found the long-run relation- ship between REC, non-renewable energy con- sumption, labor force, and gross capital formation over 1980–2012. Bidirectional causality was found between REC and economic growth, non-renew- able energy consumption, and economic growth using the panel error correction model (Kahia et al., 2017). However, not much attention has been given to the nexus between climate change, REC,
trade openness, and agricultural development within the West African countries that promote regional integration for several decades. The study will expand the literature to foster regional energy policy for sustainable development.
2. METHODS
Several methods have been used to assess the so- cioeconomic impact of REC and environmental management. The methods used to analyze the energy demand and supply and the impact assess- ment of energy transition depend on whether one considers the national or regional scales (Farajian et al., 2018; Jenniches, 2018; Khan et al., 2018; Bhattacharya et al., 2016). The production func- tion is often served as the theoretical foundation in analyzing REC and economic growth nexus (Bhattacharya et al., 2016; Inglesi-Lotz, 2016). The model applied depends on the econometric tests required and the data used. Based on the green growth theory developed by Hickel and Kallis (2020), the neoclassical production function in which some inputs are used to produce a certain level of output is used. Assume that the agricultur- al development captured by the agricultural val- ue-added (AgVA) is mainly affected by greenhouse gas emissions (CO2), trade openness (Trade), and renewable energy consumption (REC) while con- trolling for other variables (X). Then, it follows:
( ), 2 , , , .it it it it it itAgVA f REC CO Trade X ε= (1)
Trade openness (Trade) represents the ratio of ex- ports and imports to GDP. Gross fixed capital for- mation (GFCF) representing domestic investment is used as a control variable. In equation (1), i rep- resents the country (i = 1… 12) and t is the year (t = 1990… 2015). The choice of variables is based on the literature and the study period depends on the availability of the data. Assuming that the spe- cific functional form of equation (1) is the Cobb- Douglas production function, equation (1) could be rewritten in the linear form as follows:
1
2 3
4
ln ln 2
ln
,
ln
it i i it
i it i it
i it i it
AgVA CO
REC Trade
GFCF t
ϑ ϕ ϕ ϕ ϕ δ ε
= + +
+ + +
+ + +
(2)
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where ε it is the error term, ϑ
i is a column vector
capturing the country-specific effect, and t is the deterministic time trend lasting from 1990 to 2015. φ and θ are the vectors of parameters to be estimated.
The cross-sectional dependence within individ- uals in panel data may be occurred because of common shocks due to a strong economic in- tegration within countries (for example, the regional energy policy of ECOWAS, the com- mon agricultural policy in WAEMU (ECOWAS and WAEMU are sub-regional blocs within the West African countries), coronavirus disease (COVID-19) or spatial dependence (WAEMU countries versus non-WAEMU countries; two sub-regional groups within ECOWAS). Ignoring this cross-sectional dependence within units by incorporating it into the error term will lead to an inconsistent estimate (Hoyos & Sarafidis, 2006). To check the cross-sectional dependen- cy within individuals (countries), Pesaran CD test and Breusch-Pagan test (Breusch & Pagan, 1980) are mostly used (Bhattacharya et al., 2016). In this study, the number of individuals (13) is less than the time dimension (26 years). In that case, instead of Pesaran CD test, Breusch-Pagan test used to check for cross-sectional depend- ence within countries is a better fit. If the hy- pothesis of the cross-dependency within varia- bles is verified, the panel unit root test will be applied using the second-generation test sug- gested by Pesaran (2007). The null hypothesis is that there is homogenous non-stationarity. If one fails to reject the null hypothesis of the ex- istence of panel unit root, it means the variances of the time series are unstable over time leading probably to the long-run relationships between variables. In that case, the panel error correc- tion model (PVCM) will be useful. The PVCM equations are stated as follows:
In equation (3), ∆ stands for the first difference of each variable. The long-term relationship be- tween variables is captured by the error correction term and represented by ECT
it–1 . In equation (3), μ
stands for the random error term.
The study covers 13 countries in West Africa, and the data from the World Bank Indicator (WDI, 2020) are used. The economic growth was, on av- erage, about 3.86% per year. The agricultural sec- tor was the main driver of economic growth and reached about 61.41% of GDP. It accounted on av- erage for 30.38% of gross domestic product (GDP) throughout 1990–2015 (Table 1).
The renewable energy consumption (REC) in the region has reached an average of 72.17% of total final energy consumed in West African countries. This would significantly influence agricultural development through direct use for agricultur- al production and reduce CO
2 emissions (climate
change). CO 2 emissions, which are the main com-
ponents of greenhouse gas, could also directly or indirectly affect the region’s economic growth. The average CO
2 emissions in the selected coun-
tries reached a maximum of 106 thousand kilo- tons. The trade openness reached about 62.07% of GDP.
Nigeria and Ghana, the leading economies of the region, have the highest economic growth (5.11% and 5.38%, respectively), while Guinea Bissau, Ivory Coast, and Sierra Leone have the lowest rate of economic growth (2.33%, 2.55%, and 2.82%, re- spectively). The average agricultural value-added in the leading economy of West Africa reached an- nually 24.81% for Nigeria and 33.22% for Ghana. The agricultural sector has an important contri- bution to GDP in the overall selected countries with more significance in Sierra Leone (49.02% of GDP) followed by Guinea-Bissau (47.39% of
2
1 11 12 13 14 15
2 21 22 23 24 25
31 32 33 34 35
4 41 42 43 44 45
5 51 52 53 54 5
3
5
ln
ln
ln
it p p p p p
it p p p p p
it p p p p p
it p p p p p
it p p p p p
AgVA
CO
REC
Trade
GFCF
α ϑ ϑ ϑ ϑ ϑ α ϑ ϑ ϑ ϑ ϑ α ϑ ϑ ϑ ϑ ϑ α ϑ ϑ ϑ ϑ ϑ α ϑ ϑ ϑ ϑ ϑ
∆ ∆ ∆ = + ∆ ∆
1 11
2 1 2
31 1
1
441
5 51
2
3
ln
ln
X ln .
it it
it itp
itit it
k
itit
itit
AgVA
CO
REC ECT
Trade
GFCF
µδ µδ µδ µδ
δ µ
−
−
− − =
−
−
∆ ∆ ∆ + + ∆ ∆
∑ (3)
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GDP). However, Burkina Faso has the highest ag- ricultural value addition (32.52%) and econom- ic growth within the West African Economic Monetary Union (WAEMU), a sub-regional group of West Africa, sharing the common cur- rency (the Franc CFA). The proportion of REC of the total energy used in West Africa was relative- ly high in countries such as Sierra Leone (85.99%), Nigeria (86.29%), Guinea-Bissau (88.54%), Mali (80.36%), and Burkina Faso (85.25%). The gross fixed capital formation (GFCF) that captures the investment level reached on average 30.50% of GDP in Mauritania, while it was only about 11.71% in Sierra Leone over the study period. However, Nigeria has the highest level of CO
2
emissions (73652.05 kt) and low level observed in Guinea-Bissau (203.82 kt). This can be explained by the level of economic development of each country, as highlighted by Kuznets (1955).
3. RESULTS
The correlation between variables and the multi- collinearity test are provided in Table 2. The aver- age variance inflation factors (VIF = 1.31) are less than 5% showing that multicollinearity between the considered variables is not a problem in the estimation process (O’Brien, 2007). Table 2 shows a positive correlation between REC and agricul- tural development (AgVA). The econometrics tests would shed light on the causality between these variables. However, a high and negative corre- lation between REC and trade openness was ob- served (Table 2).
Table 2. Correlation between variables and multicollinearity test
Variables AgVA REC CO 2
GFCF Trade
AgVA 1 – – – –
REC 0.49 1 – –
CO2 –0.18 0.16 1 – –
GFCF –0.24 –0.26 0.18 1 –
Trade –0.17 –0.52 –0.26 0.20 1
VIF (in %) – 1.45 1.16 1.17 1.48
Mean VIF 1.31
Moreover, there was a negative and low correlation between gross fixed capital formation and REC. This suggests a probable substitution between the use of renewable energy and investment cap- tured by gross fixed capital formation. Given the individuals and the time dimension, the Breusch- Pagan test was a better fit to check the existence of cross-sectional dependence within countries (Table 3).
Table 3. Breusch-Pagan test of independence
Variables Chi-squared
statistics p-value
AgVA 330.124*** 0.0000
REC 768.506*** 0.0000
CO 2
1422.923*** 0.0000
GFCF 299.576*** 0.0000
Trade 242.946*** 0.0000
Note: (***) indicates the significance level at 1%.
The results show that the probability values of all variables are less than 1% level. This implies that the hypothesis of cross-sectional dependency
Table 1. Average annual of the variables used in the study (1990–2015)
Countries GDP AgVA REC Trade GFCF CO 2
Benin 4.52 27.18 69.09 55.94 20.50 2852.46
Burkina Faso 5.36 32.52 85.25 39.31 21.87 1344.14
Ivory Coast 2.55 25.12 72.49 78.11 11.75 6963.26
Gambia 3.33 21.58 58.13 64.51 15.10 295.75
Ghana 5.38 33.22 64.47 75.98 20.79 7728.04
Guinea 3.75 19.04 82.92 61.40 20.50 1795.66
Guinea-Bissau 2.33 47.39 88.54 49.55 15.89 203.82
Mali 4.43 34.79 80.36 55.59 19.17 794.16
Mauritania 3.83 28.29 40.16 92.46 30.50 1581.00
Nigeria 5.11 24.81 86.29 38.62 29.62 73652.05
Senegal 3.58 15.64 47.89 61.04 21.33 5055.58
Sierra Leone 2.82 49.02 85.99 53.41 11.71 640.45
Togo 3.22 35.36 76.68 80.91 16.19 1532.21
West Africa 3.86 30.38 72.17 62.07 19.61 8033.74
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within individuals cannot be rejected. Therefore, the first generation test of panel unit roots is no longer appropriate. Thus, the second-generation panel unit root tests will be applied. The author showed a cross-sectional dependence between in- dividuals. In that case, the second-generation test of panel unit root proposed by Pesaran (2007) is the most appropriate (Table 4).
Table 4. Pesaran panel unit root test with cross-sectional
Variable CIPS Critical value
10% 5% 1%
lnAgVA –2.744 –2.66 –2.76 –2.96
lnREC –2.173 –2.14 –2.25 –2.45
lnCO 2
–2.355 –2.14 –2.25 –2.45
GFCF –2.805 –2.66 –2.76 –2.96
lnTrade –2.132 –2.14 –2.25 –2.45
The null hypothesis states that all variables are ho- mogeneous non-stationary. The alternative hypoth- esis is that the time series is stationary, and the in- tegration of variables is no longer important. The results show that the Pesaran statistic values (CIPS) are all greater than the critical values for all vari- ables at least at 1% level. This result suggests that all variables need to be integrated because they are non-stationary at the level. To check the exist- ence of the long-run relationship between variables, Westerlund panel cointegration tests were used (Westerlund, 2007). This test is more appropriate than the cointegration test performed by Perdoni (1999) since there is a cross-sectional dependence between individuals. This test assumes that there is no cointegration between variables. The results show that all statistics are significant, at least at a 5% level, including the robust p-values (Table 5).
Table 5. Westerlund ECM panel cointegration tests
Variable Statistics Value Z-value Robust p-value
AgVA
Gt –5.008*** –11.908 0.000
Ga –31.848*** –10.814 0.000
Pt –18.209*** –12.336 0.000
Pa –31.641*** –13.695 0.000
REC
Gt –3.861*** 6.756 0.000
Ga –27.839*** 8.641 0.010
Pt –13.099** 6.384 0.020
Pa –22.964** 8.457 0.040
Note: (***) indicates the significance level at 1%, (**) is the significance level at 5%.
This implies that the null hypothesis of the absence of cointegration can be rejected, hence the exist- ence of a long-run cointegration among variables. However, the stability of this long-run relationship should be tested. The Granger causality test results show a unidirectional causality, running from REC to agricultural value-added (Table 6).
The Granger causality test shows that a bidirection- al hypothesis is verified between agricultural val- ue-added and trade openness. Therefore, any change in trade openness will affect agricultural value-add- ed and vice-versa. On the one hand, this result can be explained by the fact that most agricultural products in West Africa are exported as raw materials (cot- ton, cocoa, coffee, cashew, rubber) and the subject of important revenues. Alternatively, West African countries are net importers of most transformed ag- ricultural goods, and trade openness can facilitate transactions. This result was also supported by Ben Jebli and Ben Youssef (2015). Similar results were found in the literature (Raeeni et al., 2019; Brini et al., 2017; Marques & Fuinhas, 2011) but contradict Liu et al. (2017) who show a positive relationship between REC and trade openness.
Raeeni et al. (2019) found no causality between trade and REC in the Iranian case study, while Marques and Fuinhas (2017) found that the mar- ket was not a determinant of renewable energy use in the case study of 24 European countries. The re- sults also showed no causality between trade open- ness and CO
2 emissions. This result is similar to
Raeeni et al. (2019), but contradicts Ben Jebli and Ben Youssef (2015) who found that trade openness may be a source of global warming as a transac- tion in transportation traded goods may lead to more CO
2 emissions. The results show that the
long-run coefficients of agricultural value-added, REC, and trade openness are significant at a 1% level (Table 7). It implies that the three models out of five have a long-run relationship.
The negative sign is associated with agricultural value-added and trade openness, while it is posi- tive for the REC. This suggests that trade openness will not favor agricultural development in West African countries in the long run. The adjustment amount of REC from short-run to long-run is 0.076. This suggests that the previous shocks on agricultural value-added, CO
2 emissions, gross
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fixed capital formation, and trade openness will increase the demand for renewable energy by 7.6% in the long run. This can be achieved by increas- ing the investment in renewable energy produc- tion significantly (Bayale et al., 2021; Le & Van, 2020; Liu et al., 2017; Rafindadi & Ozturk, 2017). The evidence is that there is a unidirectional rela- tionship between GFCF and REC in the short run, indicating that the GFCF is a determinant of re- newable energy use. However, the results indicate that any shocks in the previous periods could have negatively resulted in agricultural growth and
trade openness at the long-run equilibrium. These shocks might be climate change (increase in CO
2
emissions), low investment in renewable energy production, or the pollution from globalization that is prone to trade openness (Schwerhoff & Sy, 2017; Ben Jebli & Ben Youssef, 2017).
4. DISCUSSION
This result suggests that any REC change has an immediate impact on agricultural development
Table 6. Granger causality test
Variable AgVA REC CO 2
Trade openness GFCF
AgVA –
Unidirectional causality between
REC and AgVA
Unidirectional causality between
CO 2 and AgVA
Bidirectional causality between Trade openness and
AgVA
No causality
REC
Unidirectional causality between
REC and AgVA
– No causality No causality
Unidirectional causality between
GFCF and REC
CO 2
Unidirectional causality between
CO 2 and AgVA
No causality – No causality No causality
Trade
openness
Bidirectional causality between Trade
openness and AgVA
No causality No causality –
Unidirectional causality between
GFCF and Trade
openness
GFCF No causality
Unidirectional causality between
GFCF and REC
No causality
Unidirectional causality between GFCF and Trade
openness
–
Table 7. Panel Vector Error Correction Model (PVCM) estimating long-run causality
Variables ∆(lnAgVA) ∆(lnCO 2 ) ∆(lnREC) ∆(lnTrade) lnGFCF
Long-run
EC t–1
–1.099*** 0.161 0.076*** –0.506*** 3.996
(0.082) (0.123) (0.036) (0.146) (3.943)
Short-run
∆(lnAgVA t-1
) 0.053 –0.100 –0.033322 0.340*** –1.572
(0.056) (0.084) (0.025) (0.100) (2.702)
∆(lnCO2 t-1
) –0.100*** –0.470*** –0.019 –0.0096 –1.274
(0.040) (0.060) (0.018) (0.071) (1.932)
∆(lnREC t-1
) –0.467*** 0.037 –0.518*** –0.313 –4.496
(0.121) (0.182) (0.054) (0.216) (5.808)
∆(lnTrade t-1
) 0.163*** –0.044 –0.015 –0.444*** –0.155
(0.030) (0.046) (0.013) (0.054) (1.467)
lnGFCF t-1
0.001 –0.002 0.001*** –0.004** –0.192***
(0.001) (0.001) (0.001) (0.002) (0.058)
C 0.002 –0.010 –0.001 0.0001 0.146
(0.006) (0.009) (0.002) (0.011) (0.307)
R2 0.5119 0.2083 0.2825 0.3579 0.4550
Adj. R2 0.5019 0.1920 0.2678 0.3447 0.2590
Note: (***) indicates the significance level at 1%, (**) is the significance level at 5%.
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in the West African region. Similar results were found by Raeeni et al. (2019) in the Iranian case study and by Ouedraogo (2013) in WAEMU coun- tries. In achieving sustainable development goals in West Africa, the role of RE in agricultural devel- opment recalls attention on financing RE (Raeeni et al., 2019; Schwerhoff & Sy, 2017). Moreover, the results indicate that the unidirectional hypothesis is verified between AgVA and CO
2 emissions. This
causality runs from CO 2 emissions to agricultur-
al value-added. This result implies that climate change induced by an increase in CO
2 emissions
could directly impact agricultural value-added. This result corroborates most studies recogniz- ing the pronounced impacts of climate change on crop productivity in West African countries (Ali et al., 2020; Parkes et al., 2018; Ali, 2018). However, the bidirectional hypothesis was verified in the case study by Ben Jebli and Ben Youssef (2015) in Tunisia. The results show that the unidirectional hypothesis is verified between REC and gross fixed capital formation (GFCF) running from GFCF to REC, on the one hand, and trade openness and GFCF running from GFCF to trade openness, on the other hand. This implies that any change in investment level will affect renewable energy con- sumption and trade openness. Indeed, an increase in investment level might increase renewable en- ergy technologies adoption, therefore, requiring trade openness. This result is similar to those from Le and Van (2020).
In light of the above results, there is a need to test the stability of the long-run relationship between variables, as shown by the inverse roots of AR characteristic polynomial (Figure 1).
Figure 1 shows that all inverse roots lie within the unit circle. It implies that the long-run relationship between variables is stable; therefore, any energy policy response in this study could be validated.
The results from PVCM indicate that CO 2 emis-
sions, renewable energy consumption, and trade openness at lag one significantly influence agricul- tural value-added. Renewable energy consump- tion might favor agricultural growth, while CO
2
emissions and trade openness would negatively affect. Trade openness in the context of globaliza- tion could facilitate technology transferability and input supply for agricultural development. It could also be a catalyst to the agricultural commodity market at the international level. Indeed, increas- ing the demand for renewable energy can increase agricultural production, and trade openness can be a catalyst in trading agricultural commodities and technology transfer for agricultural develop- ment. The negative sign might be explained by the fact that most of the agricultural products subject to international trade are exported as raw materi- als. The transformed goods from these raw mate- rials are then imported for households’ final con- sumption. In that case, importing more agricul-
Figure 1. Test of stability of long-run relationship among variables
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse roots of AR characteristic polynomial
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tural commodities for households’ consumption will favor mostly the agricultural development of net exporting countries while negatively affect the agriculture growth of net importer countries with low financial development (Kim et al., 2012). The other reason is sanitary and phytosanitary barri- ers that mostly face the agricultural commodities trade. Also, agricultural production subsidies in developed countries can lead to the negative im- pact of trade openness on agricultural develop- ment in developing countries, including the West
African countries. Reducing trade barriers can re- sult in the expected impact of regional integration on countries’ economies.
The second and fifth models indicate that there is no long-run causality relationship. The results show that agricultural value-added, renewable energy consumption, trade openness, and gross fixed capital formation do not significantly affect CO
2 emissions. Similarly, the agricultural val-
ue-added, renewable energy consumption, trade
Figure 2a. CO2 policy response on agricultural development by 2030 (SDG agenda)
Figure 2c. Regional integration policy response on agricultural development by 2030 (SDG agenda) in West Africa
Figure 2b. REC policy response on agricultural development by 2030 (SDG agenda)
Response of DLNAGVA to Cholesky
One S.D.DLNCO2 Innovation
-.010
-.008
-.006
-.004
-.002
.000
.002
.004
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
.000
.005
.010
.015
.020
.025
.030
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Response of DLNAGVA to Cholesky
One S.D.DLNREC Innovation
-.05
-.04
-.03
-.02
-.01
.00
1 2 3 4 5 6 7 8 9 10
Response of DLNAGVA to Cholesky
One S.D.DLNTRADE Innovation
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openness, and CO 2 emissions do not significantly
affect gross fixed capital formation. However, the findings show that agricultural growth has posi- tively affected trade openness, while there was a substitution between gross capital formation and trade openness.
In the specific context of West African countries, it becomes important to analyze the policy re- sponse of Sustainable Development Goals agenda (objective number 7) that seeks to ensure access to affordable, reliable, sustainable, and modern ener- gy for all by 2030 with a focus on clean, renewable
Figure 3c. Trade openness policy response on agricultural development by 2063 (African Union agenda)
-.010
-.008
-.006
-.004
-.002
.000
.002
.004
5 10 15 20 25 30 35 40 45 50
Response of DLNAGVA to Cholesky
One S.D.DLNCO2 Innovation
Figure 3a. CO2 emissions policy response on agricultural development by 2063 (African Union agenda)
.000
.005
.010
.015
.020
.025
.030
5 10 15 20 25 30 35 40 45 50
Response of DLNAGVA to Cholesky
One S.D.DLNREC Innovation
Figure 3b. REC policy response on agricultural development by 2063 (African Union agenda)
-.05
-.04
-.03
-.02
-.01
.00
5 10 15 20 25 30 35 40
Response of DLNAGVA to Cholesky
One S.D.DLNTRADE Innovation
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energy use in combating climate change. Similarly, Agenda 2063 of the African Union has set a roadmap of environmentally sustainable and cli- mate-resilient economies by increasing the use of renewable energy and fostering regional integra- tion. Either SDG agenda (Figures 2a, 2b, and 2c) or Agenda 2063 of the African Union (Figures 3a, 3b, and 3c), the impulse responses of an increase in CO
2 , REC and trade openness have similar
trends. Figures 2a, 2c, 3a, and 3c show that an in- crease in CO
2 and trade openness considering the
SDG agenda and African Union agenda will neg- atively affect the agricultural value-added of West African countries starting from 2023. However, an increase in REC will positively affect agricultur- al development. The high effect of the use of REC will be observed for the first three years and will start declining in the fourth year. After 5 years, the impact of REC on agriculture will remain con- stant but positive.
The results show that globalization through trade openness will increase CO
2 emissions. Whether
one considers the SDG agenda (Figure 4a) or the
African Union agenda (Figure 4b), similar trade openness effects on CO
2 emissions are observed.
These results corroborate those from Mutascu and Sokic (2020) and Sannasse and Seetanah (2016). The positive relationship between trade openness and pollution was also found by Bataka (2021) who found that globalization contributes to environ- mental pollution in Sub-Saharan African countries.
Trade openness can increase CO 2 emissions if
most imported goods are highly pollutants. Also, it is well recognized that developing countries like the West African countries have dirty industries with non-binding environmental laws in contrast to developed countries. These findings suggest that the impact of trade openness on CO
2 emissions
may depend on economic development; therefore, the re-examination of trade policy in developing countries toward a clean environment is needed for sustainable economic development. Findings also show that trade openness can increase renew- able energy consumption, which can be used for agricultural production (see Figures A1 and A2 in Appendix A).
Figure 4b. Trade openness policy response on CO2 emissions by 2063 (African Union agenda)
Figure 4a. Trade openness policy response on CO2 emissions by 2030 (SDG agenda)
-.002
-.001
.000
.001
.002
.003
.004
.005
.006
1 2 3 4 5 6 7 8 9 10
Response of DLNCO2 to Cholesky
One S.D.DLNTRADE Innovtion
-.002
-.001
.000
.001
.002
.003
.004
.005
.006
5 10 15 20 25 30 35 40
Response of DLNCO2 to Cholesky
One S.D.DLNTRADE Innovtion
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CONCLUSION
The use of renewable energy is a key strategy in combating climate change, one of the most world’s con- cerns of the century. In the context of developing countries where rapid population growth and urban- ization are expected, renewable energy consumption (REC) must be encouraged to meet the increasing demand for energy while reducing CO2 emissions. Therefore, the new empirical evidence is needed for setting a roadmap of regional energy policy for economic development. However, the specific energy policy could depend not only on the type of hypothesis of the relationship between REC and economic development but also on the geographical position of the region and the main drive of countries’ econo- mies. In the context of developing countries, including the West African region, where the agricultural sector remains the main driver of countries’ economies, not much attention has been given to the role of trade openness as a catalyst for RE technology transfer and REC in investigating the dynamic between CO
2 emissions as the main source of climate change and agricultural development of the region. This
study analyzes the relationship between CO 2 emissions and agricultural development in West African
countries by focusing on the role of renewable energy and trade openness.
The second-generation panel unit root tests, the Westerlund cointegration methods were used with the data from 13 countries of West Africa from 1990 to 2015. A panel error correction model was used to analyze the long-run relationship between variables. A panel Granger causality test was also used to check the causality direction between variables. Findings show a unidirectional relationship between agricultural value-added (AgVA) and CO
2 emissions running from CO
2 emissions to AgVA. The unidi-
rectional causality was also found from REC to agricultural value-added. The results confirm the uni- directional hypothesis running from the gross fixed capital formation (GFCF) to REC, on the one hand, and from GFCF to trade openness, on the other hand. Moreover, the bidirectional hypothesis is verified between agricultural development and trade openness with positive and significant effects. The results show that previous shocks on different variables might result in a negative effect on agricultural val- ue-added and trade openness in the long-run. However, the results show that the previous shocks on ag- ricultural value-added, CO
2 emissions, gross fixed capital formation, and trade openness will increase
the renewable energy demand by 7.6% in the long run. Considering the SDG agenda (Agenda 2030) or the African Union agenda (Agenda 2063), the impulse response of REC showed a positive effect on agricultural value-added while negatively related to CO
2 emissions and trade openness. Increasing the
demand for renewable energy can spur agricultural production, and trade openness can ease the trade of agricultural commodities. However, exporting more agricultural commodities as raw materials and importing mostly high pollutant commodities will result in a positive effect relationship between trade openness and CO
2 emissions, as shown in the policy responses of Agenda 2030 and 2063 in the results.
These results suggest that fostering renewable energy policy in West African countries will contribute to agricultural development. However, a re-examination of trade policy to reduce environmental pollution should be a priority for the West African countries to gain from the regional integration.
AUTHOR CONTRIBUTIONS
Conceptualization: Essossinam Ali. Data curation: Essossinam Ali. Formal analysis: Essossinam Ali. Funding acquisition: Essossinam Ali. Investigation: Essossinam Ali. Methodology: Essossinam Ali. Project administration: Essossinam Ali. Resources: Essossinam Ali. Software: Essossinam Ali. Supervision: Essossinam Ali.
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Validation: Essossinam Ali. Visualization: Essossinam Ali. Writing – original draft: Essossinam Ali. Writing – review & editing: Essossinam Ali.
ACKNOWLEDGMENT
The author wants to thank Dr. Moukpè GNINIGUE for his technical supports and Prof. Jean Marcelin Bosson BROU from the University of Houphouet Boigny (Cote d’Ivoire), Dr. Odzadifo K. WONYRA and Dr. Hodabalo BATAKA from the University of Kara, Dr. Koffi Massesso ADJI from the West African Sciences Services Centre on Climate Change and Land Use (University of Cheikh Anta Diop, Dakar) and Essotanam MAMBA from the University of Lomé for their constructive comments on the earlier version of this manuscripts. Finally, the author is grateful to the anonymous reviewers and Editor-in- Chief of Environmental Economics, whose comments have improved this paper. However, the opinions expressed in this paper are solely those of the author.
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APPENDIX
Figure A1. Policy responses in light of sustainable development agenda (Agenda 2030)
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10
Response of DLNAGVA to DLNAGVA
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10
Response of DLNAGVA to DLNCO2
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10
Response of DLNAGVA to DLNREC
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10
Response of DLNAGVA to DLNT RADE
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of DLNCO2 to DLNAGVA
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of DLNCO2 to DLNCO2
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of DLNCO2 to DLNREC
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
Response of DLNCO2 to DLNT RADE
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of DLNREC to DLNAGVA
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of DLNREC to DLNCO2
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of DLNREC to DLNREC
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of DLNREC to DLNT RADE
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of DLNT RADE to DLNAGVA
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of DLNT RADE to DLNCO2
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of DLNT RADE to DLNREC
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of DLNT RADE to DLNT RADE
Response to Cholesky One S.D. Innov ations
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Figure A2. Policy response in light of African Union agenda (Agenda 2063)
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35 40
Response of DLNAGVA to DLNAGVA
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35 40
Response of DLNAGVA to DLNCO2
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35 40
Response of DLNAGVA to DLNREC
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35 40
Response of DLNAGVA to DLNT RADE
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35 40
Response of DLNCO2 to DLNAGVA
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35 40
Response of DLNCO2 to DLNCO2
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35 40
Response of DLNCO2 to DLNREC
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35 40
Response of DLNCO2 to DLNT RADE
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35 40
Response of DLNREC to DLNAGVA
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35 40
Response of DLNREC to DLNCO2
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35 40
Response of DLNREC to DLNREC
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35 40
Response of DLNREC to DLNT RADE
-.1
.0
.1
.2
5 10 15 20 25 30 35 40
Response of DLNT RADE to DLNAGVA
-.1
.0
.1
.2
5 10 15 20 25 30 35 40
Response of DLNT RADE to DLNCO2
-.1
.0
.1
.2
5 10 15 20 25 30 35 40
Response of DLNT RADE to DLNREC
-.1
.0
.1
.2
5 10 15 20 25 30 35 40
Response of DLNT RADE to DLNT RADE
Response to Cholesky One S.D. Innov ations
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Food Policy 36 (2011) S9–S13
Contents lists available at ScienceDirect
Food Policy
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / f o o d p o l
Climate change and trade in agriculture q,qq
Hsin Huang, Martin von Lampe, Frank van Tongeren ⇑ 2, rue André Pascal, 75775 Paris, CEDEX 16, France1
a r t i c l e i n f o
Keywords: Climate change Trade policy Mitigation Adaptation Carbon leakage Carbon pricing
0306-9192/$ - see front matter � 2010 Queen’s Prin doi:10.1016/j.foodpol.2010.10.008
q The views expressed in this paper are those of the official view of the OECD or of the governments of its qq Disclaimer: While the Government Office for Scien the views are those of the author(s), are independen constitute Government policy. ⇑ Corresponding author.
E-mail address: frank.vantongeren@oecd.org (F. va 1 The authors are with the Organisation for Econo
opment (OECD), Trade and Agriculture Directorate.
a b s t r a c t
Agricultural productivity in both developing and developed countries will have to improve to achieve substantial increases in food production by 2050 while land and water resources become less abundant and the effects of climate change introduce much uncertainty. Already less resilient production areas will suffer the most, as temperatures will rise further in tropical and semi-tropical latitudes and water-scarce regions will face even drier conditions. International trade plays an important role in compensating, albeit partially, for regional changes in productivity that are induced by climate change. While a well- functioning international trade system can support the adaptation to climate change-related challenges, trade policies as such are imperfect instruments to induce less emissions globally. A well-functioning international trading system can support the adaptation to climate change-related challenges. Hence welfare gains from reforms to trade policies may be greater than normally measured if they also reduce GHG emissions globally.
� 2010 Queen’s Printer and Controller of HMSO. Published byElsevier Ltd. All rights reserved.
Driving forces in agricultural markets: where does climate change come in?
According to some estimates global food production will have to increase by 70% until 2050 (relative to 2007), to meet the de- mands of a growing population (OECD, 2009, based on FAO, 2006, and Bruinsma, 2009). Agricultural productivity in both developing and developed countries will have to improve to achieve this, while land and water resources become less abundant and the effects of climate change introduce much uncertainty. International trade will become increasingly important in connect- ing food surplus with food deficit areas.
Climate change potentially affects key drivers of international trade in agricultural products. Trade theory has traditionally emphasized differences in technology (Ricardo) and differences in endowments of production factors (Heckscher–Ohlin–Samuel- son) as determinants of international trade. According to this body of literature, countries will tend to specialize in exports of those goods that use intensively the relatively abundant factor of production.
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Through its effects on productivity and yields, climate change impacts on the technology dimension behind trade patterns. Through its impact on the amounts of arable land and water, it im- pacts on the endowments dimension. Changes in the returns to factors of production employed in agriculture are driving the po- tential changes in patterns of geographical specialisation of pro- duction. While climate change has a direct bearing on those relative returns, international trade will tend to reinforce those changes, as trade in goods is ultimately an exchange of the services of those production factors incorporated in the traded goods. If a production factor is specific to the production of one good, such as land being (almost) specific to agriculture, the specific factor model (Ricardo–Viner) shows that trade will increase the returns of the specific factor used in the export good.
On the input side to agricultural production a number of major factors have a direct link to climate change. (for a more compre- hensive review see e.g. FAO (2003, 2006), This particularly con- cerns developments in energy markets, the availability and use of water and land resources, and agriculture’s potential to both emit and sequester GHGs.
Energy prices have long been an important factor in agricultural production costs. Both fuel and other energy-rich inputs to agricul- ture represent considerable shares in variable costs differing across agricultural commodities and production regions. Increasingly, su- gar and grain crops are also used for transport fuel production, while other forms of bioenergy use biomass are competing with food and feed production for land and other resources, hence strengthening the link between energy markets and agricultural markets. While most of the biofuel and bioenergy chains depend
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2 The process for the next update, AR5, is currently in preparation and the final report is scheduled for 2013.
S10 H. Huang et al. / Food Policy 36 (2011) S9–S13
strongly on government subsidies, higher energy prices in general (as well as growing concerns about climate change) also result in increased interest in these forms of renewable energy deemed (though not always justifiably so) to significantly contribute to GHG emission savings (OECD, 2008).
Land use for agricultural production is expanding notably in La- tin America and parts of Africa, but global agricultural area has been largely constant over the past 15 years following losses due to competing land use (urbanization, industrialization, etc.) and environmental pressures. Bringing significant new areas of land under production has a wide variety of social implications, re- source and environmental consequences, including on deforesta- tion and forest degradation in developing countries, an important issue under climate change negotiations. Deforestation, mainly to convert land for agricultural use is responsible for some 17% of cur- rent global GHG emissions.
Agriculture uses some 70% of global freshwater withdrawals, mostly in irrigation systems which are responsible for a substantial share in the growth of average yields, as well as for making output less volatile. With water basins being increasingly overused in an increasing number of countries and regions, and polluted and/or salinated in others, water shortages are likely to increasingly limit the growth of agricultural production.
Next to those drivers on the supply side, the developments of population and consumer demand play a key role in shaping inter- national trade flows. The most dynamic development in food and feed demand in recent years has occurred in the developing world, and especially in Asia (OECD/FAO, 2010). While an increasing pro- portion of agricultural output is being traded (Anderson, 2010), most growth takes place in processed products and market differ- entiation is increasingly important. Essentially all growth of the world population until 2050 is expected to materialize in the developing world (UN, 2008). This not only translates to growth in volume of food demand in those regions, but in combination with increasing incomes is expected to lead to a more animal pro- tein-rich diet, with obvious consequences for trade in animal feed and livestock products.
Finally, there is the dimension of policy interventions that change relative prices facing producers and consumers, and hence alter their production and consumption decisions. Agriculture par- ticularly in a number of OECD countries is known to benefit from substantial transfers from both tax payers and consumers (OECD, 2010). Much of that support remains coupled to production and hence keeps distorting markets and trade. In a similar way, unac- counted negative (or positive) external effects of agricultural pro- duction, such as the emission (or sequestration) can be seen as an implicit support (or tax) to agricultural production, coupled to output to the degree these external effects are directly linked to production. Just as tariffs or payments can distort production deci- sions and trade, lacking internalization of external effects can have direct implications on agricultural markets and trade flows.
A new set of climate-change related policies adds to this set of interventions and affects the incentives that producers and con- sumers are facing. Policies such as carbon taxes, border carbon adjustments and carbon footprint standards alter the relative prices of commodities according to their carbon content and thus may hamper or foster trade flows, depending on the nature of their implementation. The challenge is to design policies that are addressing climate change while being least trade restrictive.
To assess how exactly those impacts translate into changes in factor returns, production and trade patterns, including specialisa- tion within the agricultural sector and between agriculture and other sectors, large scale models are needed that incorporate huge amounts of data. Even with well developed data to support future projections with empirical observation, the assessment remains somewhat speculative as their exist many uncertainties, particu-
larly regarding the impacts of climate change on productivity of crops and livestock. A part of that uncertainty stems from still inconclusive scientific results on the future evolution of climate change, another part stems from the inherent unpredictability of developments in new technologies and techniques to adapt to changing climatic circumstances.
Climate change and impact on agriculture
Climate change projections vary and there are different views about the extent of likely effects of climate change on agriculture. Despite those differences, the consensus is that already less resil- ient production areas will suffer the most, as temperatures will raise further in lower latitudes (tropical and semi-tropical) and water-scarce regions will face even drier conditions. Heat-related and water borne-diseases related to rising temperatures and more flooding may also increase food safety risks.
There is a large body of literature on the impacts of climate change on agricultural production, but generally individual studies are only focusing on a particular country or region. The vast major- ity of studies are on crops, and only a few on livestock. The Inter- governmental Panel on Climate Change (IPCC, 2007), in its latest assessment (Fourth Assessment Report, AR4),2 summarizes the glo- bal agricultural situation as follows:
� Climate change will lead to higher average global temperatures over the time scale of several decades. The global distribution of temperature change is quite variable – locally this may trans- late into more extremes of warm and cold. � Implications for crop yields are generally positive for tempera-
ture increases up to 3 �C (longer growing season and CO2 fertil- ization), after which increasing heat stress implies a trend to decreases in crop yields. While there is much uncertainty around these estimates, whatever the net effect globally, the expectation is that farm productivity may be higher in temper- ate rain-fed regions and lower in the tropics. The negative impacts on yield may be partly overcome through adoption of appropriate varieties, but requires investment into basic research and development as well as into deployment of new varieties and extension. � Higher temperatures lead to greater evapotranspiration, and
therefore faster cycling of water through the system, although the global quantity available may not change significantly. Pat- terns of precipitation are very likely to change. Floods and droughts may become more frequent; however, there is a high degree of uncertainty on the causal relationships between cli- mate change and frequency of extreme events. While this has implications for rain-fed crops, irrigated crops may also face issues with water scarcity.
Model-based projections of the global agricultural system de- pend to a large extent on the assumed changes in agricultural pro- ductivity, but the effects of climate change on agricultural productivity and hence on trade flows are highly uncertain, partic- ularly at a regional level. Many studies highlight the importance of assumptions for crop yields under increasing levels of CO2 concen- tration in the atmosphere (CO2 fertilization). While laboratory tri- als under optimal conditions have demonstrated crop yield increases, few results are available under real world conditions. Reilly et al. (2007) analyse the counter-argument that increased respiration of ozone may off-set the positive yield effects of CO2 fertilization. Moreover, as already noted, as the severity of climate
H. Huang et al. / Food Policy 36 (2011) S9–S13 S11
change increases, other factors having a negative impact on yield are likely to dominate.
The body of literature on the wider economic impacts of climate change, particularly those including quantitative estimates span- ning most sectors, is relatively small. Tol (2009) finds that only 14 estimates of the total damage cost of climate change have been published (includes all sectors), but notes that ‘‘the number of authors is lower and can be grouped into a UCL group and a Yale one’’. Generally, the studies find that the impacts of a doubling of atmospheric concentration of GHG on the current economy are rel- atively small. He also notes that more recent studies tend to come up with smaller estimates, due in part because earlier studies ex- cluded reaction by farmers, thereby overstating some of the costs.
The recognition that economic agents would adapt to climate change in ways that reduce negative impacts and take advantage of positive impacts is one of the most important advances in newer studies. However, as Antle (2008) points out, while aggregate im- pacts are often estimated to be relatively small, important local im- pacts may be expected, particularly in the poorest and most vulnerable regions of the tropics. He argues that to the extent change is gradual, farmers with access to resources (i.e. in devel- oped countries) will be able to adapt through changes in crop man- agement/selection and appropriate capital investments. In developing countries on the other hand there is a compelling case for increased investment in public research, outreach and infrastructure.
Wreford et al. (2010) review the recently growing body of liter- ature on the costs of adaptation to climate change. They also note that many of the large-scale global cost estimates ‘‘mask the distri- butional impacts of adaptation and do not provide sufficient infor- mation for decision-making at a local or national level’’.
In summary, existing studies seem to be in general consensus that the economic impacts of climate change on agriculture are modest in the aggregate. However, the analysis so far has been strongly influenced by rather simplistic assumptions about future crop yields, and therefore do not provide a satisfying answer and are surrounded by much uncertainty. Most analytical frameworks also completely ignore the potentially very important impacts of increasing frequency of extreme events. Significant additional re- search will be required in many other areas, not least to recognize the location specific impacts of climate change at more disaggre- gated levels. Thus the impact of climate changes on the global dis- tribution of agriculture production, and therefore on trade patterns, remains uncertain.
Carbon balances: agriculture is part of the problem and solution to GHG emissions
In 2004 agriculture directly contributed about 14% of global anthropogenic greenhouse gas (GHG) emissions according to IPCC. Land use, land use change and forestry account for a further 17%, much of which is deforestation to convert into agricultural land. Thus globally agriculture contributes approximately one third of total anthropogenic emissions.
In addition to the emissions consequences of land use change, changes in land cover can also be important contributors to climate change and variability, particularly at the local level. There is a growing body of literature on the complex relationships between changes in land cover and local climate (Pielke, 2005; Stone, 2009). These links include the radiation (both solar and longwave) balance of the land surface, the exchange of sensible heat between the land surface and the atmosphere, and the roughness of the land surface and its uptake of momentum from the atmosphere.
In contrast to other sectors, agricultural activities not only pro- duce greenhouse gas emissions, but can also remove carbon from
the atmosphere. This is achieved through management practices that increase soil organic carbon, which according to Smith et al. (2007), accounts for most of the technical potential (89%). The eco- nomic potential will be lower; however agricultural mitigation op- tions are found to be cost competitive with a number of non- agricultural options in achieving long-term (i.e., 2100) climate objectives.
Given the significant potential contribution it will be important to develop an appropriate framework that provides incentives for these activities. A comprehensive framework for a full accounting of terrestrial carbon continues to be the subject of intense multilat- eral negotiations at the UNFCCC.
Linkages between climate change, agriculture and trade
Climate change affects the supply side of agriculture primarily through its impacts on productivity, yields, and the availability of arable land and water. These changes in technology and endow- ments in turn alter the returns to factors of production employed in agriculture and are driving the potential changes in patterns of geographical specialisation of production.
Climate change is expected to lead to important changes in the geographical distribution of agricultural production potential, with increases in mid- to high-latitudes and a decrease in low latitudes. This shift in production potential will have to coincide with higher trade flows of mid- to high-latitude products such as cereals and livestock to low latitudes. For example, Fischer et al. (2002) esti- mate that by 2080 cereal imports by developing countries would rise by 10–40%.
Policy actions that countries take to mitigate the effects of cli- mate change also have an impact on trade flows in agriculture. Golub et al. (2010) show that given higher emission intensities of livestock industries in many developing compared to developed countries and of ruminant meat compared to other livestock sec- tors, and given large differences in abatement costs across live- stock sectors and regions, a global carbon tax, together with a sequestration subsidy in forestry, would hurt livestock production in developing countries particularly strongly, resulting in increased net imports to Sub-Saharan Africa and reduced exports from South America. Such a scenario, however, may not be very likely, given food security considerations in developing countries. Sparing developing (non-Annex 1) countries from the carbon tax would re- duce those pressures, even though much of the reduced crop and livestock output in South America is found to be linked to reduced deforestation and increased re-forestation due to the sequestration subsidy.
The production and use of biofuels, such as ethanol and biodie- sel, are also supported by public policies in many countries, with the aim of mitigating climate change as one of the main arguments. Studies, including recent work by OECD (2008), have shown that the actual GHG savings from most biofuels along the whole life cy- cle are relatively small and come at high public costs. The implica- tions of biofuel policies for agricultural commodity and food prices are subject to intense debate. Cereal and oilseed prices are in- creased through the induced additional use of these crops in the ethanol and biodiesel chains, and the OECD (2008) estimates that international grain prices are increased by between 5% and 7% in the medium term (i.e. around 2015) through the policies in place in 2007 – the more recent changes in US and EU legislations are likely to further increase this effect.
Among the existing policies fostering northern hemisphere bio- fuel markets, tariffs on ethanol imports appear to be particularly problematic. Their removal would result in a more efficient supply of biofuels globally. At the same time, given the much superior en- ergy and GHG performance of cane-based ethanol relative to its
S12 H. Huang et al. / Food Policy 36 (2011) S9–S13
grain-based counterparts, reducing these trade barriers would also increase the positive impact of biofuels on climate change.
But international trade itself has direct and indirect influences on climate change (OECD, 1994, 2000). Trade directly impacts on emissions through the use of fossil fuels in transportation. More indirect impacts are stemming from trade as an important deter- minant of the distribution, structure and scale of the global pro- duction and its associated GHG emissions. But trade also influences the choice and development of technologies. It is impos- sible to determine beforehand the net impact on GHG emissions of all those factors combined.
This can be illustrated with the much publicized debate on ‘‘Food-Miles’’. While a priori, the expectation is that the further food is transported, the greater the GHG emissions, the total im- pact on emissions is crucially dependent on the method of trans- port (air, land, or sea). Moreover, the emissions from the production process also depend crucially on natural endowments (including weather) and technology. A complete life-cycle assess- ment, from extraction of raw materials, to production, processing and transport to the final consumer may result in a higher or lower carbon footprint for imported goods compared to the domestically produced equivalent. Thus the important message is that trade can have both positive and negative effects for climate change – the impact cannot be known a priori, and will depend on the exact way in which carbon content is measured and how much GHG emissions are taxed.
Trade will thus have a role to play in accommodating climate change-induced changes in production by enabling the flow of agricultural products from surplus to deficit, and from lower cost (inclusive of GHG emissions) to higher cost regions. Climate change mitigation policies in agriculture will have an impact on trade flows by influencing the relative production costs of different agricultural products. At the same time, the activity of trade itself has complex influences on emissions.
Trade is part of the solution to climate change challenges
Summarizing the state of knowledge around the turn of the mil- lennium, the IPCC AR4 report noted that the climate-induced shift in production potential is expected to result in higher trade flows of mid- to high-latitude products such as cereals and livestock to low latitudes where yields are expected to fall. Indeed, interna- tional trade is regarded as an important factor to compensate for falling yields in tropical and semi-tropical latitudes.
However, more recent work indicates that trade may only par- tially compensate for falling yields. Nelson et al. (2009) estimate that developing country imports of grain will increase substan- tially, but imports do not compensate fully for the reduced produc- tivity following from climate change. As a result, this work predicts increased malnutrition in developing countries.
Since GHG emissions are trans-boundary, and the resulting cli- mate change problems are truly global commons issues, policy coordination between countries is intrinsically difficult to achieve, especially in view of unequal distribution of costs and benefits of mitigating actions. The question then is whether trade policies would be an appropriate instrument to induce globally less GHG intensive production methods. Reductions in one country or region could be off-set by additional emissions elsewhere through ‘‘car- bon leakage’’. Because countries undertaking commitments to re- duce emissions put domestic industry at a cost disadvantage, proponents argue that a compensating carbon tariff on imports proportional to the carbon emitted in the manufacture of those goods could be applied. However the actual consequences of car- bon leakage may not be very significant. Recent research (Matoo et al., 2009; OECD, 2009, Winchester et al., 2010) finds that as
the coalition of countries acting domestically to reduce energy intensity increases, the leakage rate falls rapidly. The global eco- nomic welfare effects of border carbon adjustments are over- whelmingly found to be negative and the net effects on emissions are very small. Better targeted domestic policies to im- prove energy efficiency in non-coalition countries do a far better job.
Another set of trade policies relates to the use climate-change related standards, such as carbon footprint standards. There is much activity in many countries on development of private and government standards on energy efficiency in products such as cars and electrical appliances and carbon accounting and labelling on food items by supermarket chains. (OECD, 2010). Climate-re- lated standards are already in use on some agricultural products and biofuels and are likely to become increasingly important in the future (Hebebrand, 2009). In order not to act as trade barriers that discriminate between domestic and foreign products both the WTO Agreement on Technical Barriers to Trade (TBT) and the San- itary and Phytosanitary (SPS) Measure agreement encourage the use of relevant international standards in domestic regulation. A likely area of further debate is whether climate-related standards should only refer to the characteristics of the final product, or whether standards applicable to so-called non-product related production and processing methods’ are permissible under WTO rules. The latter would enable countries to control the imports of products based on their production methods, including on their al- leged ‘climate friendliness’. Clearly, the development and applica- tion of internationally more or less harmonised standards can have profound implications for trade flows and regional specialisa- tion of agricultural production, and hence international coordina- tion of what constitutes a climate-related standard and which body or bodies should be charged with international standards set- ting warrants close attention from policy makers.
Concluding remarks
Available research indicates that climate change is expected to lead to important changes in the geographical distribution of agri- cultural production potential, with increases in mid- to high-lati- tudes and a decrease in low latitudes. International trade plays an important role in compensating, albeit partially, for regional changes in productivity that are induced by climate change. To- gether with productivity changes, changes in endowments of ara- ble land and usable water, developments in energy markets, population growth and government policies, both existing agricul- tural policies and newly conceived climate-related policies, all drive the patterns of regional specilisation and of international trade. Considerable uncertainty surrounds the model-based long- term projections, particularly regarding the impacts of climate change on productivity of crops and livestock. This uncertainty stems both from still inconclusive scientific results on the future evolution of climate change, and from unknown developments in new technologies and techniques to adapt to changing climatic circumstances.
The ability to realize the compensating potential of interna- tional trade depends on a well-functioning international trade architecture. Imposing import restrictions, perhaps motivated by the desire to increase domestic production in the face of declining yields, and hence confounding food security with food self-suffi- ciency, is clearly not a sustainable solution. Likewise imposing ex- port restrictions in surplus regions, as witnessed during food price spikes in 2007/2008 and motivated by the objective to keep domestic prices low relative to world prices, creates problems for food importing countries and undermines the trust in the function- ing of the global trade system. Concluding the Doha Development
H. Huang et al. / Food Policy 36 (2011) S9–S13 S13
Agenda round of trade negotiations as soon as possible would make a very important contribution to tightening international disciplines on trade policy measures and to improving the stability of the trade system. Welfare gains from reforms to trade policies may be greater than normally measured if they also reduce GHG emissions globally.
There is general agreement on the need to internalize the exter- nality of GHG emissions through a carbon pricing mechanism, but there is little consensus on how to achieve this at a global level as evidenced by the recent failure to reach a multilateral agreement at the UNFCCC Negotiations in Copenhagen. Thus we are in a world of second best solutions (or third, fourth, etc.). However, direct bor- der policies, such as import tariffs based on the carbon content of the imported product are found to be ineffective instruments to re- duce GHG emissions. Besides, there is no consensus on how to cal- culate the direct and indirect carbon content, and internationally agreed methods are lacking. Indirect trade policies stemming from behind-the-border measures, such as climate-related standards, have a bearing on trade flows, but neither are all their impacts fully understood nor is there a clearly defined international coordina- tion mechanism to set and enforce international standards.
While a well-functioning international trade system can (and is indeed crucial to) support the adaptation to climate change-related challenges trade policies in isolation are imperfect instruments to induce less emissions globally.
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- Climate change and trade in agriculture
- Driving forces in agricultural markets: where does climate change come in?
- Climate change and impact on agriculture
- Carbon balances: agriculture is part of the problem and solution to GHG emissions
- Linkages between climate change, agriculture and trade
- Trade is part of the solution to climate change challenges
- Concluding remarks
- References
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Climate change, agricultural trade and
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Climate change,
agricultural trade and
global food security
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Thomas W. Hertel
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iii
Contents
Acknowledgements iv
Acronyms v
Abstract vi
I. Climate change impacts on agriculture 1
Background 1
Methods for assessing climate impacts on crops 2
Agricultural adaptation to climate change 4
II. Consequences of climate change for commodity markets, trade, food security and aggregate welfare
– an overview of the models 6
Incorporating climate impacts into a trade model 6
Evaluating global economic models of agriculture and climate 8
III. Climate mitigation, international trade and food security 16
IV. Policy implications related to trade as a tool for adaptation 19
References 22
iv
Acknowledgements
The author thanks Christophe Gouel for his valuable comments.
v
Acronyms
AEZs Agroecological Zones AgMIP Agricultural Modeling and Intercomparison Project ARM Armington models CGE Computable General Equilibrium CDS Constinot, Donaldson and Smith GE General equilibrium GHG Greenhouse gases GTAP Global Trade Analysis Project HO Homogenous product models IFPRI International Food Policy Research Institute IPCC Intergovernmental Panel on Climate Change PE Partial equilibrium SS Self-sufficiency WTO World Trade Organization
vi
Abstract
Climate is an essential input to agricultural production. Changes in climate will inevitably
have an impact on agricultural productivity, output, farm incomes and prices. Elevated
temperatures will also affect human and animal health.
To date, most of the studies of climate impacts on agriculture have ignored the impacts
on humans and livestock, focusing instead on the consequences for crop production. One
of the foremost reasons is that research on crop impacts assessment is the field where
the necessary modelling infrastructure was most fully developed.
The present paper provides an overview of the latest modelling research on the impact
of climate change and agriculture. It specifically focuses on different modelling
approaches that include the interlinkages between climate change and trade, and the
potential role trade can play to support adaptation and mitigation to climate change.
1
I. Climate change impacts on agriculture
Background
Climate is an essential input to agricultural production and changes in climate will
inevitably have an impact on agricultural productivity, output, farm incomes and prices.
Historically, most studies of agricultural productivity ignored climate – implicitly
assuming it is unchanging -- focusing instead on changes in productivity at a given
location over time due to improved knowledge, new varieties of crops and livestock, as
well as improved farming practices. However, as the Intergovernmental Panel on Climate
Change (IPCC) has noted in its last assessment report, the impacts of a warming planet
are now becoming detectable and these impacts are expected to accelerate in the coming
decades (IPCC, 2013). Understanding these historical impacts as well as the potential
future impacts of climate change on agriculture has become an entire field of science in
its own right. The emergence of the Agricultural Modeling and Intercomparison Project
(AgMIP) has lent structure and direction to this effort at assessing the impact of changes
in climate on global agriculture.
There are many ways in which climate affects agriculture. Perhaps the most obvious is
the impact of elevated temperatures on those people working outside and exposed to the
sun. Kjellstrom et al. (2009) estimate that, under a high warming scenario, labour work
capacity in agriculture will fall by 11-27 percent across Southeast Asia, Central America
and the Caribbean. While many farmers in the wealthier regions can avoid heat stress by
working inside air conditioned equipment, there remain many field tasks which are not
yet mechanized. In short, this is likely to be a significant source of cost increases as well
as threats to human health – particularly in those parts of the world where poverty and
food insecurity predominate.
Just as humans suffer from high temperatures, so do livestock. While there is limited
evidence of these effects at broad scale, experiments as well as observational data suggest
that a warming planet will have negative effects on feed intake, rates of gain, dairy
production disease and parasites as well as mortality rates. In addition, by altering the
growth rate of pastures, climate change will have an indirect effect on ruminant and dairy
productivity.
Climate change will also have important impacts on crop growth (see Table 1 in Hertel
and Lobell (2014) for a summary). Higher temperatures tend to lead to faster crop
development, a shortened grain-filling stage and reduced yields. Elevated temperatures
also affect net carbon uptake and contribute to higher vapour pressure deficits leading to
water stress. This is counteracted to some degree through increased stomatal
conductance, owing to elevated carbon dioxide (CO2) concentrations, which leads to
improved water used efficiency and increased optimum temperatures for C3 plants.
However high temperatures can damage plant cells, and extreme heat during the
flowering stage increases sterility rates. On top of this, invasive weeds tend to be better
adapted to a changing climate, with short juvenile periods, long distance seed dispersal
and greater response to elevated CO2 concentrations. In short, there are several different
avenues through which climate change can affect crop productivity – many of them
2
negatively – with the adverse impacts growing more dominant as temperatures rise
(IPCC, 2014).
To date, most of the studies of climate impacts on agriculture have ignored the impacts
on humans and livestock, focusing instead on the consequences for crop production.
There are a variety of reasons for this. However, it seems one of the foremost reasons is
simply that crop impacts assessment is where the necessary modelling infrastructure
was most fully developed. Nonetheless, it should be noted that most of the crop modelling
tools being used to assess climate impacts on agriculture were originally developed for
different purposes and largely in the context of major field crops produced in the
temperate (high income) regions of the world (White, Hoogenboom and Hunt, 2005). For
this reason, there are (e.g.) many studies on maize production in the Midwestern of the
United States of America, but relatively few of a crop like cassava in West Africa. This has
further hampered our ability to assess the full effect of climate change on global
agriculture. Even when a well-established model of maize crop growth is applied to the
analysis of global warming impacts on African agriculture, it is likely to miss many
important factors which are critical to climate impacts in the tropics, but which were not
deemed significant when the model was developed for managerial purposes in the United
States of America or Western Europe.
Methods for assessing climate impacts on crops
In their comprehensive (although now somewhat dated) review of crop models used for
climate change analyses, White et al. (2011) find that, of the 221 studies using 70 different
crop models to evaluate climate impacts, only six studies considered the effect of elevated
CO2 on canopy temperature and only a handful considered the direct effect of elevated
temperatures on seed set or leaf senescence. Another key point is that any single crop
model only includes a subset of the relevant processes. This may lead models to omit key
interaction effects. In addition, the omitted processes are thought to become more
damaging with climate change, so productivity may be upward biased as a result (Hertel
and Lobell, 2014). Finally, and perhaps most important, is the fact that the types of
climate impact pathways omitted by most crop growth models tend to be more important
in the tropics. These include: pressures from pest and disease, the impact of heat stress
on grain set and leaf senescence, and the impacts of high vapour pressure deficits on
photosynthesis (Hertel and Lobell, 2014). So the estimates of crop productivity are likely
to be upward biased in those regions of the world where food security is of greatest
concern.
An alternative, lower cost means of assessing climate impacts is via statistical analysis.
David Lobell and Wolfram Schlenker, among others, have been strong proponents of this
approach. The work by Schlenker and Roberts (2009) identifying critical temperature
thresholds for major field crops in the US stands as one of the most important findings to
emerge from the statistical climate impacts literature. Until recently there was a common
perception that the statistical models generated quite different – and possibly much more
pessimistic – predictions in the context of climate change. However, a recent special issue
of Environmental Research Letters has put this perception to rest. The lead article
provides a good overview of the findings. It is authored by David Lobell and Senthold
3
Asseng (2017). Asseng is a leader in the crop modelling community and therefore a
perfect complement to Lobell in this comparison of statistical and modelling approaches.
They show that, when the studies refer to the same geographic location are equally
carefully done, and control for the same variables, the results are remarkably similar
across statistical and process models in terms of the yield impacts of moderately elevated
temperatures on crop productivity (up to 2 degrees Celsius (⁰C) global average warming).
And the responses to precipitation also seem broadly consistent, although this is more
difficult to evaluate. The main difference between the two approaches resides in the fact
that most statistical models neglect the response of crop growth to elevated CO2
concentrations. This stems from the fact that, unlike temperature and precipitation, there
is little spatial variation in atmospheric CO2 and the temporal variation is slow-moving
and correlated with many other variables. This is why state-of-the-art work in the crop
impacts area now has moved in the direction of blending insights from process models
with the more cost effective statistical approaches (Lobell and Asseng, 2017).
In the second paper published in this special issue of ERL, Moore, Baldos and Hertel
(2017), provide a formal, statistical meta-analysis of more than 1 000 impact estimates
delivered to the IPCC under AR5. Their findings reinforce the intuition and case study
comparisons offered by Lobell and Asseng (2017). In particular, they fail to reject the
hypothesis that the meta-impact function is the same across statistical and process model
estimates of temperature impacts. Figure 1 maps the yield impact results from Moore et
al. (2017) for the four major field crops at 2 ⁰C of global warming based on pattern-scaled
temperature and including CO2 effects which vary between C3 and C4 crops. Note that,
while maize and wheat show losses in most regions, rice shows gains in high latitudes
and at higher elevations. The gains in soy yields in the Northern latitudes should be
discounted as these projections are solely based on temperature, precipitation and CO2 –
ignoring soils and other factors. Of far greater importance are the large losses in soy
yields in the tropics –most importantly in Central Brazil (Mato Grosso). This meta-
analysis also provides confidence intervals on the climate impacts, which can be used in
characterizing uncertainty in climate impacts. This is important, since it is generally
found that crop models are a greater source of uncertainty than the climate models
feeding into them (Rosenzweig et al. 2014).
4
Figure 1: Global gridded yield shocks for maize, wheat, rice and soybeans at 2°C
warming
Source: based on Moore et al. (2017).
Agricultural adaptation to climate change
In addition to comparing climate impacts from statistical and simulation models, Moore,
Baldos and Hertel (2017) formally test the ‘Adaptation Illusion Hypothesis’ first posed by
David Lobell several years earlier (Lobell, 2014). Based on his work as a Lead Author for
the climate impacts section of the IPCC report (2014), he postulated that most of the so-
called ‘adaptation’ identified by the process modellers was in fact not really climate
adaptation at all, but rather simply beneficial farming practices which serve to boost
yields equally under both current and future climate. Moore et al. (2014) are able to test
this hypothesis by including an adaptation indicator variable in the meta-analysis. It
appears in two places. The first is a pure shift effect (added to the intercept) thereby
picking up adaptation ‘illusions’ which are independent of climate. The second interacts
with temperature and picks up evidence of true climate adaptation under which (e.g.)
adverse impacts of elevated temperature are moderated by adaptation. As predicted by
Lobell, the pure shift effect is statistically significant, while the term capturing true
climate adaptation is statistically insignificant. This does not mean that farm level
adaptation is not important under a changing climate. It simply suggests that the types of
adaptation included by crop modellers are not really climate adaptation. They are simply
good practices which would be equally beneficial if implemented under current climate.
Including these beneficial effects in a climate impact analysis is likely to be misleading,
since adoption of these practices is likely constrained by other factors (e.g., access to
credit). This is important when it comes to evaluating studies of climate impacts which
rely on crop models and include adaptation components.
5
The foregoing confirmation of Lobell’s adaptation illusion hypothesis in the context of
agronomic models of crop growth notwithstanding, we do expect farmers to engage in
very significant adaptation to climate change in the future. Antle and Capalbo (2010)
identify three different types of adaptation to climate change. The first is adaptation
based on current technology. For example, in the face of elevated CO2 concentrations,
farmers may choose to apply more fertilizer to relieve potential nutrient constraints.
They may also employ more machinery and labour to deal with the increase in weed
infestations. And they are very likely to increase irrigation rates in the face of higher
temperatures. Indeed, in regions where irrigation is already being undertaken, those
farms not employing irrigation are likely to consider investing in this technology. Across
the literature, irrigation has been found to be one of the most effective tools for
adaptation to a warming climate, as it both contributes to cooling the plants as well as
overcoming water stress, which poses a major challenge to crop productivity at elevated
temperatures. Schlenker and Roberts (2009) show that, for maize production in the
United States of America, irrigation allows farmers to largely avoid the adverse impacts
of temperature extremes. The desirability of irrigation as adaptation to a warming
climate poses a significant sustainability challenge in a world of increasing water scarcity,
and should be a part of all future studies of climate impacts on agriculture, as will be
further discussed below.
The second broad avenue for adaptation in the face of climate change, identified by Antle
and Capalbo (2010), is the development and dissemination of new technologies. This
typically involves a mix of public and private investment and therefore requires a longer
lead-time. It also likely entails irreversibilities such that investors may be reluctant to
pursue these investments until some of the climate change uncertainties are resolved.
Considerable work is already underway in both the public and private sectors to develop
new crop varieties that are resilient in the face of drought and extreme heat. Less obvious
is the need for greater cold tolerance in crops in order to facilitate a more rapid migration
of crops to higher latitudes and cooler locations. Earlier sowing of seeds can also be
beneficial to avoid extreme heat during the critical flowering stage. And improved pest
resistance will be important under climate change. However, the time lag in development
of new technologies can be quite long (Alston et al. 2010) and these typically require local
adaptation. This is a major stumbling block in the poorest countries of the world, which
are often the most vulnerable to climate change as well. Hertel and Lobell (2014) argue
that this is one reason why many climate impact models likely overstate the potential for
adaptation in the poorest parts of the world. In addition, farmers in the poorest countries
often do not have access to credit – a critical determinant of adoption of new technologies.
Models of climate impacts need to consider both the potential for the development of new
technologies, as well as the barriers to their adoption throughout much of the developing
world.
The final avenue for adaptation involves changes in governance and institutions. This is
an area in which there is ample evidence of both positive and negative adaptation
(sometimes termed maladaptation) (Hertel and Lobell, 2014). Free trade is one oft-
touted avenue for adaptation, and this will be discussed at length later in this report.
Subsidies for agriculture are another important governance variable affecting the farm
6
sector. In the United States of America, the shifting of most government payments to crop
insurance subsidies is having an adverse effect on climate adaptation as it is discouraging
investment in irrigation as well as encouraging production of more weather sensitive
crops in risky locations (Müller, Johnson, and Kreuer, 2017). These types of adaptation
and maladaptation will be important to take into account in modelling exercises –
particularly as they affect the degree of market integration. As will be shown below,
market integration can have a significant impact on the expected consequences of climate
change for food security.
II. Consequences of climate change for commodity markets, trade, food security and aggregate welfare – an overview of the models
Incorporating climate impacts into a trade model
There is now a robust and growing literature seeking to estimate the impacts of climate
change on commodity markets, trade and food security. The first question which must be
addressed in any such study is how to translate the productivity shocks emerging from
the statistical and/or biophysical models discussed above into a form which can be
entered into the global economic models. There are basically three approaches which
have been used in the literature (Hertel, Baldos and van der Mensbrugghe, 2016). The
first is an ad hoc approach favoured by reduced-form, partial equilibrium commodity
models such as International Food Policy Research Institute’s (IFPRI) IMPACT model. It
treats the climate-induced productivity shock as a parallel shift in the supply function,
which we denote here as shock to yields, or, in terms more familiar to modellers using
Computable General Equilibrium (CGE) models, an exogenous shift in the derived
demand for land by the crops sector: D
L . When this shift is positive, there is an
improvement in productivity, yields rise, and the derived demand for land at current
output levels falls. The first column of Table 1 reports analytical expressions for the
resulting change in crop output and price in the case where nonland inputs are available
in perfectly elastic supply (Hertel, Baldos and van der Mensbrugghe, 2016). These
equilibrium output and price changes logically depend on the supply and demand
elasticities in the model – a point to which we will return momentarily. For the time
being, note the important role played by the total elasticity in the commodity market in
question: , ,S I S E D
, where ,S I
is the intensive margin of supply response, ,S E
is the
extensive margin of supply response, and D
is the absolute value of the farm-gate price
elasticity of demand for the commodity in question. The total elasticity appears in the
denominators throughout the expressions listed in Table 1. The less responsive is the
model to price changes – both on the supply and demand sides, the larger the price
adjustment required to restore equilibrium after a given climate change shock.
7
Table 1. Impacts of climate change shocks on equilibrium output and price changes
Variable Supply shift Land-augmenting technical change
Hicks-neutral technical change
Output
, ,
D D
L
S I S E D
,
, ,
( 1) D S E
L L
S I S E D
a
, ,
, ,
( 1) D S E S I
O
S I S E D
a
Price
, ,
D
L
S I S E D
,
, ,
( 1) S E
L L
S I S E D
a
, ,
, ,
( 1) S E S I
O
S I S E D
a
Source: Hertel, Baldos and van der Mensbrugghe (2016).
The second column of Table 1 reports the changes in output and price which arise when
climate change is introduced as a type of land-augmenting (or, more likely, dis-
augmenting) technical change in the agricultural production function. This approach is
preferred by many of the CGE modellers in their analyses of climate change impacts on
agriculture (Robinson et al., 2014). In this case, climate change is treated as a form of
biased technical change in the context of an explicit production function, with the shock
reported in Table 1 as 0La for a positive (land-augmenting) technical change and
negative for adverse climate impacts. This approach assumes that the climate change does
not have an impact on the productivity of nonland inputs. Based on a comparison with the
supply shift model in the prior column of the table, it is clear that this approach will give
rise to larger changes in output, and hence larger price changes, for a given yield shock.
The difference between these two arises from the fact that, in the explicit production
function approach, technical change not only affects the derived demand for land, but also
the profitability of farming. This is why there is an additional term, related to the
extensive margin of commodity supply, in the numerator. Assuming the price elasticities
are the same between two models, we expect to see a larger output and price response in
the CGE model. Of course these elasticities are not the same across models, as we will see
below (Table 3), further complicating such inter-model comparisons.
The third methodology for translating climate driven productivity changes into an
equilibrium model is shown in the final column of Table 1. It is also an explicit shock to
the production function for crop output. However, in this case, the climate shock is
viewed as a Hicks-neutral technical change. Here, the idea is that, if the farmer does
everything the way they did under the historical climate (no adaptation at this point – so
all input levels are the same as before the shock), but climate change reduces yield by ten
percent, then Oa = -10% and ten percent more of all inputs – including land -- are required
to restore the original output level in the absence of adaptation. This is the approach
taken by Hertel, Burke and Lobell (2010), Diffenbaugh et al. (2012), Costinot, Donaldson
and Smith (2016) and Moore et al. (2017). As can be seen from a comparison of the
entries in the second and third columns of Table 1, this makes a big difference in the
model outcomes. Since all factors are impacted, there is a much larger change in
profitability and a larger output response in the case of the Hicks-neutral treatment,
provided the model elasticities are the same across the two approaches.
8
Which of these approaches to administering a climate shocks is preferred? To my
knowledge this issue has not been formally explored. A natural test would be to see which
approach gives the best fit to historical output and price variability, given observed yield
shocks. Of course, the answer will be conditional on the supply and demand elasticities
in the model. On this point, Diffenbaugh et al. (2012) found the Hicks-neutral approach,
which they incorporated into a short run CGE model of year-on-year corn yield shocks in
the United States of America, to give a level of annual corn price variability which was
broadly consistent with that observed in the United States of America over the period
from 1980-2000. The question of which approach to implementing climate shocks is most
appropriate remains a topic worthy of deeper exploration as work in this area proceeds.
Evaluating global economic models of agriculture and climate
As noted previously, AgMIP has provided an institutional framework for comparing
models of climate impacts on agriculture. While the bulk of that project’s efforts have
been focused on crop models, they have also assembled a diverse team of global economic
modellers for purposes of assessing the impacts of climate change on regional and global
production, trade and welfare. In the process of undertaking this model comparison, they
have generated some useful results which permit a deeper comparison of the models – in
particular their supply and demand elasticities. In addition, we consider other global
economic models which have been used to assess global climate impacts. The models
considered here are listed in Table 2 which divides them into partial equilibrium (PE), at
the top panel of Table 2, and general equilibrium (GE), at the bottom panel of Table 2.
The spatial dimensionality of these models is summarized in the third column of Table 2
and is categorized across both the demand and the supply sides of the model. All of the
GE models (excepting CDS – which is effectively a PE model) rely on some aggregation of
the Global Trade Analysis Project (GTAP) database (see Aguiar et al., 2016) that may
include large countries individually, but typically collapse global activity to between 20
and 30 regions. Using GTAP’s supplemental Agro-Ecological Zones database (see
Monfreda et al., 2009), production within a region can be distinguished across up to 18
Agroecological Zones (AEZs) and this is the case in a number of the GE models. The PE
models specify demand at either an aggregate regional or country level. Supply, on the
other hand, varies from the grid-cell level (MAgPIE, GLOBIOM, CDS, GL), to sub-regional
(that may be defined by AEZ or water basin), to national. For example, IFPRI’s IMPACT
model has a country resolution for demand (and trade), but sub-regional Food
Production Units (which tend to follow major river basins) for production.
9
Table 2: Overview of the models
Model References: GCAM: Wise and Calvin (2011); GLOBIOM: Valin et al. (2013); IMPACT: Robinson et al. (2015); MAgPIE: Lotze-
Campen et al. (2008); GAPS: Kavallari et al. (2016); CDS: Costinot, Donaldson and Smith (2016); GL: Gouel and Laborde (2017); AIM:
Fujimori et al. (2012); ENVISAGE: van der Mensbrugghe (2008); EPPA: Chen et al. (2015); FARM: Sands et al. (2014); GTEM: Pant
(2007); MAGNET: Woltjer and Kuiper (2014).
The global drivers of these models are reported in the next column of Table 2. These are
not the focal point of this review, but are relevant in driving the underlying economy
forward in the context of climate change. Suffice it to note that: (a) population is
exogenous in all of these economic models, and (b) GDP is exogenous in the PE models.
In some notable cases (GCAM), food consumption is specified exogenously, based on the
idea of eventual convergence of caloric consumption. This means that food demand is
unresponsive to the economic forces which may vary across scenarios. Empirical
evidence suggests that both the price- and income-responsiveness of consumers’ demand
for food becomes smaller in absolute value as households become wealthier (Muhammad
et al. 2011) Some of the models in Table 2 seek to take this into account through a series
of ad hoc parameter adjustments over the course of their simulation.
Of course it is not just final demand that is potentially responsive to prices. Intermediate
demands by the livestock and food processing sectors are also potentially quite
important. All of the GTAP-based GE models have both of these channels for determining
aggregate agricultural demand (but not CDS and GL). None of the PE models have food
manufacturing sectors; a few incorporate the livestock sector and price sensitive feed
demand (e.g., GLOBIOM, GAPS, IMPACT). Biofuel demand is included in most of the
models as a long run driver. In the case of the partial equilibrium models, this source of
demand is typically exogenously specified, whereas in the general equilibrium models
this may be related to the price of oil, as well as to government mandates which may, or
may not be binding, depending on the oil price scenario (e.g., MAGNET). When these other
sources of demand are also price responsive, we expect a larger farm level price elasticity
of demand and a more muted market price responses to supply side shocks – particularly
when the biofuel mandates are not binding.
10
The next set of columns of model characteristics identified in Table 2 are those associated
with the price responsiveness of crop supply. This depends critically on the scope for
endogenous intensification in response to scarcity (or the reverse in the case of crop
surplus). In most cases, this intensification is viewed simply as increased application of
variable inputs per hectare. However, in the case of the MAgPIE model, land scarcity
engenders increased investment in agricultural R&D which, in the longer run, can
generate higher yields (Dietrich et al. 2014). As shown in Table 2, several of the models
do not allow for endogenous intensification (GCAM, IMPACT, GAPS, CDS and the baseline
version of GL) – although some models allow for the choice between alternative fixed-
proportion technologies thus exhibiting some substitution in the aggregate factor
proportions (e.g., GCAM). These fixed proportions models tend to favour land conversion
as an avenue for responding to scarcity, such as that induced by adverse climate change,
in global food markets.
Virtually all of the models in Table 2 rely on endogenous land supplies as a key factor in
equilibrating long run supply with growing demands. However, as we will see below, the
magnitude of this component – the extensive margin of supply response -- varies greatly
across models. There is also a column in Table 2 relating to the role of non-land factor
supply response to the crops sector. This is a largely overlooked constraint on long run
crop output. Yet the supply of labour, capital, fertilizer and other non-land inputs to the
farm sector can play an important role in constraining crop output expansion in response
to food scarcity (Hertel, Baldos and van der Mensbrugghe, 2016). Nearly all of the PE
models ignore this element, thereby overstating the importance of land (and possibly
water) as the sole constraining factors on the supply side. The fact that they explicitly
incorporate non-land factor supplies is a strength of the GE models – although the
empirical basis for these non-land input supply elasticities is quite limited.
As noted in our discussion of alternative methodologies for applying climate shocks to PE
and GE models, even when two models use the same approach to incorporating the very
same climate shocks, unless they employ the same supply and demand elasticities the results
will be different. This raises the question: what are the values of these elasticities in the
models currently being used for global economic analysis of climate change? And how
might these influence the outcomes predicted by these models? Hertel, Baldos and van
der Mensbrugghe (2016) took advantage of outputs generated by one of the AgMIP
economic model intercomparison exercises undertaken recently and used a set of
expressions like those in Table 1 in order to ‘back out’ the global elasticities from nine of
the global economic models most widely used to analyse climate impacts in agriculture
(these were the participants in the AgMIP economic model comparison exercise). Table
3 reports these elasticities for a variety of models. In the case of the AgMIP models, these
elasticities pertain to a composite of the five major field crops (which they term CR5), at
global scale (see footnote in Table 3). For purposes of comparison, the final row reports
the global elasticities from the more aggregated (all crops combined) SIMPLE model
which has been validated against historical data for the aggregate crops sector over the
period 1961-2006 (Hertel and Baldos 2016; Baldos and Hertel, 2013). This long run
validation makes it an appropriate point of comparison for the more disaggregated
11
models which have not been compared against a multi-decadal historical record
comparable in length to the projections period.
Table 3. Demand and supply elasticities for global economic models a
Model Total Demand Extensive Intensive
Partial equilibrium models
IMPACT 0.58 0.24 0.37 -0.03
GCAM 2.80 0.63 2.52 -0.36
GLOBIOM 0.49 0.28 0.08 0.13
MAgPIE 0.36 0 0.18 0.18
General equilibrium models
CDS 2.46 1.00 1.45 0.01
GL 0.53 0.20 0.33 0.00
AIM 0.85 0.10 0.92 -0.17
ENVISAGE 3.22 0.47 1.57 1.18
FARMb 1.33 0.07 1.30 -0.04
GTEMb 0.96 0.07 0.52 0.36
MAGNET 0.93 -0.04 1.23 -0.26
SIMPLE 1.16 0.29 0.36 0.51 a Elasticities for IMPACT, GCAM, GLOBIOM, MAgPIE, AIM, ENVISAGE, FARM, GTEM and MAGNET are based on five major crops. See Hertel, Baldos, and van der Mensbrugghe 2016 for the method by which these arc elasticities are calculated, using a set of simultaneous equations. Elasticities for CDS apply to all crops combined and these marginal elasticities were obtained via simulation by Christope Gouel (personal communication). The demand elasticity for GL applies to all crops while the supply elasticity applies to maize and was obtained from Christophe Gouel (personal communication). Elasticities for SIMPLE are obtained via model simulations and apply to marginal changes.
Examination of the elasticities in Table 3 leads to a number of important conclusions.
Firstly, the aggregate response of these models to crop prices varies greatly. With the
exception of GCAM, which is a hybrid model designed as part of an Integrated Assessment
modelling system, the partial equilibrium models tend to have a much smaller total
elasticity than the general equilibrium ones. This point has been made previously by
Hertel (2011) who hypothesizes that these settings may reflect the evolution of these
agricultural commodity models from near term forecasting to long term projections
frameworks. The only way to obtain the kind of crop price volatility observed on an inter-
annual basis is to have a relatively low total price elasticity. This is obtained in the
commodity models by having small supply elasticities at the intensive margin – a point
consistent with short run analysis. By contrast, the CGE models are not used for year-on-
year forecasting, and price volatility is a lesser point of emphasis. Furthermore, the
supply elasticities are functions of deeper parameters (Robinson et al. 2014) which are
consistent with longer run, equilibrium assumptions. Thus we see in the CGE models
larger aggregate responses to scarcity, with the supply side of the market dominating the
overall price responsiveness.
Combining this information about the total elasticities (generally smaller in the PE
models), with the analytical expressions in Table 1 which show that, for equivalent
elasticities, the output and price responses will be smaller in the PE models due to their
methodology for introducing the climate impacts on yields, we find that we cannot reach
12
a definitive conclusion based on theory about which model will generate larger impacts.
However, with just a little additional information (the cost shares of land), we can make
some rough calculation to speculate about which models will tend to show more price
responsiveness to climate change.
In addition to determining the aggregate effects on price and output, the relative size of
the demand and supply elasticities in each model will play a key role in determining the
relative incidence of an adverse climate change shock. The smaller the share of the total
elasticity contributed by the farm-gate demand elasticity, the greater the share of the
burden which will be borne by consumers. Indeed, producers in many regions stand to
gain from the higher prices under such circumstances. The MAgPIE model is a case in
point. By design, demand is exogenously specified. This inelasticity of demand, coupled
with a small aggregate supply elasticity, yields very large price changes (recall the second
row of Table 1). This is evidenced in the MAgPIE paper authored by Stevanović et al.
(2016) which reports significant producer gains from climate change over the 21st
century, while consumers lose a great deal of consumer surplus. Introducing a larger role
for consumer response to higher prices, as in the IMPACT model (Table 3), will shift some
of the burden of climate change towards producers, as households reduce their food
consumption or shift away from the most heavily affected commodities. In this
dimension, along with MAgPIE, the CGE models reported in Table 3 appear to be
particularly oriented towards consumer-incidence of climate change shocks due to their
relatively small role for farm-level price elasticities in the overall demand elasticity, and
hence the relatively large supply elasticity. A major reason for these small farm gate
demand elasticities in the CGE models is the fact that very little of the crop commodity is
sold directly to consumers – a fact faithfully reflected in the underlying input-output
tables. Rather, crops must first pass through multiple processing activities, which tend to
mute the farm-level price responsiveness of final demand. Finally, note the extremely
large price elasticity of demand for food implied by the demand system used in CDS. This
results in some peculiar conclusions about the role of trade in climate adaptation which
will be discussed below.
There are also a number of counter-intuitive signs in Table 3 (i.e., negative entries in this
table – since the demand elasticities in Table 1 are defined as being positive as are the
supply elasticities). This is presumably due to compositional effects. For example, the
MAGNET model has very large land supply elasticities and relatively small intensification
elasticities, suggesting that the main response to adverse technological change (i.e., a
negative climate change impact) will be to bring in more cropland area. At this point,
given the focus on compositional effects, we need to bring in the final column of Table 3
which identifies the trade structure of the model. Given the trade specification in
MAGNET (segmented markets via the Armington assumption), if the adverse climate
shocks are largest in regions with relatively low yields, this is where the price rises will
be largest. If, in addition, these regions also have large land supply elasticities (e.g.,
Africa), then we expect strong expansion in low-yielding land areas. This would result in
a decline in global average yields for grains and oilseeds in MAGNET. This outcome is
observationally equivalent to a negative intensive margin when viewed at global scale
through our conceptual lens, which is why we see the negative entries in the final column
13
of Table 3. The AIM and FARM (also Armington models) also show negative intensive
margins at global scale. In the case of the two PE models which show a negative intensive
margin, IMPACT and GCAM – neither of which incorporate product differentiation, this
appears to be due to the absence altogether of intensification possibilities, combined with
a more muted compositional effect.
The question of how international trade is modelled is central to the impact of climate
change on food security – particularly when climate shocks vary widely across
countries/geographic regions. The final column of Table 2 reports on the trade structure
of the models reviewed here. HO denotes homogenous product models. SS refers to a self-
sufficiency specification where countries/regions are assumed to strive for a given level
of self-sufficiency which may evolve slowly over time. ARM denotes Armington and refers
to those models in which products are differentiated by country of origin, therefore
allowing for market segmentation and the divergence of prices for the same product (e.g.,
wheat) across markets (homogenous product models can have price divergences in the
presence of transport costs – e.g., GLOBIOM).
In their paper on globalization of the food system, Hertel and Baldos (2016) emphasize
the critical importance of the distinction between the HO and ARM specifications by
contrasting the impacts of a variety of different shocks on food and environmental
outcomes under the two types of models. In the case of the adverse (most extreme)
climate change scenario which they consider, nonfarm undernutrition rises by 45
percent, relative to the baseline year 2050 under segmented markets, but just 27 percent
under fully integrated markets. When trade is frictionless and there is a unified global
market, it is much easier for consumers in severely affected regions with the highest
undernourished headcount (South Asia and Africa) to access lower cost food from
abroad. Of course agricultural trade is not frictionless. Rather it is hampered by transport
costs – but more importantly by government interventions – both at the border and at
the consumer and producer levels. Given the need to constrain the HO models to avoid
specialization and overly dramatic changes in trade patterns -- which would fly in the face
of historical evidence -- many of the HO models find other ways to constrain trade. As
noted above, MAgPIE introduces a self-sufficiency criterion. GLOBIOM introduces trade
costs as well as increasing costs of adjustment for changing trade flows.
Which model more accurately reflects the evolving geography of world trade? Villoria
and Hertel (2009) formally test the integrated markets hypothesis using a model of global
cropland change and reject it in favour of the Armington specification. I believe that, in
the near term, the answer to this question is quite clear – the Armington model of product
differentiation fits the data much better, which is why virtually all of the empirical trade
models now employ product differentiation by country of origin (or by firm/country of
origin pairs, which is empirically isomorphic). However, over the very long run (decades
– or even a century) there is a legitimate concern about how persistent will be the
historical geography of trade in agricultural products. I believe it is fair to say that the
jury is still out on how best to model the evolution of agricultural trade patterns over the
very long run.
14
Given the tendency for the bilateral geography of agricultural trade to persist over time,
it is interesting to draw out the implications for the incidence of climate impacts. Moore
et al. (2017), begin to explore this issue, employing the meta-analysis of Moore et al.
(2017) underpinning Figure 1 to characterize the biophysical impacts of climate change
and insert these into the GTAP model of global trade to elicit the national welfare impacts.
Using the welfare decomposition tool of developed by Huff and Hertel (2001), the
resulting national impacts can be decomposed into three components: (a) the direct
(biophysical impact) contribution to welfare, (b) the terms of trade effect, (c) the
allocative efficiency effect and (d) the total national welfare effect (see Fig. 2). From
Figure 2a it is clear that South America is hard hit by the direct effects of climate change.
This derives from its heavy reliance on soybeans which are adversely affected by higher
temperatures – particularly in the tropics, where current growing season temperatures
are already high (recall Figure 1). However, exporters in this region (e.g., Brazil) are able
to shift some of this burden of climate change to other regions through higher export
prices. As a consequence, there is a substantial improvement in the terms of trade for
Brazil, Argentina and Paraguay (Figure 2b). On the other hand, China – a large importer
of soybeans from South America, experiences a strong terms of trade loss (Fig. 2b). In
addition to international adjustments to climate change, there is potentially significant
scope for intra-national adjustments. Constinot, Donaldson and Smith (2016) (CDS)
explore the latter in detail, using their globally gridded CGE model. Their model, which
does not include an opportunity cost for land expansion, and which represents food
demand as being price-elastic (recall Table 3), leads them to conclude that most of the
adjustment to climate change occurs within countries, by shifting production from land
less-well suited to a crop under the new climate to more suitable land. Constraining land
use change within a country results in significantly higher welfare losses from climate
change. On the other hand, freezing trade patterns does not have a large impact on the
resulting welfare losses, leading them to conclude that international trade does not play
a large role in adaptation to climate change.
Taking the CDS model as a starting point, Gouel and Laborde (2017) develop a new model
of global gridded production and trade in which there are explicit opportunity costs for
cropland expansion. This feature, coupled with an inelastic demand for agricultural
products – the authors argue this is more consistent with empirical evidence – leads to a
very different conclusion from CDS. In their model, international trade plays a crucial role
in the adjustment to climate change shocks in agriculture, with trade patterns changing
rather dramatically under their climate change scenario. These authors also show how,
as they increase the price elasticity of demand for food, the role for changing trade
patterns is diminished, since, in the face of more expensive food household reduce
consumption instead of importing more food. This underscores the important role for the
price elasticities of supply and demand – as was emphasized earlier in this survey – albeit
now in the context of trade.
15
Figure 2: Decomposition of national welfare changes at 2°C warming
Source: Moore et al.. 2017.
One final dimension of the climate change/trade modelling literature which is crucially
important, but which has not received sufficient attention, is the role of irrigated
agriculture and potential impacts on water scarcity (Rosegrant et al. 2013). As noted
above, we expect that irrigation will be an important adaptation response to a warming
climate. Yet many parts of the world where irrigation is prevalent are already water
scarce (Wada et al. 2010). And in many of these water scarce regions, fossil groundwater
is being mined. And, with agriculture accounting for 70 percent of water withdrawals,
worldwide, there will be little choice but to respond by restricting irrigation withdrawals
(in addition to investing in more efficient irrigation systems).
What will this mean for international trade and for climate change adaptation? Liu et al.
(2014) use a CGE model with rainfed and irrigated cropping disaggregated and both land
(AEZs) and water (river basins) broken out, in order to explore the implications of
projected water scarcity in 2030, in the absence of climate change, for food security and
land use. They find that international trade offers an important vehicle for adaptation to
a water scarce future. While significant local scarcity is projected in some regions –
particularly in South Asia and the Middle East – the impact on prices is relatively modest.
This is in large part due to the fact that water becomes less scarce in some regions, which,
in turn, boost net exports. If we add climate change to this picture, it is likely that the
mediating role for international trade will become even more pronounced, as many of the
regions projected to show scarcity in the absence of climate change (e.g., South Asia) are
also expected to be hard hit by climate change.
16
We can gain further insight into the potential interplay between water scarcity, irrigation
and adverse climate shocks from the paper by Taheripour et al. (2013). While their object
of investigation is the United States of America biofuels boom, the international market
effects of an increase in the excess demand for food production is not dissimilar from the
role of an adverse climate shock – albeit now a shock to the supply side of the market.
Those authors examined the land use and terrestrial carbon impacts of an increase in
biofuel demand – both in the absence and presence of constraints on irrigation expansion
in the most physically water scarce regions of the world. They find that the presence of
an irrigation constraint boosts overall land expansion – since irrigated yields are, on
average, higher than rainfed yields. In addition, the irrigation constraint has a dramatic
impact on terrestrial carbon emissions, since the rainfed areas also have higher levels of
above-ground carbon. By forcing more land expansion into more carbon-rich regions, the
irrigation constraint in the presence of a shock to global excess demand was shown to be
very significant. We might expect a similar result in the context of an adverse climate
scenario.
A word of caution is in order for those considering incorporation of irrigation and water
scarcity into models of climate impact on agriculture. Water scarcity is a highly localized
phenomenon and gridded projections of water scarcity at mid-century show
considerable variation across sub-basins within countries and even within river basins
(Liu et al. 2017). On average, there may be plenty of water, but the water may not be
where it is needed for climate adaptation, in a timely fashion. So addressing the irrigated
agriculture challenge is likely only appropriate in those models with considerable spatial
detail.
III. Climate mitigation, international trade and food security
In addition to facilitating adaptation to climate change impacts, international trade also
plays a key role in determining the impacts of policies aimed at climate change mitigation.
Indeed, Havlik et al. (2015) find that the near term impacts of land-based mitigation
efforts on food prices are likely to be significant. Thus any discussion of climate change
and global food security cannot ignore the mitigation side of the story. This section
discusses the potential impacts of land based mitigation on food security and poverty and
the role which international trade might play in distributing the associated costs of
mitigation actions.
First of all, it is important to highlight the disproportionate role which land-based
mitigation – largely in agriculture and forestry – can play in economically efficient, near-
term abatement of greenhouse gases (GHG) emissions. In a paper for the Copenhagen
Consensus, Brent Sohngen (2010) calculated, using the DICE model, how much the
optimal carbon tax would be reduced by incorporating forest sequestration as a
mitigation option in that framework which had hitherto largely focused on fossil fuel
abatement. The downward shift of the DICE model’s optimal tax path is dramatic and
amounts to roughly a 50 percent reduction in carbon tax in any given time period. This
finding is further underscored in a paper by Golub et al. (2009) who use a global CGE
model with both fossil fuel abatement, carbon sequestration and non-CO2 GHG abatement
17
possibilities to show that roughly half of the economically efficient near term abatement
should come from agriculture and forestry. Clearly including these land-based sectors in
the overall mitigation strategy will be beneficial from the point of view of global welfare.
However, such massive interventions into the land-based activities can be expected to
have significant consequences for food prices. This lead Hertel and Rosch (2010) to
conjecture that the near term impacts of climate mitigation on food prices and poverty
could be larger than the near term impacts of climate change itself. This conjecture is
borne out in the work of Havlik et al. (2015) who use the GLOBIOM model to examine the
impacts of a global carbon tax aimed at reducing Agriculture, Forestry and other Land
Use (AFOLU) emissions as part of a climate stabilization scenario (2 ⁰C). Results depend
heavily on the range of mitigation options available. In GLOBIOM these are dealt with as
discrete technologies. They find that this policy would result in lower agricultural
production in 2030, relative to baseline: a 4 percent decline for crops, a 5 percent decline
for meat and a 9 percent decline for milk. This, in turn results in higher world prices: a 4
percent increase for crops and a 7percent increase for livestock, but these could be much
higher in some regions, reaching a 22 percent increase in Sub-Saharan Africa. These
higher prices, in turn result in reduced consumption. Indeed, these impacts rival, and in
some cases exceed, the impacts of climate change over the same time horizon.
The potential adverse impacts of land-based mitigation on the poor is further elaborated
by Hussein et al. (2013) who use the GTAP-POV model in order to assess the poverty
impacts of a global carbon tax. They find that land-based mitigation policies can have
significant impacts on poverty, with the consequences depending on the earnings source
of the poor – they consider seven different types of poor households, distinguished by
agriculture/non-agriculture, land, labour, capital and transfer payments, earnings
sources. To the extent that the climate mitigation policy raises food prices, all types of
poor households suffer. However, the policy also boosts land returns and, in some cases,
can boost rural wages. This can benefit rural households. Unfortunately, most poor rural
households control relatively little land, and so the adverse effect of higher food prices
dominated. Overall, the authors find that a land based mitigation policy tends to boost
national poverty across the sample of countries which they studied. For this reason, these
authors, as well as Havlik et al. (2015) suggest some form of revenue recycling of the
carbon tax receipts to offset these adverse effects on the poor.
Of course, the impacts of mitigation policies on food prices depend very much on how the
policies are implemented. The simplest form of implementation from the economic
modelling point of view – and also the most economically efficient – is that of a global
carbon tax. However, the distributional consequences of such a tax – both across
countries and within them – are dramatic. Avetisyan et al. (2011) focus on the ruminant
livestock sectors in their analysis of a global carbon tax and find that this hits the poorest
countries in the world hardest – resulting in significant increases in food prices and
reductions in livestock output and earnings. This suggests that such a policy would be
politically untenable.
Henderson et al. (2017) also focus on the ruminant livestock sector – the most emissions
intensive agricultural sector – and explore a variety of policies aimed at reducing
18
emissions. They incorporate differences in emissions factors by livestock type and region,
and also include abatement cost curves that represent a summary of many different
mitigation alternatives as estimated by Henderson et al. (2017). They find that a global
carbon tax of USD 20/ton CO2e emissions could mitigate 626 metric megatons of CO2/year
through the adoption of new production practices and a restructuring of cattle
production, increasing the share of meat coming from the dairy sector, compared to the
more emissions intensive beef sector. However, they, too, deem such a policy politically
unlikely due to the adverse impacts on livestock output, incomes and prices in developing
countries. Therefore, they explore a revenue recycling policy which provides a subsidy to
producers aimed at helping them maintain profitability in the face of the carbon tax on
emissions. While this policy does maintain production levels, it greatly dilutes the original
objective of reducing emissions, with the abatement falling to just 185 metric megatons
of CO2/year. There is an unavoidable conflict between abatement and consumption. This
points to the importance of boosting income growth so households can afford the higher
food prices which result from a carbon tax.
These adverse impacts on developing country food security, from any global carbon
policy which includes land-based mitigation, suggest that it is unlikely that such a policy
will be adopted worldwide. Therefore, it makes sense to evaluate a policy which exempts
developing countries, or at least allows them to set their own, nationally determined
plans. This was indeed the spirit of the Paris Accord on climate mitigation. However, once
some regions are either exempted, or impose less restrictive standards, the issue of
‘leakage’ immediately arises. Will livestock production simply shift from the more
restrictive to the less restrictive regions? If this occurs, and if the less restrictive regions
also have much higher emissions intensities, then the mitigation effects of such a policy
could be greatly diluted. In short, the response of international trade to this policy regime
becomes much more important in this context.
Golub et al. (2012) find that such leakage due to international trade is indeed significant
when developing countries are omitted from the land-based mitigation policy. However,
they also find that much of this leakage can be eliminated if a global forest carbon
sequestration policy is put in place. This is due to the fact that restricting deforestation in
the tropics, and encouraging afforestation in some places, raises the cost of ruminant
livestock production throughout much of the tropics, thereby acting as a brake on
expansion of this industry when the industrialized economies apply a carbon tax to
agriculture.
19
IV. Policy implications related to trade as a tool for adaptation
International trade can play an important role in facilitating adaptation to climate
extremes and climate change. One of the most compelling examples of this potential role
comes from 19th Century India and is documented by Burgess and Donaldson (2010) who
studied the impact of variation in the annual monsoons on mortality in the Subcontinent
– either failure of the monsoon to arrive early enough for planting or excessive rainfall
and flooding. In the absence of infrastructure to transport large amounts of food to the
stricken regions, monsoonal variations resulted in considerable price and income
volatility as well as high levels of mortality. After the introduction of a railroad system,
the local impacts of such climate extremes were greatly moderated, illustrating the great
potential of market integration to facilitate adaption to the vagaries of climate and
extreme weather events.
Climate models currently predict an increasing likelihood of extreme events – both
temperature and precipitation – and these are expected to result in more frequent and
more severe supply-side shocks which can only be accommodated by reductions in
consumption, increases in costly stockholding, or increases in imports into, or reductions
in net exports from, the affected regions. In the context of such climate change, increased
market integration could have greater value to society. This point is illustrated in a paper
by Verma et al. (2014) who focus on the potential for market integration to lessen the
commodity market volatility resulting from increased year-on-year variability in maize
supplies in the United States of America under a mid-century climate. They characterize
supply-side volatility using high resolution climate model outputs in conjunction with a
non-linear climate impact function estimated by Schlenker and Roberts (2009). After
validating this approach on historical data, they use it to project supply side shocks under
mid-century climatology, considering two different types of market integration as
adaptation alternatives. The first involves international market integration, through
which global trade barriers in maize trade are removed. This results in a modest (8
percent) reduction in domestic maize price volatility in the United States of America,
relative to what would have existed under future climate in the presence of current tariffs.
Of course current trade policies are in fact endogenous, and can respond to market
conditions – particularly natural disasters and extreme climate events. This is the case,
even though World Trade Organization (WTO) disciplines impose some rules and binding
constraints on import barriers. The fact is that current tariffs are often well-below bound
WTO rates, leaving significant room for endogenous tariff adjustments – both upwards
and downwards -- in the face of changing market conditions. In addition, an agreement
to discipline export restrictions under the WTO remains elusive, thereby leaving room
for countries to respond to food crises by banning exports. This can generate panic and
knock-on effects as was seen in food crisis a decade ago. The problem of endogenous
policy responses in the face of changing market conditions has been highlighted by Kym
Anderson and Will Martin in the context of the 2006-2008 food price spikes (Martin and
Anderson 2012; Anderson and Nelgen 2012). They find that endogenous policy
responses (export taxes and downward adjustments in import tariffs) contributed
20
significantly to the rise in world commodity prices over this period. The estimated
contribution was largest for rice, where two-fifths of the world price rise is attributed
solely to policy responses – as opposed to changes in supply or demand conditions. For
wheat, the figure is one-fifth, while for maize it was just one-tenth.
These findings are particularly disturbing since, when fewer countries participate in the
adjustment to periodic regional or global production shortfalls, the remaining countries
are forced to absorb more of the adjustment. And typically it is the poorest countries that
are least able to insulate their domestic markets. Add to this, the fact that the price
elasticity of demand for crops is highest amongst the poorest elements of the population,
and we have a recipe for nutritional disaster, as the poorest households in the poorest
countries are forced to bear a disproportionate share of the burden of extreme climate
events and output volatility.
In their study of market integration as a vehicle for adaptation to climate change, Verma
et al. (2014) also examine the effect of closer inter-sectoral integration. Here, as opposed
to integration of maize markets across borders, the authors explore the effects of
integration between the agriculture and energy sectors. This has, in fact, been an
important feature of the agricultural economy in the United States of America over the
past decade. Higher energy prices, accompanied by biofuel mandates under the United
States of America Renewable Fuel Standard, have resulted in as much as 40 percent of
maize production in the United States of America going to ethanol – and ultimately being
consumed as a liquid fuel. The authors explore two different kinds of inter-sectoral
integration – one driven by higher energy prices (market-driven integration) and one
driven by ethanol mandates in the face of low energy prices (mandate-driven
integration). In their projections of the market-driven integration scenario, the mandate
is not binding under future climate, whereas under low energy prices it is binding. They
find a very large difference between commodity market price volatility under these two
different types of integration. Specifically, market-driven integration reduces maize price
volatility under future climate by about one-quarter. This stems from the fact that the
demand for ethanol is far more price-elastic than the demand for food. With ethanol
comprising just a small share of total liquid fuel demand, variation in crop supplies are
readily accommodated with modest price changes. This stands in marked contrast to the
situation under mandate-driven agriculture-energy integration wherein demand is
completely inelastic. In this case, maize price volatility under future climate by more than
half due to the presence of the ethanol mandate. In short, inter-sectoral integration can
serve as an important avenue for adaptation to greater supply-side volatility under future
climate, but only if this integration is market-driven.
In the long term, by fundamentally changing the pattern of comparative advantage in
global agriculture, climate change calls for a significant reconfiguration of international
trade and production patterns to reflect this new comparative advantage (Reilly et al.
2002; Tobey, Reilly and Kane, 1992). Regions which once tended to be self-sufficient or
net exporters may well become net importers of crops in the face of adverse climate
change, while some regions – particularly in the northern latitudes – may become more
competitive in a wider range of agricultural products. The more readily these shifts can
occur, the higher and more robust will be global welfare. These findings are evident in
21
recent research which seeks to explore the interplay between climate change and
international trade over the course of the 21st century. Stevanović et al. (2016) run the
MAgPIE model under a wide range of climate scenarios using a variety of biophysical crop
models, while considering two hypothetical trade regimes: FIX and LIB. The FIX regime
fixes the pattern of trade at 1995 levels, while the LIB regime allows for free and
unfettered trade in agriculture, worldwide. The authors find that introduction of the LIB
regime reduces global welfare losses (relative to impacts under the hypothetical FIX
regime) by about two-thirds. Expected aggregate agricultural welfare (factoring in both
consumer and producer surplus) is also benefited in most regions of the world. However,
the free trade scenario results in a significant redistribution of global agricultural welfare
between consumer and producer groups. Free trade benefits consumers in the most
adversely affected regions (tropical South) while hurting consumers in the temperate and
boreal North who must now compete in world markets for access to food. Producer
impacts are logically reversed, as farmers in the climate change-benefited North gain
greater access to markets in the South, while producers in the South face more intense
competition from climate-benefitted farmers in the North. Overall, the authors find that
world food prices are far lower under the LIB scenario.
The role of international trade as a vehicle for adaptation to climate change in the long
run is also explored in depth by Baldos and Hertel (2015) who focus specifically on the
impact on undernourishment at mid-century. They use the SIMPLE model of global crop
supply and demand and contrast the impacts under optimistic and pessimistic climate
impact scenarios and two trade regimes (currently segmented markets vs. fully
integrated world markets – where the latter is analogous to the LIB scenario discussed
above, but the former allows for currently observed responsiveness of trade flows). They
focus on their worst-case climate scenario which involves climate predictions from the
HADGEM global circulation model, crop impacts from the LPJmL crop growth model, and
which ignores potential crop growth gains from elevated CO2 concentrations. Under this
extreme scenario, they estimate that global undernutrition in 2050 (largely in South Asia
and Sub-Saharan Africa) could rise by nearly 55 percent, relative to their 2050 baseline.
However, this increase would be considerably moderated (by about one-third) under
integrated markets. Overall, the authors find that deeper integration in international
trade offers an excellent vehicle for shielding against worst case climate scenarios by
giving consumers improved access to world markets. The global trading system is a
public good which will only become more valuable in the future. Free and unfettered
access to global food supplies must be ensured in the face of the great uncertainty around
future climate change and its impacts on agricultural production.
22
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The Journal of North African Studies
ISSN: 1362-9387 (Print) 1743-9345 (Online) Journal homepage: https://www.tandfonline.com/loi/fnas20
Agriculture, trade, and climate change adaptation: a global CGE analysis for Morocco and Turkey
Ismail Ouraich, Hasan Dudu, Wallace E. Tyner & Erol H. Cakmak
To cite this article: Ismail Ouraich, Hasan Dudu, Wallace E. Tyner & Erol H. Cakmak (2019) Agriculture, trade, and climate change adaptation: a global CGE analysis for Morocco and Turkey, The Journal of North African Studies, 24:6, 961-991, DOI: 10.1080/13629387.2018.1463847
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© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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Agriculture, trade, and climate change adaptation: a global CGE analysis for Morocco and Turkey Ismail Ouraich a, Hasan Dudub, Wallace E. Tynerc and Erol H. Cakmak d
aTillväxtanalys, Österdund, Sweden; bEuropean Commission Joint Research Center, Seville, Spain; cDepartment of Agricultural Economics, Purdue University, West Lafayette, IN, USA; dDepartment of Economics, TED University, Ankara, Turkey
ABSTRACT The extent to which agricultural trade liberalisation can be an adaptation strategy in the face of climate change remains to be an open discussion in the literature. We set out to answer this question in the context of Morocco and Turkey by taking into account the impact of climate change on agricultural international markets at the global level. We use the GTAP model, combined with a newly developed global database on climate change impacts on agricultural crop sectors by 2050 as captured by yield projections. Results suggest that the more trade is liberalised, the higher global welfare gains are. However, the gains are not large enough to offset the loss from climate change impacts on agricultural productivity globally. In Morocco, agricultural trade liberalisation, on average, induces additional welfare losses. The main drivers are the deterioration in the terms of trade that offsets all the potential gains from the better allocation of economic resources due to free trade. For Turkey, trade liberalisation induces net welfare gains under all scenarios. The larger the tariff elimination scheme, the larger the net gains due to the more efficient allocation of economic resources, which partially offset the impact of declining terms of trade.
KEYWORDS Climate change; adaptation; agriculture; trade liberalisation; CGE model
JEL CODES D58; F18; Q17
1. Introduction
Scientists from various disciplines have devoted a significant effort from various disciplines to shed light on the causes and effects of climate change in recent years (Tol 2010). Although there is still controversy about the details (Idso and Singer 2009), it is widely accepted that climate change has already started to occur, and the impacts will increase through the
© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDer- ivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distri- bution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Ismail Ouraich ismail.ouraich@tillvaxtanalys.se, ismail.ouraich@ltu.se Tillväxtanalys (Growth Analysis), Studentplan 3, 831 40 Östersund, Sweden
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twenty-first century (De Bruin, Dellink, and Agrawala 2009; OECD 2008; Parry et al., 2007; Stern 2007). There are a wide range of social and physical effects that are linked to climate change in the literature, and the most significant effects are expected to be increasing temperatures accompanied by declining precipitation plus increasing frequency of climatic extremes. Hence, agricul- tural production, which is among the most climate dependent economic activi- ties, is likely to be the most vulnerable sector (Rosegrant et al. 2008). Various methods are employed to translate the physical effects of climate change to economic shocks. The most common way is introducing climate change shocks through the agricultural sector as yield or water requirement shocks.
Climate change is a global phenomenon with specific implications for different countries. Hence, in the literature, some studies use global Comput- able General Equilibrium (CGE) models to take into account for global impacts (e.g. Calzadilla, Rehdanz, and Tol 2011; Calzadilla et al. 2013; Eboli, Bosello, and Parrado 2011; Randhir and Hertel 2000; Reilly and Hohmann 1993; Tol 2012 to count a few). Others employed country CGE models (Arndt, Chinowsky et al., 2012; Arndt, Farmer et al., 2012b; Çakmak and Dudu 2013; Chang, Chen, and McCarl 2012; Pauw, Thurlow, and Van Seventer 2010; Thurlow, Zhu, and Diao 2012; Thurlow, Dorosh, and Yu 2012) with a stronger focus on the target country. Among the models that use global CGEs, only a few evaluate the potential of trade policies (Calzadilla, Rehdanz, and Tol 2011; Laborde 2011; Randhir and Hertel 2000; Reilly and Hohmann 1993) for adaptation while others generally try to estimate the loss in welfare or GDP at the global and regional levels for different mitigation options. On the other hand, country level CGE models generally focus on country level policy options for adap- tation, with few exceptions (e.g. see Çakmak and Dudu 2013). Further, impact of climate change is expected to worsen over time but they are not likely to be significant until 2050s. Thus, some studies use dynamic models (e.g. Arndt, Chinowsky et al., 2012; Arndt, Farmer et al., 2012; Eboli, Bosello, and Parrado 2011; Pauw, Thurlow, and Van Seventer 2010; Thurlow, Zhu, and Diao 2012; Thurlow, Dorosh, and Yu 2012) to take into account the wor- sening of the effects of climate change over time. However, others argue that more than 40 years of time span is too long to cover with CGE models and hence employ comparative static models (e.g. Calzadilla, Rehdanz, and Tol 2011; Calzadilla et al. 2013; Cline 2007; Chang, Chen, and McCarl 2012; Randhir and Hertel 2000; Reilly and Hohmann 1993). Dynamic models are gen- erally used in country level CGE models with a few exceptions that use dynamic global models (Laborde 2011; Tol 2012).
Although there is no consensus among the conclusions of these studies, some general results can be derived. The results suggest an average negative welfare effect between 1 and 2 per cent of gross domestic product (GDP) at the global level due to climate change (Calzadilla, Rehdanz, and Tol 2011; Tol 2012). Nonetheless, global aggregate impacts hide substantial variations across
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regions and countries, which are driven by their respective adaptation capacities (Arndt, Chinowsky et al., 2012; Bosello, Carraro, and De Cian 2010). Additionally, effects are non-homogenous over space, time, sectors, and social groups. Country level analysis suggest more significant effects especially in the Middle-East (Sowers, Vengosh, and Weinthal 2011; World Bank 2010), Africa (Arndt, Chinowsky et al., 2012; Arndt, Farmer et al., 2012; Pauw, Thurlow, and Van Seventer 2010; Thurlow, Zhu, and Diao 2012) and South Asia (Thurlow, Dorosh, and Yu 2012). On the other hand, the impact of climate change on agricultural output depends on various factors such as the assumed climate model, consideration of carbon fertilisation, location and adaptive capacities of the country etc. (Calzadilla et al. 2013; Cline 2007). When carbon fertilisation effects of climate change are not taken into account, many studies report that crop production will decline over time signifi- cantly (Calzadilla et al. 2013; Rosegrant et al. 2008). On the other hand, some regions are found to increase their agricultural output as a result of climate change when carbon fertilisation effects are taken into account: Canada, Europe, North Russia and North China are among those regions (Cline 2007).
Among the growth levers in the economic landscape for developing countries, international trade is argued to offer a potential for adaptation in the face of climate change (Randhir and Hertel 2000). This is achieved through the enabling channels of technological spill-overs and enhanced access to capital and infrastructure investments and production specialisation. Trade has the potential to alleviate the climate-induced scarcity burden by brid- ging the differences between demand and supply conditions globally. None- theless, it can also increase climate-induced vulnerability in certain regions which specialise in the production of certain products in which they have a comparative advantage, while relying on imports to meet their demands for other commodities and services. Trade liberalisation is reported to have welfare improving effects (Calzadilla, Rehdanz, and Tol 2011; Chang, Chen, and McCarl 2012; Laborde 2011; Reilly and Hohmann 1993). However, these effects are generally insufficient to compensate the adverse effects of climate change (Randhir and Hertel 2000; Reilly and Hohmann 1993). Welfare gains from trade liberalisation depend primarily on the elimination of trade bar- riers such as tariffs and quotas, and subsidies. The effects are not uniform and depend on the geographic location (Calzadilla, Rehdanz, and Tol 2011) and vul- nerability of the region to climate change (Reilly and Hohmann 1993). On the other hand, the impact of trade liberalisation on agricultural production is found to be insignificant (Calzadilla, Rehdanz, and Tol 2011).
To sum up, most of the studies ignore important aspects of the problem at hand by ignoring either the global scale of the issue or country level impli- cations of it. Further, the uncertainty attributed to the assumptions about the climate change impacts is generally ignored. The objective of this paper is to analyse the impacts of climate change at the country level by taking
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into account its implications for international markets. We then evaluate the potential of trade liberalisation, in the form of tariff elimination, as a means of adaptation in the context of developing and emerging countries. Our focus is on Morocco and Turkey as case studies, where we use the Global Trade Analysis Project (GTAP) computable general equilibrium (CGE) model to investigate welfare impacts of various agricultural trade liberalisation scen- arios under climate change.
We motivate the choice of Morocco and Turkey owing to the similarities exhibited in terms of the challenges that climate change poses to the agricul- tural sector. Both Morocco and Turkey are significantly water stressed countries and are classified as vulnerable to the climate change impacts. Further, share of agriculture in GDP in both countries is quite high. Addition- ally, both countries are relatively open to international markets, and have sig- nificant trade in agricultural commodities with developed countries, especially with the European Union (EU). However, the two countries exhibit also dis- parity in terms of the pace and structure of economic development, which directly impacts their respective competitiveness. In this context, we aim to investigate the potential of agricultural trade liberalisation as an adaptation tool in the face of climate change. To this end, the objectives of the analysis are to identify the channels of transmission of climate change impacts on the economy, and their interaction with policies of agricultural trade liberalisation in a context of different degrees of economic competitiveness.
In section 2, we present our methodological approach for developing the range of global yield forecasts and data sources. Section 3 discusses the results for the world and the regional patterns in welfare impacts. Section 4 focuses on the Moroccan and Turkish cases. Section 5 summarises our key findings and conclusions.
2. Methodological approach, scenarios and data discussion
2.1. Modelling framework: the GTAP model
To estimate the impacts of climate-induced agricultural productivity shocks on the economy and the linkages with international trade, we use the GTAP model and its accompanying database. The GTAP model is a multi-commod- ity, multi-region computable general equilibrium model (Hertel and Tsigas 1997). In the standard GTAP model, we assume markets are perfectly competi- tive and exhibit constant returns to scale. Consumers, as represented by the private household, maximise utility where consumption is modelled via a non-homothetic Constant Difference of Elasticity (CDE) implicit expenditure function. Producers are assumed to maximise profits subject to a nested Con- stant Elasticity of Substitution (CES) production function which bundles primary factors and intermediate inputs to produce final output. For the
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purpose of our analysis, a standard neoclassical closure under perfect compe- tition and constant returns to scale, the regional household is on its budget constraint, and global investment equals global savings, with equilibrium imposed in all markets. World price of a given commodity is determined through the global trade balance.
We make use of GTAP database version 7. It provides a disaggregation of agricultural production and harvested area by agro-ecological zones (AEZ)1 by using the Food and Agriculture Organization (FAO) 2004 data on production, harvested area and price, available by country and for 159 FAO crop cat- egories. In terms of regional aggregation, we adopt a regional structure based on 16 regions by aggregating the initial 113 countries/regions within appropriate regional blocs. Given that a special focus of the study is to analyse impacts on Morocco and Turkey, we include the latter as separate regions. Table 1 summarises the regional and sectoral aggregation adopted in the analysis.
The objective of the analysis is to shed light on the potential impacts of trade policy as an adaptation tool in the face of climate change in Morocco and Turkey. More precisely, we are interested in investigating the effects of tariff elimination under climate change. A key focus of the analysis is to high- light what are the transmission channels through which agricultural trade lib- eralisation could potentially mitigate and/or reduce the projected negative impacts of climate change in Morocco and Turkey. To that effect, the analysis is conducted using comparative static mode instead of dynamic approach. Our interest is in isolating the effects of trade liberalisation and climate change apart from any other changes that might occur. Projected yield shocks by 2050 are introduced into the model as productivity shocks for the selected crops sectors. Table 2 summarises the selected simulation scenarios.
Table 1. Regional and sectoral aggregation in the GTAP model. Regions Sectors
Oceania Paddy, rice East Asia Wheat Southeast Asia Coarse grains South Asia Vegetables, Fruits Canada Oilseeds United States Sugar crops Rest of Latin America Other crops Brazil Dairy, Livestock OECD Europe Extract Rest of the Middle-East Vegetable oil Eastern Europe Other processed food Former-USSR Textile, Apparel Turkey Manufactures Rest of North Africa Utilities, Construction Morocco Services Sub-Saharan Africa
Source: Author’s Adaptation (GTAP Database Version 7).
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In order to investigate the effects of agricultural trade liberalisation, we benchmark the results of this scenarios with that of climate change only scen- ario. Figure 1 provides a schematic of the structure of the analysis.
It is worthwhile discussing briefly the potential transmission channels through which climate change will impact the economic activity. As men- tioned earlier, we model climate change impacts on agriculture through pro- ductivity shocks, which are derived from yield projections (cf. Section 2.2). In the GTAP model, we assume that the contribution of each factor to the pro- duction will change proportional to the change in yields due to climate change. In other words, the productivity shocks – as derived from the yield projections – will directly affect the primary factor usage within the agricul- tural crop sectors in the model. This in turn will affect total output within the sector (negative if climate change reduces the yields and positive if it improves them), which then gets transferred through the transmission chan- nels characterising the production structure in the model. This in turn will
Table 2. Definition of simulation scenarios. Scenario Description
BASE Climate change only AGWRLD BASE + 100% tariff elimination on all agricultural commodities in all regions AGEU BASE + 100% tariff elimination on all agricultural commodities between OECD Europe &
Eastern Europe and Morocco, and OECD Europe & Eastern Europe and Turkey AGMENA BASE + 100% tariff elimination on all agricultural commodities among all MENA regions,
including Morocco and Turkey
Source: Authors’ construction.
Figure 1. Schematic of the analysis approach. Source: Authors’ construction.
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affect the prices of factors and intermediate inputs. Thus, households’ and firms’ income and profit will be affected respectively through the impacts on the rents for factors of production and profits, resulting in so called general equilibrium impacts.
2.2. Climate-induced yield shocks: data sources & discussion
Given the objective of the study, we develop a global dataset that takes into consideration the inherent uncertainty in terms of regional distribution of impacts and the heterogeneous nature of their magnitude across climate pro- jections. To that effect, we used two major sources for the yield impact data: International Food Policy Research Institute (IFPRI) Food Security CASE Maps database (IFPRI 2010)2 generated via the IMPACT3 model and the Integrated Model to Assess the Global Environment (IMAGE) Version 2.2. Combining the two databases, we create a comprehensive set of projected yield change esti- mates that provides estimates of productivity shocks by 2050 on the basis of the regional and sector aggregation adopted in the analysis.
A detailed analysis of the methodology developed to compute projected yield estimates and merging procedure of the IFPRI and IMAGE databases can be found in Ouraich et al. (2014).
2.3. A descriptive analysis of projected yield impacts
The nature of production systems (irrigated vs. rainfed), location and photo- synthetic typology of crops4 (i.e. C3 vs. C4 plants) are among the main factors that contribute to the heterogeneous climate-induced productivity impacts on yields. Figure 2 captures the heterogeneity characterising climate change impacts globally in our database. The average yield impact across climate projections and crops indicates that Turkey, the Rest of Middle-East, Brazil, the Rest of Latin America and OECD Europe will experience relatively large negative impacts on average agricultural productivity. The United States, Morocco, the Rest of North Africa, Eastern Europe, Former USSR and Southeast Asia experience slight negative impacts; whereas Canada, South Asia, East Asia, Oceania and Sub-Saharan Africa benefit slightly.
Nevertheless, there exist substantial differences across climate projections and crops. Thus, focusing on averages can be misleading when analysing climate change and international trade linkages. Regional variations in crop yield impacts across climate projections and crops can be large. The regional pattern of climate change impacts and their level of variability will be an important factor that affects international markets’ through the transmission channels previously discussed (Figure A1, Appendix). Equally important is the geographical distribution of the trade flows, their volumes and origins. The role of these differences in explaining the impacts of climate change on
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different regions and sectors will be mentioned to explain the results in the following sections.
3. Global welfare and macroeconomic impacts analysis
In theory, the more international trade is liberalised at the global and sector levels, the higher the welfare gains are, and the more efficient is the smoothing of adverse shocks such as climate- induced productivity shocks. To test this hypothesis, we run a benchmark scenario where we introduce a 100% tariff removal on all commodities under climate change for all regions, which will correspond to a first-best outcome. The climate change only scenario (i.e. ‘BASE’ scenario) results in a welfare loss of USD32 billion on average. When we eliminate tariffs on all commodities, there is a welfare gain of USD45 billion. Therefore, the effect of trade liber- alisation amounts to an average net welfare gain of USD77 billion, which totally mitigates the initial welfare loss due to climate change under the ‘BASE’ scenario.
Furthermore, the mitigation potential of multilateral trade liberalisation seems robust. Indeed, by investigating the distribution of welfare impacts across all climate projections, we observe that a multilateral trade liberalisa- tion scenario induces a complete shift of welfare impacts from negative to positive under all climate projections. However, when adopting a limited trade liberalisation agenda, which in our case is focused on the agricultural sectors, tariff elimination does not achieve total mitigation globally. The average welfare impact remains negative under all the agricultural trade
Figure 2. Regional distribution of average yield impacts across crops and climate projec- tions. Source: Authors’ adaptation from IFPRI (2010) and MNP (2006).
968 I. OURAICH ET AL.
liberalisation scenarios analysed. Despite observing net welfare gains from agricultural trade liberalisation, they are less than the climate change induced losses.
Welfare decomposition is an important output of this research. Although well known by the users of the GTAP model, it would help the reader to explain the main components of welfare decomposition and their intuitive meaning. The components of welfare change in GTAP are defined as alloca- tive efficiency, technical efficiency, terms of trade, and investment-saving. Allocative efficiency component covers the change in the household welfare due to the reallocation of the economic resources (e.g. factors of pro- duction) across sectors and regions due to the changes in economic policy. In our case, allocative efficiency will cover the welfare gains due to the realloca- tion of factors of production (e.g. labour, capital and intermediate inputs) towards the more efficient sectors and regions following the trade liberalisa- tion. Technical efficiency covers the changes in the welfare due to the change in the production technology. For example, since agricultural sectors become less productive under climate change, the technical efficiency component will be negative, while it will be insignificant under trade liberalisation, as the latter do not involve any change in productivity. Terms of trade component covers the welfare change due to changing prices in the international markets. Rising export prices and falling import prices are likely to increase the welfare of a region as the households will receive more income from the sectors as increasing export prices increases the production and hence demand for factors owned by households, which, in turn, will translate to higher household incomes. On the other hand, decreasing import prices will allow households access to cheaper commodities and consume more. Lastly, investment-saving component covers the change in welfare due to changing investments and savings. An increase in investments (which implies an increase in savings due to closure assumptions) will lower the household consumption and hence cause a decline in welfare.
In our case, neither climate change nor trade liberalisation affects invest- ment/saving directly, so this component will be insignificant. To sum up, it is the interplay between the allocative and technical efficiency components that drive most of the observed results for the world region (Table 3).
The terms of trade component is very small for the world region; however, it exhibits large impact at the country level which affect the level of aggregate welfare. The explanation behind the insignificance of the terms of trade com- ponent for the world region lies in the model assumptions governing the trade accounts. In the GTAP model, a key assumption is the clearing and bal- ancing of international trade markets, which implies that the sum of the value of net exports at world prices from all regions must equal zero. Hence, the aggregate contribution of the terms of trade component for the world region would tend to be insignificant, although it could be quite large at
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the country level. In fact, at the regional level, the potential gains associated with improved resource allocation due to the trade liberalisation are offset by the worsening of the terms of trade component (Tables A1 and A2, Appendix).
4. Analysis of results for Morocco
Climate change impacts on Moroccan agriculture are quite large. On average, crops yields for wheat, coarse grains, and sugar crops are projected to decline by 10% by 2050, and which account for 53% of total agricultural output and 50.5% of total arable land. In addition, these sectors tend to have strong lin- kages with non-crop agricultural sectors (e.g. livestock) and the food proces- sing sectors. Hence, large negative shocks will certainly trickle down to the rest of the economy and affect it negatively.
The results show that, on average, Morocco does not benefit from trade liberalisation ex-post climate change. In terms of net welfare impacts, tariff elimination induces further welfare losses on average for all the agricultural trade liberalisation scenarios. The lowest net welfare loss occurs under the ‘AGMENA’ scenario and reaches −4.9 million USD; whereas the highest loss is observed for the ‘AGEU’ scenario, with a net welfare loss of −164.4 million USD. When investigating the decomposition of welfare impacts, we observe that tariff elimination induces net gains from the allocative effi- ciency component under all the trade liberalisation scenarios. On the other hand, the terms of trade component are generally negative, where they represent the dominant effect for all the scenarios (Tables A1 and A2, Appendix).
In terms of impacts on GDP, the results suggest a relatively large net nega- tive impact, and especially under the ‘AGWRLD’ trade liberalisation scenario. On average, the net contribution of trade liberalisation on GDP is negative
Table 3. Aggregate welfare impact and its decomposition by effect for the world region (2004 million USD).
Scenario
Allocative efficiency component (a)
Technical efficiency
component (b) Terms of trade component (c)
Investment- Savings
component (d)
Equivalent Variation (EV) (e) = (a) + (b) + (c)
+ (d)
Climate change only (1) BASE −4075.6 −27,699.2 −0.6 0.3 −31,775.1
Trade liberalisation under climate change (2) AGWRLD 22,334.5 −27,111.8 −16.2 −0.2 −4793.7 AGEU −4076.5 −27,684.1 −1.6 0.2 −31,761.9 AGMENA −4030.9 −27,709.6 −0.4 0.3 −31,740.6
Net impact of trade liberalisation (3) = (2) − (1) AGWRLD 26,410.1 587.4 −15.7 −0.5 26,981.4 AGEU −0.9 15.1 −1.1 −0.1 13.1 AGMENA 44.7 −10.4 0.1 0 34.4 Source: Simulation results.
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under all scenarios, with the largest impact occurring under the ‘AGWRLD’ scenario (−4%) and the lowest under the ‘AGMENA’ scenario (−0.3%) (Figure 3).
In the aftermath of tariff elimination, the results suggest that exports and imports experience large increases quantitatively as suggested by the evol- ution of aggregate quantity index. Moreover, it seems export quantities increase more than imports. Nevertheless, the increase in exports quantities occurs in a context of decreasing prices; whereas imports increase in a context of increasing prices (Figure 4).
For instance, aggregate export quantity index increases on average by 8% under the ‘AGWRLD’ scenario; but with the export price index falling by 2%. For imports, the quantity index increases by 5% on average, with the price index increasing by 0.2% under the same scenario. A similar trend occurs under the ‘AGEU’ and ‘AGMENA’ scenarios, where export quantity index increases by 5% and 0.1% respectively; but the export price index declines by 1% and 0.02%. On the import side, quantity and price indices change in the same direction, where they increase on average by 3% and 0.1% for quan- tities and increase by 0.2% and 0.003% for prices, respectively under the ‘AGEU’ and ‘AGMENA’ scenarios.
At the commodity level, the impact of climate change (‘BASE’) on crop yields translates directly into a supply shock with the same sign. For the crop sectors, that display negative average yield shocks domestic supply
Figure 3. Gross domestic product (GDP) impacts for Morocco by scenario (in % change). Source: Simulation results. Note: Results are presented in box plots using the ‘ggplot2’ package in R. The lower and upper borders of the box plot represent respectively the 25th and 75th percentiles of the distribution of yield projections. The upper (lower) whisker extends from the box plot upper (lower) border to the highest (lowest) value that is within 1.5*IQR of the border, where IQR stands for inter-quartile range defined as the distance between the 25th and 75th percentiles. The black lines inside the box plot refer to the median of the dis- tribution. The red dots represent the average of the distribution. Data beyond the end of the whiskers are outliers and are plotted as blue squares. For a detailed discussion, refer to McGill, Tukey, and Larsen (1978).
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declines; and vice-versa for the crop commodities exhibiting positive yield shocks. In turn, domestic supply prices react according to theoretical expec- tations, where they increase for the negatively impacted crop sectors; and vice-versa for the positively affected ones. However, the impacts on exports and imports are mixed. For instance, for wheat, we observe that despite the negative supply shocks (−11%), exports increase (+3%). This result would seem counterintuitive in the case of wheat, especially given its strategic nature in the food basket of Moroccan households. However, when investi- gating the import dynamic, we observe that wheat imports display a large increase (+31%), despite the increase in world import prices (+7%), but which remains lower compared to the increase in domestic supply price (+16%). Hence, the explanation for increasing exports lies in the fact that imports increasingly displace domestic output in domestic markets. For
Figure 4. Average per cent change in exports and imports price and quantity indices for Morocco. Source: Simulation results. Note: The bars measure the mean of the distribution across climate projections, and the error bars rep- resent the mean ± standard deviation of the distribution.
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other crops, the same pattern holds. For the remaining crop sectors, the results match theoretical expectations, where for the crop sectors negatively (positively) impacted by climate change, domestic output declines (increases). In turn, exports decrease (increase) and imports increase (decrease) (Table A3, Appendix).
With tariff elimination on agricultural commodities, e.g. under the ‘AGWRLD’ scenario, the results exhibit interesting patterns. For instance, for wheat, we observe that domestic output declines more compared to the ‘BASE’ scenario; whereas supply price exhibit a lower increase. This driven by the same dynamic previously discussed. Imports exhibit a large increase compared to ‘BASE’ scenarios, where it reaches +204% for the ‘AGWRLD’ scen- ario. Thus, the import displacement effect on domestic supply is more pro- nounced. As a result, exports of wheat tend to increase more. For coarse grains, the results in terms of the direction of impacts is similar to ‘BASE’ scen- ario, however the magnitude are larger. Domestic supply tends to decline further (−18%), but with a lower impact on prices (+10%) compared to the ‘BASE’ scenario (+17%). Similar to case of wheat, this pattern occurs due to the displacement effect that imports have on domestically supplied coarse grains, where imports increase by +87% compared to +6% under the ‘BASE’ scenario. A similar pattern occurs for wheat and coarse grains under the ‘AGEU’ scenario, but with lower magnitudes. Under the ‘AGMENA’ scenario, the results do not exhibit major changes in terms of the direction and the magnitude of the impacts compared to the ‘BASE’ scenario (Table A3, Appendix).
The extent to which the described price and quantity dynamic, under climate change and agricultural trade liberalisation, affects welfare through the term of trade effect depends on the trade structure of Moroc- can exports and imports. As a share of total imports, agricultural crops account for 7%. Additionally, and upon investigating the structure of import demand across agents, we observe that 75% of agricultural crop imports is allocated to final household demand, and where they account for 20% of total household import demand. Under the climate change scenario (‘BASE’), the agents’ share in total import exhibit relatively small changes, where the share of households in total agricultural crop imports decreases by 1% and increases by 1% for firms. However, when investi- gating the import demand allocation structure across commodities, we observe that the share of agricultural crop commodities increases by 3% and decreases by the same amount for the non-crop commodities for households. When eliminating tariffs, e.g. under the ‘AGWRLD’ scenario, we observe an increased dependency on imports for households, where the share of agricultural crop commodities increases by 8% and declines by 8% for the non-crop commodities. The same dynamic is observed for firms, where the trend is a shift toward increasing import for the
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agricultural crop commodities and a reduction in imports of non-crop com- modities. But, the magnitudes are smaller compared to the households. These results hold for both agents, i.e. households and firms, under all the trade liberalisation scenarios, but with lower magnitudes as the scope of tariff elimination is restricted (Table 4).
Hence, the negative contribution of the terms of trade effect to the welfare results discussed previously find their explanations in the dynamic exhibited by imports and exports, and in conjunction with the structure of their allo- cation across agents. In the Moroccan context, imports become relatively less expensive with tariff elimination, but import demand exhibits a large increase. At the same time, exports prices decline for key export sectors in Morocco.
As previously argued, tariff elimination under all scenarios induces signifi- cant fluctuations in final and intermediate demands for domestic and imported commodities through their impact on market prices. We previously discussed the implications of trade liberalisation in the context of climate change in Morocco, and we concluded that it induces an increased depen- dency on imports, especially of agricultural crop commodities. This occurs under deteriorating terms of trade for Morocco, which is driven by a dynamic of an increase in imports with increasing prices, and declining export prices for key export sectors. However, in what follow, we turn our focus to the analysis of impacts on domestic markets.
In Morocco, climate change induces a 6% increase in total value of mar- keted output for the crops; where supply to domestic and export markets increases by 2.5% and 29% respectively. These results are not surprising given the fact that prices increase much more than the decline in output associated with the negative crop yield impacts under climate change. Despite the increase in domestically marketed output for agricultural crop
Table 4. Evolution of import sales disposal in Morocco by scenario, by agent type and by commodity group (in 2004 million USD and in % share).
Import sales disposal
Levels (in 2004 million USD)
Share across commodity type (in %)
Share across agent category (in %)
Hhd Gov Firms Hhd Gov Firms Hhd Gov Firms
Baseline Crops 1192 0.4 403 20% 0% 2% 75% 0% 25% Non-crops 4771 689 17,417 80% 100% 98% 21% 3% 76%
BASE Crops 1427 0.4 498 23% 0% 3% 74% 0% 26% Non-crops 4761 687 17,328 77% 100% 97% 21% 3% 76%
AGWRLD Crops 1746 0.6 734 28% 0% 4% 70% 0% 30% Non-crops 4568 656 17,378 72% 100% 96% 20% 3% 77%
AGEU Crops 1674 0.5 648 26% 0% 4% 72% 0% 28% Non-crops 4660 671 17,347 74% 100% 96% 21% 3% 76%
AGMENA Crops 1434 0.4 500 23% 0% 3% 74% 0% 26% Non-crops 4759 687 17,328 77% 100% 97% 21% 3% 76%
Source: Simulations results.
974 I. OURAICH ET AL.
commodities, their share in total consumption declines by 3.4%. This is because imports displace domestic production in final consumption, where its share increases by 14% (Table 5).
When tariffs on agricultural crop commodities are eliminated, we observe large impacts on the allocation of total marketed output between domestic and export markets. For instance under the ‘AGWRLD’ scenario, domestically marketed output for agricultural crop commodities declines by 22%; whereas exports increase by 23%. On the domestic markets, prices tend to increase fol- lowing the negative supply shock, which tend to induce a decline in quantity demanded. As a result, the share of agricultural crops in final total consumption declines by 17%. This is because imports tend to, increasingly; displace dom- estic supply when tariffs are eliminated. For the ‘AGWRLD’ scenario, the share of agricultural crops in total imports increases by 56%; whereas their share in total consumption increases by 67%. These trends are the same under the remaining trade liberalisation scenarios, but with lower magnitudes (Table 5).
5. Analysis of results for Turkey
In Turkey, trade liberalisation has a positive impact on aggregate welfare on average. Under all the scenarios analysed, we observe that tariff elimination induces net welfare gains on average. Additionally, we observe that the wider the regional scope of the tariff elimination, the larger the net welfare gains. For instance, Turkey’s net welfare gains reach USD729 million for the ‘AGWRLD’ scenario, USD23 million for the ‘AGEU’ scenario, and USD7 million for the ‘AGMENA’ scenario. However, the contribution of each welfare com- ponent, especially the allocative efficiency and terms of trade components, to aggregate net welfare impact differs across scenarios. For instance, the net contribution of the terms of trade component under the ‘AGWRLD’
Table 5. Evolution of the share of domestic, export, and import markets in total production and total consumption in Morocco by scenario (in % change from the baseline).
% change in the share in total output
by market
% change in the share of output disposal by sector
% change in the share in total
consumption by sector
Domestic Export Domestic Export Domestic Import
BASE Crops 2.5% 28.7% −3.2% 21.5% −3.4% 13.7% Non-crops −0.5% −0.7% 0.0% −0.2% 0.0% 0.0%
AGWRLD Crops −22.3% 22.5% −6.9% 46.7% −16.6% 67.0% Non-crops −0.7% 6.5% −1.2% 6.0% 0.1% −0.4%
AGEU Crops −11.0% 45.5% −7.6% 51.1% −11.2% 45.2% Non-crops −0.7% 2.7% −0.5% 2.9% 0.0% −0.1%
AGMENA Crops 2.2% 29.4% −3.3% 22.4% −3.6% 14.4% Non-crops −0.5% −0.6% 0.0% −0.1% 0.0% 0.0%
Source: Simulation results.
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scenario is negative (USD-132 million); but the net gains associated with the allocative efficiency component offset it totally (USD841 million). For the ‘AGEU’ scenario, the reverse dynamic occurs, where the net contribution of the terms of trade component (+34 million USD) is larger than the net loss associated with the allocative efficiency component (−12 million USD). For the ‘AGMENA’ scenario, both components contribute positively in net terms to aggregate welfare. Nonetheless, the net gains are not large to offset totally the initial welfare loss induced by climate change (Tables A1 and A2, Appendix).
In terms of GDP impacts, the results suggest a mixed picture. On average, impacts on GDP are negative under all the tariff elimination scenarios. The net impacts of tariff elimination are negative on average, except for the ‘AGEU’ scenario. For instance, average net impacts on GDP reach −0.31% under the ‘AGWRLD’ scenario and −0.01% under the ‘AGMENA’ scenario; whereas the net impacts are positive under the ‘AGEU’ scenario and reach +0.03% (Figure 5).
In the aftermath of tariff elimination, the results for Turkey suggest that the net impacts on exports and imports are positive for all the scenarios. Unlike the Moroccan case, Turkey seems to benefit on the export side given that both, the export quantity and price indices exhibit net positive impacts for all scenarios, except under the ‘AGWRLD’ scenario where the net impact on the export price index is negative. On the imports side, the net impact of tariff elimination is positive on the import quantity index for all scenarios. A similar pattern is observed for the import price index, except for the ‘AGWRLD’ scenario where the net impact is negative. Another important result is the fact that the magni- tudes of net impacts are larger for exports compared to imports under all scen- arios. For instance, the average net decline in the price index reaches −0.18%
Figure 5. Gross domestic product (GDP) impacts for Turkey by scenario (in % change). Source: Simulation results.
976 I. OURAICH ET AL.
and −0.02% for exports and imports respectively; whereas for the quantity index, the net impacts reach +1.6% and +1.2% for exports and imports respect- ively under the ‘AGWRLD’ scenario. This explains the negative net contribution of the terms of trade effect to aggregate welfare as previously discussed for the ‘AGWRLD’ scenario (Figure 6).
At the sectorial level, and focusing on the crop commodities, we observe that domestic supply declines for all crop commodities owing to the climate change impact on the yields. As a result, domestic supply prices increase, which increases export prices. Hence, exports decline for most crop commodities, except for rice and wheat, which display a counterintuitive pattern in terms of quantity and price impact. For rice, despite the positive yield impact, domestic supply declines by −12% on average. However, the final impact on supply price is negative (−1%). The explanation behind this result lies on the import dynamic. The decline in domestic supply, despite increasing yield for rice, occurs due to an increase in imports (+13%) with
Figure 6. Average per cent change in export and import price and quantity indices for Turkey. Source: Simulation results.
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declining import prices (−6%). Thus, rice imports increasingly displace dom- estic rice on domestic markets. For wheat, the implication of climate change on domestic markets follows the standard economic response. The slump in domestic supply owing to declining yields induces domestic supply prices to increase (+11%). However, we observe that wheat exports increase by +7%. The driving force of this pattern of impacts lies again in the import dynamic. Wheat imports tend to increase substantially (+40%), with relatively increasing prices (+2%). Hence, the displacement effect of domestic supply for imports is large, which helps explaining the pattern observed for exports. For the remaining crop sectors, the climate change induces impacts that are in line with theoretical expectations. The decline in domestic supply translated into declining exports, at the while increasing domestic prices. The latter increase more compared to import prices, which results in an increase in imports to fill in the gap. Unsurprisingly, the food pro- cessing sectors, by virtue of their strong linkages with agricultural markets, display a similar trend in terms of price and quantity impacts, but with lower magnitudes (Table A4, Appendix).
Under all the tariff elimination scenarios analysed, the direction and mag- nitude of change in terms of price impacts remain largely similar to the ‘BASE’ scenario, with slightly lower magnitudes for the ‘AGWRLD’ scenario. For the quantity variable, a similar pattern holds except for the ‘AGWRLD’ scenario where the magnitude increase substantially, but remain mostly in the same direction (Table A4, Appendix).
The extent to which, the described price and quantity dynamic under climate change affects welfare through the terms of trade effect depends on the structure of Turkish exports and imports. As a share of total imports, agricultural crops account for 3%. Additionally, 39% agricultural crop imports are for final household demand, 58% to intermediate demand by firms and 3% to government expenditure. As a share in total household import demand, agricultural crops account for 4%; whereas they represent 7% and 2% of total government and firms import demand. Under the climate change scenario (‘BASE’), the agents’ share in total import exhibit small changes, where the share agricultural crops in total imports increases by 1% for government expenditure and firms, while it remains unchanged for private households. When tariffs on agricultural crop commodities are eliminated, e.g. under the ‘AGWRLD’ scenario, there is increased dependency on agricultural imports for all agents. Moreover, most of the increase is allo- cated to private household consumption as their share in total imports of agri- cultural crops increases by 6%, while it declines for firms by 7% (Table 6).
For Turkey, the negative impact of climate change on crop yields induces a structural change in the allocation of output between domestic and export markets for the crop and non-crop commodities. The share of crop commod- ities in the domestic market increases by 9% on average, and it declines by
978 I. OURAICH ET AL.
−0.7% for the non-crop commodities. A reverse dynamic occurs in the export markets, where the share of crop commodities declines by −8% and increases by 0.3% for the non-crop commodities.
When tariffs are eliminated, e.g. the ‘AGWRLD’ scenario, the share of crop commodities in total domestically sold output increases by 2% only compared to 9% under climate change; whereas the non-crop commodities’ share declines by −0.1% compared with −0.7% under climate change. As a result, the share of domestic markets in total output sales for crop commodities declines by −2%; whereas it increases by 20% for export markets. When inves- tigating the structure of consumption allocation, the decline in domestic markets’ share is even higher and reaches −5%. This is primarily driven by the surge in imports, which tend to increasingly displace domestic supply in total consumption, which is suggested by the 48% increase in the share of imports in total consumption for crop commodities. For the remaining tariff elimination scenarios, the results remain similar to the climate change scenario, albeit with lower magnitudes (Table 7).
6. Conclusions
As per theoretical expectations, the more trade is liberalised, the higher the welfare gains for the world region. Through a ‘benchmark’ scenario, we showed that the welfare gain accruing from a total elimination of tariffs for all regions and all traded commodities offset all the welfare loss associated with the climate change impacts on the agricultural sectors. However, total tariff elimination for all regions and all commodities may not be a realistic scen- ario. Hence, we focus in the paper on the agricultural sectors by implementing a series of trade liberalisation scenarios via tariff elimination schemes.
Table 6. Evolution of import sales disposal in Turkey by scenario, by agent type and by commodity group (in 2004 $US million and in % share).
Import sales disposal
Levels (in 2004 million US$)
Share across commodity type
(in %) Share across agent category (in %)
Hhd Gov Firms Hhd Gov Firms Hhd Gov Firms
Baseline Crops 993 93 1481 4% 7% 2% 39% 4% 58% Non-crops 27,084 1245 69,942 96% 93% 98% 28% 1% 71%
BASE Crops 1186 103 1847 4% 8% 3% 38% 3% 59% Non-crops 26,768 1231 69,681 96% 92% 97% 27% 1% 71%
AGWRLD Crops 1808 179 2059 6% 13% 3% 45% 4% 51% Non-crops 26,734 1228 69,619 94% 87% 97% 27% 1% 71%
AGEU Crops 1218 105 1877 4% 8% 3% 38% 3% 59% Non-crops 26,785 1232 69,671 96% 92% 97% 27% 1% 71%
AGMENA Crops 1207 104 1862 4% 8% 3% 38% 3% 59% Non-crops 26,769 1231 69,681 96% 92% 97% 27% 1% 71%
Source: Simulation results.
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The main conclusions is that the scope of the tariff elimination at the regional and sectorial level does matter in determining the ability of trade lib- eralisation to mitigate and/or assist in dampening the losses associated with climate change. For all the scenarios analysed, the results suggest that the net welfare gains from tariff elimination are not large enough to offset the welfare loss under climate change for the world region. For our regions of interest, i.e. Morocco and Turkey, dependency on international agricultural markets tends to increase for Morocco and Turkey under climate change, which is driven by the large negative productivity impacts on agriculture and their transmission in the wider economy. Dependency on international markets deepens when tariffs are eliminated on agricultural trade flows. However, the welfare impli- cations of trade liberalisation differ for the two countries.
For Morocco, the results suggest that the average net welfare contribution of agricultural trade liberalisation across climate projections is negative. Despite the net gains stemming from the improved efficiency in resource allo- cation, the large negative contribution of the terms of trade component offsets all the gains. Exports tend to increase, but in a context of decreasing export prices on average, whereas imports show large increases in a context of increasing prices, especially for crop and food commodities.
In Turkey, on the other hand, trade liberalisation induces net welfare gains under all scenarios. However, the magnitude of the gains is not large enough to offset the totality of the initial loss under climate change. Simulation results suggest that the combined contribution of the net gains from the efficient allocation of resources and the terms of trade components drive the results. On average, Turkish exports tend to increase with increasing prices; and a similar pattern occurs for imports, albeit at substantially lower magnitudes for the import prices compared to the Moroccan case.
The difference in terms of the net impacts of trade liberalisation in both countries stems from several factors. First, the structure of domestic demand is different. In Morocco, staple crops and food commodities
Table 7. Evolution of the share of domestic, export, and import markets in total production and total consumption in Turkey by scenario (in % change from the baseline).
% change in the share in total output
by market
% change in the share of output disposal by sector
% change in the share in total consumption
by sector
Domestic Export Domestic Export Domestic Import
BASE Crops 9% −8% 2% −15% −1% 11% Non-crops −0.7% 0.3% −0.2% 1% 0.0% 0.0%
AGWRLD Crops 2% 23% −2% 20% −5% 48% Non-crops −0.1% −1% −0.3% 1% 0.0% 0.0%
AGEU Crops 9% −1% 1% −8% −1% 13% Non-crops −0.6% 0.0% −0.2% 1% 0.0% −0.01%
AGMENA Crops 9% −6% 1% −12% −1% 12% Non-crops −0.6% 0.2% −0.2% 1% 0.0% 0.0%
Source: Simulation results.
980 I. OURAICH ET AL.
represent a substantial share in private households’ expenditure from dom- estic and import markets; whereas in Turkey, it is much lower, where the share of firms are higher. Second, the Moroccan economy exhibits a higher level of protectionism compared to Turkey, as exhibited by the tariff structure in the two countries. Hence, total tariff elimination on agricultural commod- ities in Morocco represents a bigger shock to prices compared to the Turkish case. Additionally, we might argue that the Turkish economy exhibit a competitive edge compared to its Moroccan counterpart (Table A5, Appen- dix). Last but not the least, the analysis conducted focused primarily on inves- tigating the linkages between climate change and barriers to trade. In other words, we did not account for the presence, or not, of government support (e.g. input subsidies, export subsidies, etc.). For instance, firms in Turkey benefit from government support (i.e. subsidies) with respect to their inter- mediate input purchases, especially for staple and crop commodities (Table A5, Appendix). Therefore, we argue that final consumers (i.e. private house- holds) in Turkey are more insulated from the vagaries of price shocks caused by climate change and tariff elimination compared to their Moroccan counterparts.
Finally, we believe that the analysis presented in the paper points to several areas that need further investigation. For instance, model results need testing with respect to parametric assumptions, especially for the Armington and trade elasticities. Many studies show that the welfare impacts of trade liberal- isation can be sensitive to parametric assumptions, where higher values for the Armington elasticities induces higher welfare gains (e.g. Domingues, Haddad, and Hewings 2008; Valenzuela, Anderson, and Hertel 2008; Zhang 2006). In our case, this could translate into an enhanced capacity for trade lib- eralisation to alleviate the negative impacts of climate change. However, this remains to be verified and tested, Another important area of investigation, which is receiving increasing attention in the literature of climate change, is the linkages of non-tariff barriers to trade. The latter are increasingly being a problematic issue in international trade negotiations, especially with respect to agricultural trade.
Notes
1. The AEZ structure in the GTAP model is based on the SAGE (The Centre for Sus- tainability and the Global Environment), database, which was developed by aggregating the IIASA/FAO GAEZ data into six categories identified by the length of growing period (LPG). In addition to the LGP break-down, the world is subdivided into three climatic zones, namely: tropical, temperate, and boreal.
2. For further details, please refer to: http://www.ifpri.org/book-775/ourwork/ researcharea/climate-change/case-maps.
3. The IMPACT model is IFPRI’s global partial equilibrium model designed to examine alternative futures for global food supply, demand, trade, prices, and
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food security. For further details, please refer to: http://www.ifpri.org/book-751/ ourwork/program/impact-model.
4. ‘So-called C3 plants use CO2 less efficiently than C4 plants, so C3 plants such as rice and wheat are more sensitive to higher concentrations of CO2 than C4 plants like maize and sugarcane. However, when nitrogen is limiting, the CO2 fertilization effect is dramatically reduced. So the actual benefits in farmer fields of CO2 fertilization remain uncertain’ (Nelson et al. 2010).
5. Refers to industry output by commodity. 6. Refers to supply price by commodity. 7. Refers to aggregate exports by commodity, FOB weights. 8. Refers to aggregate exports price index by commodity. 9. Refers to aggregate imports by commodity, CIF weights.
10. Refers to world price of composite import by commodity.
Acknowledgements
The authors gratefully acknowledge the financial contribution of the World Institute for Development Economics Research of the United Nations University (UNU-WIDER), Hel- sinki, Finland. In addition, we express our gratitude for Dr. Gerald Nelson for comments and technical reviews. Lastly, but not the least, we extend our gratitude to the anon- ymous reviewers at the Journal of North African Studies who have contributed in the improvement of the quality of the paper through their valuable reviews and comments.
Disclaimer
The views expressed herein are those of the authors and do not necessarily reflect the views of the World Institute for Development Economics Research of the United Nations University (UNU-WIDER). The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This paper is one of a series of studies prepared within the World Institute for Devel- opment Economics Research of the United Nations University (UNU-WIDER) project on “The Middle East, North Africa, and Climate Change,” directed by Wallace E. Tyner and Imed Drine.
ORCID
Ismail Ouraich http://orcid.org/0000-0002-0111-9981 Erol H. Cakmak http://orcid.org/0000-0001-9873-0113
982 I. OURAICH ET AL.
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Appendix
Figure A1. Distribution of climate change impacts on yield by 2050, by region and by crop (in % change). Source: Source: Authors’ adaptation from IFPRI (2010) and IMAGE (2010).
Table A1. Average of the allocative efficiency component by region and by scenario (in 2004 million USD).
Climate change only (1)
Trade liberalisation under climate change (2)
Net impact of trade liberalisation (3) = (2) − (1)
BASE AGWRLD AGEU AGMENA AGWRLD AGEU AGMENA
Oceania 106.2 121.4 105.7 106.3 15.2 −0.5 0.1 East Asia 415.4 21,271.5 422.3 421.5 20,856.1 6.9 6.1 South-East Asia −78.3 110.4 −78.5 −78.1 188.7 −0.2 0.1 South Asia −189.8 104.0 −188.0 −188.6 293.8 1.8 1.3 Canada −111.7 −117.5 −109.1 −110.8 −5.8 2.5 0.8 United States −548.5 −469.2 −537.1 −542.5 79.3 11.4 6.0 Rest of Latin America
−826.4 −1556.7 −813.8 −821.1 −730.4 12.6 5.3
Brazil −306.8 −443.3 −304.3 −306.3 −136.5 2.5 0.5 OECD Europe −1964.6 1576.9 −1995.2 −1953.7 3541.5 −30.6 10.9 Rest of the Middle-East
−173.5 213.8 −175.2 −171.5 387.3 −1.7 2.0
Eastern Europe −365.4 135.1 −366.7 −364.2 500.5 −1.3 1.2 Former USSR −121.3 42.7 −124.0 −120.3 164.0 −2.7 1.0 Turkey −3.8 837.2 −15.4 0.6 841.0 −11.6 4.4 Rest of North Africa
10.8 104.1 10.0 15.2 93.3 −0.8 4.4
Morocco 77.6 318.6 85.4 78.1 241.0 7.8 0.5 Sub-Saharan Africa
4.5 85.5 7.5 4.7 81.1 3.0 0.2
Total −4075.6 22,334.5 −4076.5 −4030.9 26,410.1 −0.9 44.7 Source: Simulation results.
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Table A2. Average of the terms of trade component by region and by scenario (in 2004 million USD).
Climate change only (1)
Trade liberalisation under climate change (2)
Net impact of trade liberalisation (3) = (2) − (1)
BASE AGWRLD AGEU AGMENA AGWRLD AGEU AGMENA
Oceania 147.1 93.8 131.4 142.0 −53.3 −15.7 −5.1 East Asia −794.5 −324.8 −768.2 −791.5 469.7 26.3 3.0 Southeast Asia 90.4 225.1 92.7 86.8 134.8 2.4 −3.6 South Asia 200.0 311.4 195.1 190.9 111.5 −4.8 −9.1 Canada 212.9 309.7 186.2 210.8 96.9 −26.7 −2.1 United States 1462.5 1406.6 1440.4 1446.9 −55.9 −22.1 −15.6 Rest of Latin America
1052.7 2812.6 1036.4 1045.6 1759.8 −16.3 −7.1
Brazil 795.9 1604.5 779.2 791.1 808.6 −16.6 −4.8 OECD Europe −2321.3 −5324.6 −2088.5 −2327.7 −3003.3 232.8 −6.3 Rest of the Middle-East
−610.0 −642.4 −603.9 −559.2 −32.3 6.2 50.9
Eastern Europe 44.9 −139.3 62.0 43.7 −184.2 17.1 −1.2 Former USSR −271.3 108.5 −281.9 −271.5 379.8 −10.6 −0.1 Turkey 207.2 75.6 240.9 210.8 −131.6 33.7 3.6 Rest of North Africa
−363.5 −357.9 −362.6 −355.2 5.6 0.9 8.2
Morocco −32.8 −456.7 −240.6 −37.7 −423.9 −207.8 −4.9 Sub-Saharan Africa
179.3 281.5 179.6 173.7 102.1 0.3 −5.6
Total −0.5 −16.2 −1.6 −0.4 −15.7 −1.1 0.1 Source: Simulation results.
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Table A3. Average impact on quantities and prices for domestic output, exports and imports for Morocco by commodity and by scenario (in % change). Scenario ‘BASE’ Scenario ‘AGWRLD’
qo5 ps6 qxw7 pxw8 qiw9 piw10 qo ps qxw pxw qiw piw
Paddy rice 16.4 −7.5 195.2 −7.5 −32.4 5.8 −4.6 −14.0 93.3 −14.0 27.7 2.3 Wheat −10.9 15.9 3.2 15.9 30.7 7.0 −49.1 6.8 13.9 6.8 203.8 9.8 Coarse grains −4.6 16.6 −9.4 16.6 5.5 7.7 −18.4 9.6 −5.5 9.6 86.9 9.2 Vegetables, fruits, and nuts 10.6 −1.0 34.6 −1.0 −8.7 4.4 9.0 −6.0 31.9 −6.0 50.2 4.1 Oilseeds 4.1 1.3 43.0 1.3 −5.4 3.7 3.3 −4.0 61.5 −4.0 4.1 3.6 Sugar cane, Suger beet −2.1 21.1 −32.8 21.1 52.8 4.3 3.4 15.3 −16.1 15.3 96.1 5.3 Other crops nes −0.4 3.4 12.7 3.4 0.9 2.6 −1.2 −1.8 55.7 −1.8 20.4 1.5 Meat, livestock, raw milk −1.3 1.0 −1.2 1.0 −0.3 0.8 1.9 −2.9 24.3 −2.9 −8.2 0.4 Forest, fish & minerals 0.0 −0.2 0.3 −0.2 −0.4 −0.1 2.6 −0.3 2.3 −0.3 2.4 −0.1 Vegetable oils and fats −2.8 2.0 −5.9 2.0 −1.5 2.0 13.1 −2.7 24.6 −2.7 −8.4 2.1 Other processed food −3.4 3.2 −9.0 3.2 3.1 0.6 2.7 −2.6 10.5 −2.6 −4.8 0.1 Textile and apparel −1.5 0.2 −1.5 0.2 −0.8 0.0 7.9 −1.5 9.7 −1.5 2.1 −0.2 Manufactures −0.2 −0.1 0.5 −0.1 −0.5 −0.1 3.7 −1.3 8.6 −1.3 −1.8 −0.1 Utilities and Construction −0.4 −0.2 0.5 −0.2 −0.7 −0.1 −0.7 −1.6 7.2 −1.6 −2.3 −0.2 Transportation and Services −0.4 −0.1 0.1 −0.1 −0.5 −0.1 0.8 −1.8 6.3 −1.8 −3.3 −0.1
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Table A3. Continued. Scenario ‘AGEU’ Scenario ‘AGMENA’
qo ps qxw pxw qiw piw qo ps qxw pxw qiw piw
Paddy rice 0.4 −11.1 288.2 −11.1 18.2 7.0 16.5 −7.6 195.0 −7.6 −32.5 5.8 Wheat −35.4 10.8 42.8 10.8 164.6 10.5 −10.9 15.9 3.6 15.9 30.6 7.0 Coarse grains −3.8 14.0 −2.5 14.0 12.3 7.8 −4.6 16.5 −9.3 16.5 5.5 7.7 Vegetables, fruits, and nuts 15.3 −2.9 52.2 −2.9 33.4 5.2 10.1 −1.1 35.2 −1.1 16.1 4.7 Oilseeds 6.0 −0.8 64.3 −0.8 −3.7 3.7 4.1 1.2 45.3 1.2 −5.2 3.7 Sugar cane, Suger beet 0.3 18.6 −25.8 18.6 102.0 5.6 −2.0 21.0 −32.9 21.0 52.7 4.2 Other crops nes 2.3 1.3 61.0 1.3 6.1 2.9 −0.4 3.3 16.5 3.3 2.3 2.7 Meat, livestock, raw milk 0.1 −0.8 11.6 −0.8 −5.1 0.8 −1.2 0.9 −1.0 0.9 −0.4 0.8 Forest, fish & minerals 1.2 −0.3 1.3 −0.3 0.8 −0.1 0.0 −0.2 0.3 −0.2 −0.3 −0.1 Vegetable oils and fats 2.2 0.4 3.9 0.4 −3.9 2.0 −2.6 1.9 −5.5 1.9 −1.6 2.0 Other processed food −0.6 0.5 0.2 0.5 −1.2 0.6 −3.3 3.1 −8.8 3.1 3.0 0.6 Textile and apparel 3.0 −0.6 3.9 −0.6 0.5 0.0 −1.4 0.2 −1.4 0.2 −0.7 0.0 Manufactures 1.5 −0.7 4.2 −0.7 −1.2 −0.1 −0.2 −0.1 0.6 −0.1 −0.5 −0.1 Utilities and Construction −0.6 −0.8 3.7 −0.8 −1.6 −0.1 −0.4 −0.2 0.6 −0.2 −0.7 −0.1 Transportation and Services 0.0 −0.9 2.9 −0.9 −2.0 −0.1 −0.4 −0.1 0.2 −0.1 −0.5 −0.1 Source: Simulation results.
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Table A4. Average impact on quantities and prices for domestic output, exports and imports for Turkey by commodity and by scenario (in % change). Scenario ‘BASE’ Scenario ‘AGWRLD’
qo ps qxw pxw qiw piw qo ps qxw pxw qiw piw
Paddy rice −11.6 −1.3 13.6 −1.3 13.2 −6.4 −37.8 −3.4 1945.4 −3.4 83.5 0.4 Wheat −3.9 11.0 6.6 11.0 39.5 2.3 −6.4 10.0 34.2 10.0 101.7 4.4 Coarse grains −3.4 19.6 −11.6 19.6 4.8 6.8 −4.0 18.8 35.3 18.8 21.2 7.8 Vegetables, fruits, and nuts −3.5 12.6 −13.0 12.6 10.1 5.8 −4.9 11.5 −17.1 11.5 107.5 6.2 Oilseeds −29.1 44.9 −66.8 44.9 36.8 1.2 −26.7 43.3 −13.6 43.3 42.7 1.7 Sugar cane, Suger beet −3.1 44.5 −74.9 44.5 106.6 10.4 −2.5 43.5 −74.6 43.5 207.9 11.5 Other crops nes −10.6 10.6 −29.0 10.6 10.8 3.2 −16.1 8.5 110.2 8.5 82.0 3.1 Meat, livestock, raw milk −2.7 5.0 −23.4 5.0 9.5 0.9 −2.4 4.0 −22.0 4.0 8.9 0.4 Forest, fish & minerals 0.1 −0.2 0.9 −0.2 0.0 −0.1 0.3 −0.2 1.1 −0.2 0.1 −0.1 Vegetable oils and fats −2.5 1.9 −3.9 1.9 0.4 1.1 −1.9 1.4 −3.1 1.4 −0.1 0.8 Other processed food −2.7 3.4 −9.2 3.4 3.6 0.6 −1.9 1.8 −5.7 1.8 1.7 0.3 Textile and apparel 0.8 −0.2 1.2 −0.2 −0.3 0.0 0.6 −0.4 0.9 −0.4 −0.3 −0.2 Manufactures 0.4 −0.3 1.4 −0.3 −0.7 −0.1 0.6 −0.4 1.9 −0.4 −0.8 −0.1 Utilities and Construction −0.5 −0.3 1.0 −0.3 −1.4 −0.1 −0.5 −0.5 1.6 −0.5 −1.6 −0.1 Transportation and Services −0.6 −0.4 1.0 −0.4 −1.5 −0.1 −0.4 −0.5 1.6 −0.5 −1.6 −0.1
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Table A4. Continued. Scenario ‘AGEU’ Scenario ‘AGMENA’
qo ps qxw pxw qiw piw qo ps qxw pxw qiw piw
Paddy rice 103.9 3.8 28567.3 3.8 30.5 −6.4 −47.3 −3.4 38.3 −3.4 95.4 −7.7 Wheat −4.1 11.2 134.8 11.2 52.4 2.9 −3.9 11.1 7.2 11.1 39.8 2.3 Coarse grains −3.1 19.8 50.9 19.8 9.2 6.9 −3.5 19.6 −10.5 19.6 5.0 6.8 Vegetables, fruits, and nuts −3.1 12.8 −7.7 12.8 42.9 5.7 −3.4 12.6 −10.3 12.6 41.3 6.7 Oilseeds −29.7 44.8 −65.9 44.8 38.3 1.3 −29.0 44.9 −65.5 44.9 36.9 1.2 Sugar cane, Suger beet −3.1 44.7 −75.1 44.7 107.9 10.3 −3.1 44.5 −75.1 44.5 106.8 10.4 Other crops nes −11.1 10.7 −27.5 10.7 14.2 3.2 −10.6 10.6 −28.1 10.6 11.2 3.2 Meat, livestock, raw milk −2.6 5.1 −23.9 5.1 9.8 0.9 −2.7 5.0 −23.4 5.0 9.5 0.9 Forest, fish & minerals 0.1 −0.2 0.9 −0.2 −0.1 −0.1 0.1 −0.2 0.9 −0.2 0.0 −0.1 Vegetable oils and fats −2.4 1.9 −3.7 1.9 0.3 1.1 −2.5 1.9 −3.9 1.9 0.4 1.1 Other processed food −2.7 3.4 −9.2 3.4 3.6 0.6 −2.7 3.4 −9.1 3.4 3.5 0.6 Textile and apparel 0.6 −0.2 1.0 −0.2 −0.4 0.0 0.8 −0.2 1.2 −0.2 −0.3 0.0 Manufactures 0.3 −0.3 1.3 −0.3 −0.7 −0.1 0.4 −0.3 1.4 −0.3 −0.7 −0.1 Utilities and Construction −0.5 −0.3 0.9 −0.3 −1.3 −0.1 −0.5 −0.3 1.0 −0.3 −1.4 −0.1 Transportation and Services −0.6 −0.3 0.8 −0.3 −1.5 −0.1 −0.6 −0.4 1.0 −0.4 −1.5 −0.1 Source: Simulation results.
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Table A5. Average import tariff and taxation on firms’ domestic and import purchases by commodity (% ad valorem rate).
Import tariffs (% ad valorem rate) Taxes on firms’ imports purchases (% ad valorem
rate) Taxes on firms’ domestic purchases (% ad
valorem rate)
Turkey (1) Morocco (2) Ratio (3) = (2)/(1) Turkey (1) Morocco (2) Ratio (3) = (2)/(1) Turkey (1) Morocco (2) Ratio (3) = (2)/(1)
Rice, paddy 8.3 4.2 0.5 −0.9 0.00 0.00 −0.25 0.00 0.00 Wheat 8.8 31.4 3.6 −0.5 0.00 0.00 −0.16 0.00 0.00 Coarse grains 15.1 42.6 2.8 −0.5 0.00 0.00 −0.19 0.00 0.00 Vegetable & Fruits 36.7 38.6 1.1 −0.2 0.00 0.00 −0.25 0.00 0.00 Oilseeds 8.4 19.0 2.3 −1.3 0.00 0.00 −0.14 0.00 0.00 Sugar crops 1.3 2.0 1.6 0.0 0.00 n.a. −0.78 0.00 0.00 Other crops 21.8 15.7 0.7 −1.5 0.00 0.00 −0.32 0.00 0.00 Meat, Livestock, Milk 16.8 37.5 2.2 −0.3 0.00 0.00 −1.66 0.00 0.00 Extraction 0.7 14.1 19.8 11.2 0.00 0.00 2.24 0.03 0.01 Vegetable oil 13.4 16.3 1.2 0.0 0.00 0.00 −0.01 0.00 0.00 Other proc food 27.7 37.2 1.3 −0.2 0.00 0.00 −0.01 0.00 0.00 Textile & Apparel 5.2 28.2 5.4 0.2 0.00 0.00 −1.60 0.00 0.00 Manufactures 2.3 17.2 7.6 11.7 0.00 0.00 39.63 0.00 0.00 Utilities & Construction 0.0 0.0 n.a. 8.5 0.00 0.00 14.11 0.00 0.00 Services 0.0 0.0 n.a. 0.0 0.02 n.a. −1.60 0.00 0.00 Source: Authors’ adaptation (Data source: GTAP database version 7).
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- Abstract
- 1. Introduction
- 2. Methodological approach, scenarios and data discussion
- 2.1. Modelling framework: the GTAP model
- 2.2. Climate-induced yield shocks: data sources discussion
- 2.3. A descriptive analysis of projected yield impacts
- 3. Global welfare and macroeconomic impacts analysis
- 4. Analysis of results for Morocco
- 5. Analysis of results for Turkey
- 6. Conclusions
- Notes
- Acknowledgements
- Disclaimer
- Disclosure statement
- ORCID
- References
- Appendix
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20211126015618dellink_et_al_2017.pdf
Please cite this paper as:
Dellink, R. et al. (2017), “International trade consequences of climate change”, OECD Trade and Environment Working Papers, 2017/01, OECD Publishing, Paris. http://dx.doi.org/10.1787/9f446180-en
OECD Trade and Environment Working Papers 2017/01
International trade consequences of climate change
Rob Dellink, Hyunjeong Hwang, Elisa Lanzi, Jean Chateau
JEL Classification: C68, F17, F18, O44, Q56
THE INTERNATIONAL TRADE CONSEQUENCES OF CLIMATE CHANGE– 1
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2 – THE INTERNATIONAL TRADE CONSEQUENCES OF CLIMATE CHANGE
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Abstract
The International Trade Consequences of Climate Change
Rob Dellink, Hyunjeong Hwang, Elisa Lanzi and Jean Chateau (OECD)
This report provides an analysis of how climate change damages may affect international trade in the coming decades and how international trade can help limit the costs of climate change. It analyses the impacts of climate change on trade considering both direct effects on infrastructure and transport routes and the indirect economic impacts resulting from changes in endowments and production. A qualitative analysis with a literature review is used to present the direct effects of climate change. The indirect impacts of climate change damages on trade are analysed with the OECD’s ENV-Linkages model, a dynamic computable general equilibrium model with global coverage and sector-specific international trade flows. By building on the analysis in the OECD (2015) report “The Economic Consequences of Climate Change”, the modelling analysis presents a plausible scenario of future socioeconomic developments and climate damages, to shed light on the mechanisms at work in explaining how climate change will affect trade.
The report highlights the important regional differences in the effects that climate change will have on regional and sectoral economic activities and on competitiveness. Consequently, international trade changes are governed not by domestic climate impacts only, but also by the relative severity of these impacts compared to the major trading partners. By being aware of how climate impacts may affect its economy, not just through impacts on its production factors but also on trade, countries can design climate and trade policies that are aligned and thus avoid the worst climate damages at least cost.
JEL classification: C68, F17, F18, O44, Q56.
Keywords: Trade and environment, Trade and climate change, CGE model
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Résumé
Ce rapport analyse dans quelle mesure les coûts résultants du changement climatique pourraient altérer le commerce international dans les décennies à venir, ainsi que le rôle du commerce international dans la réduction des coûts liés au changement climatique. Plus précisément, le rapport étudie les effets directs du changement climatique sur les infrastructures et les voies de transport, mais aussi les effets économiques indirects résultant des impacts du changement climatique sur les activités et les facteurs de production. Les effets directs du changement climatique sur le commerce international sont appréhendés de façon qualitative par le biais d’une revue de la littérature existante sur ce sujet. Les effets indirects sont analysés à l’aide du modèle ENV-Linkages de l’OCDE. Ce modèle d’équilibre général calculable est dynamique, mondial et intègre directement le commerce international des biens et services. L’analyse quantitative repose sur celle effectuée dans le rapport de l’OCDE(2015) « Les conséquences économiques du changement climatique », et présente un scénario plausible des tendances socio-économiques et du changement climatique dans les décennies à venir. Ce scénario illustre notamment les mécanismes à l’œuvre, qui expliquent les impacts du changement climatique sur le commerce.
Le rapport met en avant les fortes disparités, entre régions et secteurs d’activité, des effets du changement climatique, qui à leur tour modifient leur compétitivité relative. Par conséquent les changements de la structure du commerce international sont non seulement régis par les impacts domestiques du changement climatique, mais aussi par le différentiel d’impacts entre partenaires commerciaux. Ce n’est seulement qu’en tenant compte de la façon dont les impacts climatiques peuvent affecter leurs économies, non seulement au travers d’effets sur leur production mais aussi sur leur commerce, que les pays pourront élaborer de concert des politiques climatiques et commerciales qui permettront d’éviter un renforcement des impacts négatifs du changement climatique.
Classification JEL : C68, F17, F18, O44, Q56.
Mots clés : Commerce international et environnement, changement climatique et commerce, modèle MEGC.
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Acknowledgements
This report on “The international trade consequences of climate change” explores
the potential economic consequences of climate change with a specific focus on international trade.
The quantitative analysis in this paper builds heavily on the OECD CIRCLE project, especially the 2015 report “The economic consequences of climate change”. It extends beyond that analysis by diving much deeper into the consequences for macroeconomic competitiveness and changes in international trade patterns; part of the paper focuses on agricultural damages, as these are both significantly affected by climate change and heavily traded on international markets.
This report was written by Rob Dellink, Hyunjeong Hwang, Elisa Lanzi and Jean Chateau of the OECD Environment Directorate. The report was overseen by the Joint Working Party on Trade and Environment (JWPTE). The paper has benefitted from comments on earlier versions by delegates of the JWPTE and the Joint Working Party on Agriculture and Environment (JWPAE). Shunta Yamaguchi’s feedbacks on earlier drafts and coordination of the review process have been essential in preparing this report. Comments and suggestions from colleagues at the OECD Secretariat, not least Andrew Prag from the Environment Directorate and Guillaume Gruère, Jehan Sauvage, Ronald Steenblik from the Trade and Agriculture Directorate, are gratefully acknowledged. Natasha Cline-Thomas and Marie-Jeanne Gaffard provided editorial assistance. Work on this paper was conducted under the overall responsibility of Shardul Agrawala, Head of the Environment and Economy Integration Division.
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Table of contents
Abstract .................................................................................................................................................... 2
Résumé ..................................................................................................................................................... 3
Acknowledgements .................................................................................................................................. 4
Acronyms and Abbreviations .................................................................................................................. 7
Executive Summary ................................................................................................................................. 8
1. Introduction ........................................................................................................................................ 10
2. The Evolution of International Trade in the Coming Decades .......................................................... 12
2.1 Evolution of regional economic activity and pressure on the climate system .............................. 12 2.2 Evolution of international trade flows .......................................................................................... 14
3. Impacts of Climate Change on Domestic Economies and International Trade ................................. 18
3.1 The direct impacts of climate change on international trade ........................................................ 18 3.2 The indirect consequences of climate change on international trade ........................................... 23
3.2.1 The regional economic consequences of climate change ...................................................... 23 3.2.2 Changes in trade patterns due to climate change impacts ...................................................... 28
4. Understanding the Indirect Impacts of Climate Change on International Trade ............................... 32
4.1 Income effect: changes in macroeconomic competitiveness of countries .................................... 33 4.2 Compositional effects: changes in comparative advantage in agriculture and food .................... 37
4.2.1 Macroeconomic consequences of agricultural impacts ......................................................... 37 4.2.2 Revealed Comparative Advantage (RCA) in food products .................................................. 40 4.2.3 A deeper look at RCAs: food exports to the EU .................................................................... 43
4.3 Sensitivity of domestic consequences to international spillovers ................................................ 47
5. Concluding Remarks .......................................................................................................................... 51
Annex A. Description of the ENV-Linkages Modelling Tool ............................................................... 53
Annex B. Details on the Evolution of International Trade in the No-damage Baseline Projection ....... 58
Changes in consumption patterns ....................................................................................................... 58 Changes in production patterns .......................................................................................................... 59 Changes in trade specialisation .......................................................................................................... 62
Annex C. Summary of the Approach to Represent Damages from Climate Change in the Model ....... 64
Annex D. Key Simulation Results for 25 Regions ................................................................................ 66
References .............................................................................................................................................. 68
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Tables Table 1. Geographical distribution of trade in the no-damage baseline projection ......................... 16 Table 2. Potential direct impacts and consequences on trade infrastructures ................................. 20 Table A.1. Sectoral aggregation of ENV-Linkages ........................................................................ 56 Table A.2. Regional aggregation of ENV-Linkages ....................................................................... 57 Table C.1. Climate impact categories included in ENV-Linkages ................................................. 65 Table D.1. Regional results for the climate damages scenario ....................................................... 66 Table D.2. Regional results for the agricultural damages scenario ................................................. 67 Figures Figure 1. Trend in real GDP in the no-damage baseline projection ................................................. 12 Figure 2. Evolution of key climate change indicators in the no-damage baseline projection .......... 14 Figure 3. Bilateral trade between OECD and non-OECD countries in the no-damage baseline
projection .......................................................................................................................... 17 Figure 4. Regional damages from selected climate change impacts in the climate damages scenario
........................................................................................................................................... 24 Figure 5. Sources of damages from selected climate change impacts by production factor in the
climate damages scenario .................................................................................................. 26 Figure 6. Impact of climate change on yields for selected crops in the climate damages scenario .. 28 Figure 7. Changes in trade volumes from climate impacts in the climate damages scenario .......... 29 Figure 8. Change in export shares in the no-damage baseline projection and in the climate damages
scenario ............................................................................................................................. 30 Figure 9. Change in trade volumes and in GDP for a range of climate damages scenarios ............. 31 Figure 10. Change in trade volumes and in GDP in the climate damages scenario ....................... 34 Figure 11. Change in real exchange rates in the climate damages scenario ................................... 35 Figure 12. Change in sectoral imports in the climate damages scenario ........................................ 36 Figure 13. Change in aggregate crop yields and GDP in the agricultural damages scenario ......... 38 Figure 14. Change in regional GDP in different agricultural damages scenarios .......................... 40 Figure 15. RCA levels for food products and changes due to agricultural damages ...................... 41 Figure 16. Changes in RCAs for food products and agricultural damages .................................... 42 Figure 17. Change in food exports to the EU in the agricultural damages scenario ...................... 45 Figure 18. Levels and change in RCA of food exports to the EU in the agricultural damages
scenario ......................................................................................................................... 46 Figure 19. Decomposition of changes in real GDP in 2060 in the climate damages scenario ....... 49 Figure A.1. Production structure of a generic sector in ENV-Linkages ........................................... 54 Figure B.1. Changes in sectoral composition of world trade ........................................................... 58 Figure B.2. Changes in consumption patterns, selected countries ................................................... 60 Figure B.3. Changes in industrial structure, selected countries ....................................................... 61 Figure B.4. Changes in trade specialisation patterns in selected aggregate industries..................... 62 Boxes Box 1. Uncertainty on the projections .................................................................................................. 31 Box 2. The Revealed Comparative Advantage (RCA) ......................................................................... 33 Box 3. Modelling assumptions used for the decomposition ................................................................. 48
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Acronyms and Abbreviations
CGE Computable General Equilibrium
CO2 Carbon dioxide
ECS Equilibrium Climate Sensitivity
FAO Food and Agriculture Organization of the United Nations
GHG Greenhouse Gases
GDP Gross Domestic Product
GTAP Global Trade Analysis Project
IAM Integrated Assessment Model
IEA International Energy Agency
IFPRI International Food Policy Research Institute
IMO International Maritime Organisation
NSR Northern Sea Route
NWP Northwest Passage
IPCC Intergovernmental Panel on Climate Change
PPP Purchasing Power Parity
RCA Revealed Comparative Advantage
SIC International Standard Industrial Classification
WTO World Trade Organization
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Executive Summary
Over the next half century, international trade is projected to continue to outpace growth in global gross domestic product (GDP). While economies will increasingly rely on trade, climate change will affect trade patterns and specialisation. Changes in the climate system, not least sea level rise and the increasing frequency of extreme events, will modify transport routes and infrastructures, thereby changing the access and possibilities for the international transport of goods and services. Other types of climate impacts, such as those on agriculture and labour productivity, will cause changes in production and specialisation, which will also affect trade.
The literature on trade has focused mostly on the trade consequences of climate change mitigation policies or on the effects of trade policies on greenhouse gas emissions. Dedicated analyses that look at the long-term impacts of climate change on international trade are still very scarce.
This paper provides an analysis of how climate change damages will affect international trade in the coming decades and how international trade can help limit the costs of climate change. It analyses the impacts of climate change on trade considering both direct effects on infrastructure and transport routes and the indirect impacts resulting from changes in endowments and production. A qualitative analysis with a literature review is used to present the direct effects of climate change. The indirect impacts of climate change damages on trade are instead analysed with the OECD’s ENV-Linkages model, a dynamic computable general equilibrium model with global coverage and sector-specific international trade flows. By building on the analysis in OECD (2015a), the modelling analysis limits itself to presenting one plausible scenario of future developments, to shed light on the mechanisms at work in explaining how climate change will affect trade
The direct consequences of climate change on trade could become manifest in damages to trade from more frequent extreme weather events or rising sea levels. Supply, transport and distribution chains might become more vulnerable to disruptions due to climate change. Maritime shipping, which accounts for around 80% of global trade by volume, could experience negative consequences, for instance from more frequent port closures due to extreme events. At the same time there could also be positive economic impacts on maritime shipping through the potential further opening of Arctic shipping routes, albeit at the cost of environmental degradation.
Indirect impacts on trade patterns primarily result from regional and sectoral disparities in the economic consequences of climate change. Model simulations show that climate change is projected to impact on the production of goods and services through changes in natural endowments or through the efficiency with which factors of production of such as land, labour, and capital can be deployed.
The effects of climate change on trade lead to changes in the comparative advantage of economies, and hence affect trade patterns. The results from the ENV- Linkages model simulations imply that climate damages will place negative pressure on the economies of almost all regions through smaller trade flows than in a projection that ignores feedbacks from climate change on the economy. Notwithstanding this,
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significant growth in baseline trade volumes projected over the coming decades will still see the absolute level of trade flows grow even when climate damages are accounted for. The economic consequences of climate change are especially marked in Africa and Asia, where high economic growth rates are combined with increased trade dependency and large damages from climate change. In terms of economic sectors, trade in agricultural commodities is projected to be relatively strongly impacted by climate damages.
The results of this study show that in the most affected countries exports are projected to decline more than imports and GDP and this will weaken their trade position. In contrast, producers in the least affected countries can improve their competitive position on both domestic and export markets. Therefore, despite being negatively affected by climate damages, a region may increase its competitiveness if other competitors for a certain market are more severely damaged, or there is a move to specialise in the production of other goods.
Focusing on the impact of agricultural damages from climate change on food products, and using Revealed Comparative Advantage (RCA) as an indicator of regional competitiveness, this paper finds that while the ranking of comparative advantage is largely unchanged by climate change damages on the agricultural sector, there are significant effects for some countries. The effects are particularly large for the regions that are most specialised in food and agricultural products.
The regional changes in comparative advantage are driven by complex interactions in the economic system, where all sectors in all regions are intricately tied together and where climate damages affect all parts of the economy. Countries that have larger domestic markets and more diversified trade patterns can absorb climate shocks better than countries that are more specialised. There are numerous interactions between regions and sectors that make it impossible to establish rules of thumb on the competitiveness impacts of climate damages, but it is clear that the relative impacts in a region compared to its trading partners matter more than the absolute size of the regional damages. This highlights the need for each region to understand not only the direct impacts of climate change on its sectoral production and trade flows, but also the possible impacts of climate change on regions it is competing with for specific markets.
This paper only presents results from one single model and baseline. More robust quantitative insights would require a more elaborate modelling analysis, using multiple scenarios on the major modelling assumptions, and ideally comparing different models. Nonetheless, the paper highlights the important effects that climate change will have on economic activities and on competitiveness. By being aware of how climate impacts may affect its economy, not just through impacts on its production factors but also on trade, countries can design climate and trade policies that are aligned and thus avoid the worst climate damages at least cost.
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1. Introduction
The atmospheric effects from continued greenhouse gases (GHG) emissions will lead to changes in the climate system (IPCC, 2013). Higher global surface temperatures and changed weather patterns are projected to accelerate the melting of glaciers, lead to rising sea levels, and to result in more frequent temperature extremes and longer-lasting heat weaves in certain parts of the world, among other effects. These impacts will have significant economic consequences for regions around the world, with large changes in sectoral and regional production and consumption (OECD, 2015a) and hence on international trade.
Recent analysis by the OECD projects that the trend of economic integration and intensified global trade will continue in the future, albeit at a slower pace than in the last decades (Chateau et al., 2015). In the long run, global trade and its relative size to global income are driven by (i) transportation and communications costs (including “transaction costs”), (ii) income growth and changes in preferences, (iii) sectoral comparative advantage in production of goods and services, and (iv) trade policies and trade agreements (Feenstra, 1998). Changes in each country’s specialisation depend ultimately on differences in these drivers amongst countries. The products in which countries specialise are determined by the availability of inputs used in the manufacture of different products and by access to different technologies. As such, specialisation is strongly driven by unevenly distributed natural resources across the globe.
Climate change will affect some of these elements, thereby changing trade and specialisation patterns through different mechanisms. Some climate impacts, such as higher frequency of extreme events or rising sea levels, will have direct impacts on trade as they will affect transport and distribution chains. Further, changes in factors of production of economies (i.e. land, labour, and capital) will affect production structure and trade specialisation. But, climate change is also expected to have indirect impacts on trade, as all regions and sectors are linked through inputs in production and trade in produced goods and services.
On the policy side, mitigation policies may affect trade. Similarly, trade policies may also impact GHG emissions. Both topics have been dealt with in the literature (OECD, 2007, 2008, 2009; Copeland and Taylor, 2004; Cosbey and Tarasofsky, 2007; WTO-UNEP, 2009). However, dedicated analyses that look at the long-term impacts of climate change on international trade, and at how international trade affects the economic consequences of climate change impacts, are still very scarce. Huang et al. (2011) summarise the key mechanisms at play in the consequences of climate change on trade in agriculture, and highlight the different roles of changes in technology and changes in endowments as drivers of changes in international trade patterns. Willenbockel (2012) investigates the consequences of extreme weather events on food prices and changes in international trade. He shows how regional productivity shocks can have widely varying impacts on food prices, export prices and export volumes. Bosello and Parrado (2014) show that the economic consequences of climate change depend on the possibilities to adapt international trade patterns. Schenker and Stephan (2014) explicitly look at the impacts of international climate policy on international trade. They find that funding adaptation in developing regions can reduce climate
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change costs as developing regions benefit from receiving adaptation funding and high and middle income donor countries will generally benefit from improved terms- of-trade. Liu et al. (2014) study the role of trade in analysing the impacts of possible future irrigation shortfalls. They find that regional differences in impacts of irrigation water shortages on local production significantly alter the geographical distribution of international trade.
This paper specifically aims to shed light on how climate change damages will affect international trade in the coming decades, and on how international trade affects climate change costs. The focus of the analysis is on the assessment of the costs of inaction, i.e. the economic consequences that are projected to occur when no further policy action is taken. Thus, an analysis of the trade policy response to climate change is left for future research. It first surveys the direct effects of climate change on the trade infrastructure. Then, the indirect impacts resulting from the economic consequences of climate change will be investigated in detail.1 While it is clear that climate policies will have profound effects on different sectors and economies, and on international trade patterns, an analysis of the trade consequences of climate change policies is left for future research. Such analysis could also investigate the trade consequences of international financial flows as part of a multilateral climate agreement.
The analysis of sectoral and regional economic changes in this paper relies on a dynamic computable general equilibrium modelling tool – the OECD’s ENV-Linkages model – to draw global economic scenarios up to 2060. These scenarios can be used to analyse the linkage between trade and climate (see Annex A for a brief description of the model and Chateau et al., 2014, for more details). This multi-regional and multi- sectoral dynamic general equilibrium model has been recently enhanced to consider the impact of climate damages on the economy (OECD, 2015a). CGE models are traditionally well-suited to the type of analysis in this report, as they focus on linkages between economic sectors in various regions. This type of model is based on national accounts and international trade flows at sectoral level. The paper focuses on climate change impacts on trade of goods and services among countries, as opposed to capital flows and labour migration, both important issues but outside the scope of this paper. An important caveat is that the use of one central projection of economic developments with one specific assessment of the impacts of climate change implies that the quantitative results presented in this paper are mostly indicative. More robust quantitative insights would be gained from studying multiple scenarios and comparing different models, and by adopting a risk-based framework. This would, however, imply a major additional effort.
This paper is structured as follows. Section 2 presents a projection of world trade and specialisation patterns in the coming decades, as projected by ENV-Linkages, without considering how these trends are affected by climate change. Section 3 then summarises the main direct impacts of climate change and presents results on indirect impacts as quantified in the model. Section 4 discusses how these changes in trade flows can be explained by the different mechanisms that drive trade patterns, including macroeconomic competitiveness and relative comparative advantage at the sectoral level. Section 5 provides some concluding remarks.
1 Changes in trade patterns may also have a feedback effect on the climate as emissions will
likely change in the future as centres of production relocate and shipping routes change. This topic is however outside the scope of this report.
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2. The Evolution of International Trade in the Coming Decades
2.1 Evolution of regional economic activity and pressure on the climate system
The ENV-Linkages model projects developments of economic activity at the sectoral and regional level until 2060 (see Annex A for further details on the model structure). Sectoral economic activity is projected using a production function for economic sectors, a utility function for households and international trade flows, with macroeconomic closure, i.e. all commodity flows have an origin and a destination, and are coupled to a reverse financial flow.
Based on a number of exogenous socioeconomic trends concerning population growth, demographic changes, and technological developments, the model projects economic activity, pressure on the climate system and international trade patterns in the coming decades. Figure 1 shows the projected evolution of regional GDP and trade along the no-damage baseline projection. This baseline projection does not contain environmental feedbacks and is detailed in OECD (2015a).
Figure 1. Trend in real GDP in the no-damage baseline projection Panel A. Evolution over time
(Billions of USD, 2010 PPP exchange rates)
0
50 000
100 000
150 000
200 000
250 000
300 000
2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060
Rest of Europe & Asia South and South-East Asia Sub Saharan Africa Latin America Middle East & North Africa OECD Pacific OECD Europe OECD America
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Panel B. Growth in GDP and exports (Average annual growth rate)
Source: OECD (2015a) based on OECD (2014) for OECD countries and ENV-Linkages model for non-OECD countries.
Over the next half century, world GDP is projected to grow on average around 2.5% per year, with declining rates in many countries in the last 20 year of the period. The trend GDP growth for the OECD region is projected at about 1.8% annually until 2060, and growth in emerging economies will continue to outpace the OECD, but the difference will narrow over coming decades as income levels in emerging economies catch up to those in the OECD. Near the middle of the century fast growth in Africa is expected to be the prime source of global economic growth. As a result, the next 50 years will see major changes in country or region shares in global GDP. The faster growth rates in emerging and developing economies imply that the combined GDP of present non-OECD economies are projected to account for around 70% of world GDP in 2060 versus 50% in 2015.
Despite slowdowns in the growth rates of both population and GDP, the shift in economic significance to emerging and developing economies, and – in the absence of new climate policies – unabated use of fossil fuels lead to a sharp increase in GHG emissions. In particular, the increased consumption of coal accelerates increases in emissions. Nonetheless, there is some relative decoupling: emissions grow less rapidly than production. This is caused not least by energy efficiency improvements in many countries. This relative decoupling occurs in many countries, and at the global level, but the strength of this effect varies widely between countries. Global anthropogenic greenhouse gas (GHG) emissions (excl. emissions from land use, land-use change and forestry, which are treated exogenously) are projected to rise from around 45 Gigatonnes (Gt) of CO2 equivalent (CO2e) in 2010 to around 95 GtCO2e in 2060 (Figure 2, top left panel). Carbon dioxide (CO2) is projected to remain the dominant greenhouse gas. The rapid increase in GHG emissions accelerates climate change. Concentrations of CO2 in the atmosphere rise from 390 parts per million (ppm) to 590 ppm between 2010 and 2060 (Figure 2, top right panel). These concentration levels, plus forcing from other GHGs and aerosols lead to an increase in total radiative forcing from anthropogenic sources from just over 2 to almost 5 Watts per square meter (W/m2) (Figure 2, bottom left panel). The central projection delivers temperature increases of more than 2.5°C by 2060 above the pre-industrial level
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
World
Non-OECD
OECD
World
Non-OECD
OECD
Ex po
rts (v
ol um
e) G
DP (v
ol um
e)
percentages
2015-2040 2040-2060 2015-2060
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(Figure 2, bottom right panel), although there is substantial uncertainty on the temperature changes implied by these carbon concentrations and radiative forcing (Box 1 in Section 3.2.2 discusses these uncertainties in more detail).
Figure 2. Evolution of key climate change indicators in the no-damage baseline projection
Source: ENV-Linkages model and MAGICC6.4 Model (Meinshausen et al., 2011).
2.2 Evolution of international trade flows The projection of changes in international trade patterns is a core element of the
ENV-Linkages model. A central assumption on the representation of international trade in the model is the so-called Armington assumption: domestic and foreign goods and services are considered to be imperfect substitutes. This approach, which is common in CGE models, can mimic plausible levels of bilateral trade by differentiating the price of each good across countries. The model abstracts from an explicit representation of international capital markets, and instead assumes specific pathways for regional current account balances. This latter assumption implies that regional trade balances follow an exogenous path and real exchange rates will adjust in each period to reproduce these balances, and thus maintain model closure.2
2 It is a common assumption in CGE models to decouple international capital markets from
international goods markets. In this context current accounts are exogenously given. Hence, the real exchange rates are the “macro” variables that equilibrate trade balance constraints and there are no financial variables in the model. The baseline real exchanges rates of emerging and developing economies progressively increase relative to those of OECD, reflecting a Balassa- Samuelson effect. This effect comes from high productivity growth in sectors that produce tradable goods, which will in turn drive wage increases in the slower growing non-tradable sectors. Hence, domestic price levels increase, and thus also relative prices vis-a-vis other countries, i.e. the real exchange rate.
0 10 20 30 40 50 60 70 80 90
100
2010 2020 2030 2040 2050 2060
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CO2 Kyoto GHGs
0 100 200 300 400 500 600 700 800
2010 2020 2030 2040 2050 2060
Concentrations (ppmv)
CO2 Kyoto GHGs
0
1
2
3
4
5
6
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Anthrop. radiative forcing (W/m2)
0
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1
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3
2010 2020 2030 2040 2050 2060
Temperature increase (°C)
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In line with the long-term economic projections presented in Chateau et al. (2015), growth in trade (gross exports of goods and services) is projected to continue to outpace GDP growth over the next 45 years. The projected global trade-to-GDP elasticity is assumed to be around 1.2 for all goods and services (1.35 for goods and 1.15 for services), over the whole period. Thus, although trade is projected to increase more rapidly than income, this assumption could be seen as low relative in comparison to the historical values of 1.6 for goods between 1950-2009 or the projected value of 1.4 for goods for 2012-2060 presented in the context of the OECD@100 project (Chateau et al., 2015).3 The more conservative approach adopted in this report is in line with the more pessimistic outlook for international trade in the Economic Outlook of November 2015.
In terms of geographical distribution, large shifts in trade patterns are projected, reflecting among other things uneven developments in income across the globe as well as changes in comparative advantage (Table 1). The People’s Republic of China (hereafter China) and India are projected to gain market shares in world trade over the next half century. Likewise, Africa, Indonesia and other Asian economies are projected to experience sizeable increases in trade shares, especially after 2040, reflecting rapid growth leading to larger economic size combined with low production costs. These gains in trade shares of emerging and developing economies are mostly at the expense of European Union and OECD Asia, while some other OECD regions observe similar relative reductions in their trade shares.4 Contrarily, some other OECD economies, including the United States, Mexico and Australia & New Zealand, are projected to see their trade shares relatively constant over the period.
3 Technically two assumptions done in the baseline construction explain our conservative view
about the future trade to GDP elasticity: firstly in this report and contrarily to the OECD@100 projection no new trade policies and agreements are assumed after 2010, secondly the baseline assumes only small changes will occur in transaction costs for manufacturing goods in non- OECD countries.
4 These declining shares in exports follow a declining share of these regions in global GDP. They reflect a growth in exports that is slower than in other regions, rather than declining absolute export volumes over time.
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Table 1. Geographical distribution of trade in the no-damage baseline projection
(Regional gross exports as share of world exports)
2015 2040 2060 European Union 34% 26% 21% People's Republic of China 13% 19% 18% USA 11% 10% 10% OECD Asia 8% 6% 5% Other ASEAN countries 6% 7% 8% Middle East & North African 5% 5% 5% Other OECD 4% 3% 3% Other Asia 3% 4% 5% Other Europe 3% 2% 2% Canada 3% 2% 2% Other Latin America 2% 2% 3% Sub-Saharan Africa 2% 4% 7% Mexico 2% 2% 2% India 1% 3% 4% Australia & New Zealand 1% 2% 2% Indonesia 1% 1% 2% Brazil 1% 1% 1% Caspian region 1% 1% 1%
Source: OECD ENV-Linkages model.
The changing geographical distribution of trade is also featured by changes in the relative importance of trading partners (Figure 3). The expected shift of wealth creation from OECD to non-OECD countries will have important implications for trade patterns. While currently about half of total trade flows in bilateral terms took place within the OECD area, the share of bilateral trade among OECD members is expected to nearly halve by 2060. Instead, by 2060 trade among non-OECD economies is projected to more than double, to account for approximately one-third of global trade. The growing share of non-OECD countries in world GDP is one driver for this, but it is also because non-OECD countries will progressively adopt more similar production structures to those in the OECD, so that they can trade between each other instead of trading with OECD partners.
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Figure 3. Bilateral trade between OECD and non-OECD countries in the no-damage baseline projection
(Regional gross exports at FOB prices as share of world exports)
Source: OECD ENV-Linkages model.
Changes in sectoral trade patterns are driven by differences in income growth, but also by convergence in consumption patterns. As shown in Annex B, the baseline scenario will project a convergence in consumption patterns across countries that imply, among other things, a large shift away from consumption of food and necessary products towards services in emerging economies. Convergence in consumption patterns is projected to follow convergence in income levels. For production, there is more differentiation between countries in terms of access to technologies, factor endowments and productivity levels of production. Hence, production patterns evolve more slowly, and international trade patterns adjust to equate demand and supply in all regions. As a consequence, most OECD countries are projected to lose market shares for almost all goods (see also Figure B.4 in Annex B), while non-OECD East-Asian countries and African countries are projected to gain market shares in manufacturing goods and Latin American countries in textiles and food products (but not necessarily in raw agricultural goods).
OECD to OECD 21%
OECD to RoW 25%
RoW to OECD 26%
RoW to RoW 28%
2060
OECD to OECD 46%
OECD to RoW 18%
RoW to OECD 21%
RoW to RoW 15%
2010
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3. Impacts of Climate Change on Domestic Economies and International Trade
The physical impacts of climate change will have direct as well as indirect consequences for trade. Direct effects encompass the effects of climate change on trade-relevant supply, transport and distribution chains, which could become manifest in damages to trade infrastructure such as ports from more frequent extreme weather events or rising sea level. Other impacts, such as retreat of polar ice under warmer temperatures can lead to opening up new trade routes in the Arctic. Indirect impacts for trade will primarily result from the impact of climate change on the production of goods and services through changes to the factors of production of economies, i.e. land, labour, and capital. Both direct and indirect effects of climate change on trade will likely lead to changes to the comparative advantage of economies, hence trade flows and patterns.
3.1 The direct impacts of climate change on international trade
Climate change will impact trade through a number of channels, not all of which can be easily quantified. This section outlines some of the main impacts, based on a brief review of the literature. One prominent explanation for the rise in international trade in the last decades was a decline in international transportation costs (Hummels, 2007). One key direct effect of climate change is that supply, transport and distribution chains might become more vulnerable to disruptions due to climate change, thereby affecting future international trade patterns. Extreme weather events, for instance, may lead to the temporary shutdown of ports and transport routes; they might also damage infrastructure critical to trade and thus have longer-lasting effects. These and other interruptions can lead to delays, increase the costs of international trade and could lead to a shift in trade patterns as companies involved in trade seek alternatives to increase reliability of shipping (WTO, 2009).
Although the literature on the link between climate change and trade is limited and mostly qualitative, there is high agreement among experts that climate change will on balance negatively affect transport infrastructure. According to reports surveyed by the IPCC (2014), climate change will affect all forms of transport relevant for international trade, including seaborne transportation, land-based transport modes, and aviation. There is only a small amount of research that points to the potential positive consequences of climate change on trade infrastructure, and supply, transport and distribution chains (Hansen et al. (2016), Heininen et al. (2015), Liu et al (2010), Maddocks et al. (2010)).5
Trade-relevant impacts to land-based transportation from climate change may become manifest in faster degradation of road and bridge infrastructure, and shorter
5 All these literatures are on the Artic shipping.
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availability of transport routes through permafrost zones (IPCC, 2014). Bridges will be particularly prone to damage from sea level rise and changes in long-term flow regimes if authorities do not encourage necessary investments in adaptation. In the United States, for example, engineers typically design bridges to endure storms that have a historical probability of occurring only once or twice every 100 years. However, past climatic observations may no longer reliably predict future impacts due to climate change. Extreme weather events, including storms, may take place every 50 or even 20 years by the end of the century if global warming continues (IPCC, 2012). In addition, heat stress and a higher number of freeze thaw cycles may accelerate the degradation of paved roads. Higher temperatures will likely contribute to the melting of permafrost, shortening the availability of transportation routes through zones of cryotic soil (WTO, 2009; IPCC, 2014).
Airborne transport of goods for international trade might be impacted by climate change, for instance through damage to or impairment of the operations of airports. Research suggests that sea level rise, increased storminess, and extreme precipitation induced by climate change can affect the operations of airports, lead to more frequent disturbances, and affect infrastructures in weather-exposed or low-lying areas. Higher temperatures may also reduce aircraft lift, making airports adapt runways and air companies to change aircraft types or maximum payload with climate change. To address climate change from international flight emissions, which contributes about 2% of globally produced CO2 and accounts for 13% of fossil fuels consumed by transport (IPCC, 2007), the International Civil Aviation Organization (ICAO) 6 has initiated movements toward strengthening technology standards and market-based instruments – such as a levy or a cap-and-trade scheme based on GHG emissions.7 It is expected that this may result in an additional financial burden on aviation transport.
Maritime shipping, which accounts for around 80% of global trade by volume and more than 70% of global trade by value (UNCTAD, 2014), could also experience some negative consequences from climate change. Increased storms, increased precipitation, and sea level rise may cause more frequent port closure, affect speed of passage, necessitate the use of alternative shipping routes or additional safety measures, and increase the maintenance costs for ships and ports (IPCC, 2014). Dependent on location, physical impacts from climate change might also affect future inland navigation.
6 The ICAO is the primary UN body responsible for regulating civil aviation. 7 www.icao.int/Pages/default.aspx
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Table 2. Potential direct impacts and consequences on trade infrastructures
Climate change effect Mode Direct impact Consequences on trade infrastructure Increased temperature and solar radiation
Land-based Road pavement cracking; Asphalt rattling; Rail buckling; Loss of water seal causing potholing
Require more frequent maintenance (-) Require track and road repairs or speed restrictions to avoid derailments (-) Higher maintenance and insurance costs (-)
Aviation Reduced life of asphalt on airport tarmacs; Reduced airlift capacity
Need to construct longer runways to compensate for reduced airlift (-); Need for ground-cooling mechanisms (-) Higher maintenance and insurance costs (-)
Sea-based Reduced refrigeration storage period Increase refrigeration costs (-) Increased precipitation and river floods
Land-based Flooding of land infrastructures; River bridge scour; Wet pavements and safety risks
Need to re-route to avoid climate change– affected roads (-); Higher maintenance and insurance costs (-)
Aviation Flooding of runways and access roads; Reduced visibility; Damage facilities including airstrips;
Higher maintenance costs and insurance costs (-)
Sea-based Reduced capabilities in loading/uploading of cargo at ports; Increased rates of corrosion / oxidation equipment
Risk of delays (-); Increased construction and maintenance costs (-)
Sea level rise and sea storm surges
Land-based Permanent or temporary inundation; Submerge of bridges
Risk of delays (-); Higher maintenance and insurance costs (-)
Aviation Submerge of terminals and villages Relocation and migration of people and business (-)
Sea-based Lower clearance under waterway bridges; Damage to port infrastructure; Increased rates of corrosion and oxidation equipment
Need for new ship design (-); Need for reconfiguration of operational areas (-); Higher maintenance costs and repair of port facilities (-)
Extreme weather conditions Land-based Disturbance to transport electronic infrastructures, signalling, etc.
Disruption to operations (-); Higher maintenance and insurance costs (-)
Aviation Disturbance to transport electronic infrastructures, signalling, etc.
Risk of delays; (-); Higher maintenance and insurance costs (-)
Sea-based Temporary shutdown of ports; Deterioration of sailing conditions; Disturbance to transport electronic infrastructures, signalling, etc.
Risk of delays (-); Higher maintenance and insurance costs (-)
Reduced Arctic sea ice cover
Sea-based Opening of Arctic shipping routes
Reduced distances and time (+); Need for additional navigation aids such as ice-breakers for ships using the Arctic route (-); Higher insurance costs for ships using the Arctic route (-)
Source: OECD based on Race (2015), UNCTAD (2014), Maddocks et al. (2010).
At the same time the loss of the Arctic ice cap will open up new possibilities for maritime transportation in the Arctic. One high-profile example is the potential further opening of Arctic shipping routes, including the Northeast Passage, the Northwest Passage, and the Transpolar Sea Route, for longer periods. Given that the Arctic ice is melting at a rapid rate, a growing number of papers find that reduced ice cover would permit ships with light icebreakers access to pretty much anywhere in the Arctic Ocean by 2040. 8
8 The main Arctic shipping routes are the North East Passage (NEP), the North-West Passage (NWP), and
the Transpolar Sea Route (TSR). These are currently seasonal sea routes which has ice-free period only for summer. During summer, the North East Passage and North West Passage are easily navigable whereas the Transpolar Sea Route is navigable only with powerful icebreakers.
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This implies that the availability of Arctic shipping paths could lead to distance reduction for the relevant bilateral trade routes. Such distance reduction can have significant implications on international trade patterns. Shorter sailing distances allow for time savings and result in cost savings. Transportation cost is an important factor to determine the trade pattern, and in turn the transportation cost is determined by variables such as distance, time, trade volume and vessel size, competition, infrastructure, and piracy and other risk (OECD, 2011). Among such factors affecting the transportation costs, distance has been regarded as one of the most important determinants. Many studies and literature confirmed this “distance decay” – the volume of trade declines as the distance between two countries increases, reflecting transportation costs of increased freight costs and increased length of transit.
For trade between Europe and Asia, the conventional sea route is mainly the Suez Canal Route, which connects the Mediterranean and the Eastern Asia. The emerging alternative through the Arctic region is the Northern Sea Route (NSR), which is also called as Northeast Passage. Bekkers et al. (2015) analyses that the northern route would reduce the distance from Japan to northern European countries by 37%, from Korea by 31%, China 23%, and Chinese Taipei 17%. The countries in Europe that will gain most from the new sea route are those with access to ports on the North Sea and the Baltic. For South Asian countries and southern European countries, the conventional southern route will still be shorter. For trade between America and Asia, the traditional route is via the Panama Canal and the emerging alternative is the Northwest Passage (NWP). As an alternative to the traditional route, the distance savings achieved by navigating the NWP are close to 20% for most of the large ports located in North Eastern Asia (Hansen et al., 2016).
If the new sea route becomes a viable alternative for large portions of the year, world trade patterns may alter, benefiting northern countries, and potentially causing a reduction in revenues for the current main trade routes such as the Suez Canal. Bekkers et al. (2015) investigate the hypothetical extreme scenario in which the arctic route becomes fully operational all year around, and project that roughly 8% of world trade goes through the Suez Canal, and that two-thirds of this volume could potentially go via the shorter Arctic route if that becomes permanently available. The northern route would then become one of the busiest shipping lanes in the world, increasing the economic and political importance of the Arctic. At the same time, it will put economic pressure on countries that benefit from shipping that uses the southern route, but also some countries in eastern and southern Europe would experience a drop in trade because of the comparatively longer distances their exports and imports would need to travel. Over time, the opening of the Arctic route may have positive indirect effects on jobs and prosperity in all the countries concerned, but it is predicted that this will be a gradual rather than sudden process.
However, given that commercial use of Arctic shipping routes depends not only on distances but also on a number of other factors, distance advantage does not guarantee the fully viable commercial use of Arctic shipping routes in the near future. The shorter distances could attract time-sensitive cargoes, but on the other hand, other factors may outweigh the benefits.
There are a number of factors that caution against the hypothetical scenarios reviewed in the preceding paragraphs. Ships operating in the Arctic are exposed to unique risks. The most significant barrier is the transport logistic obstacles. These include underdeveloped communication systems, insufficient navigational aids, the need to use icebreakers, limited commercial weather forecasts, patchy search and
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rescue capabilities, scarcity of relief ports along the route, reduced sailing speeds, poorer fuel economy, detours, and damage to ships (Humpert and Raspotnik, 2012). These conditions increase the insurance premium and costs, thereby limiting the commercial viability of shipping operation.
At the same time, there has been an increasing concern about the vulnerability of Arctic ecosystems due to the potential further opening of Arctic shipping routes. Arctic shipping is among the greatest threats to biodiversity in the Arctic. Even if it is potentially beneficial for the economy, it may have detrimental effects on the environment. New opportunities for arctic shipping due to ice-melting present many threats for the regional environment and biodiversity, if not properly managed. First, it may increase emissions to air. Studies show that the increase of pollutants such as black carbon (BC), particulate matter, nitrogen oxide (NOx), carbon monoxide (CO) and sulphur oxide (SOx) may have significant regional effects potentially affecting human and environmental health in the Arctic Area (AMSA, 2009). BC emissions – the result of incomplete combustion of fossil fuels and biomass – are of particular concern, in spite of their short atmospheric lifetimes, as they accelerate snowmelt and sea ice loss by reducing the albedo of snow and ice.9 Second, it may increase release of oil through spills or operational or illegal discharges. The release of oil into the Arctic environment could have short and long-term consequences on marine life, given that some Arctic animals are sensitive to oil (Arctic Yearbook, 2015). The Arctic environment is particularly vulnerable to the heavy fuel oil, which accounts for three-quarters of the fuel used in Arctic shipping (AMAP, 2013).10 Third, international shipping can also be an important vector in introducing invasive alien marine species, including through hull fouling and discharge of ballast water (Bax et al, 2003). These species are a major threat to Arctic ecosystems, both for flora and wildlife. Besides these significant challenges, increased waste, sound and noise disturbances, vessel collisions with marine mammals are also threats to Arctic environment and biodiversity.
Faced with growing concerns about the vulnerability of Arctic ecosystems to increasing traffic11, the International Maritime Organisation (IMO) formally adopted the new International Code of Safety for Ships Operating in Polar Waters (the Polar Code) by in May 2015.12 Reflecting the need for a high degree of environmental protection, the Polar Code includes much stricter regulations for Arctic shipping such as mandatory requirements for ship design, crew training, barriers to separate fuel tanks from ships’ outer hulls, and a limit on discharge of sewage (ABS, 2016).13 These
9 According to the Arctic Council’s Arctic Monitoring and Assessment Programme, although
shipping is currently contributing only 5% of the black carbon load in the Arctic, this amount could quadruple by 2050.
10 “It degrades slowly under Arctic conditions, the evaporation and dispersion rates are low compared to lighter, refined fuels, it may emulsify once released into the marine environment, and it is impossible to clean up in ice covered conditions and with a lack of nearby response resources and infrastructure. It has a devastating effect on marine life, particularly as Arctic marine food webs are so simple.” (Arctic Yearbook, 2015, p. 392).
11 In the Arctic, approximately 2,000 vessels currently operate, and the number is likely to grow as ice melts.
12 The code is expected to enter into force on 1 January 2017. 13 However, heavy fuel oil – which has been regarded as one of the biggest threats to the Arctic
climate – is not banned under the Polar Code.
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new requirements would be another factor that may affect the net economic gains resulting from shortened transit route on Arctic shipping routes.
Several issues on direct impacts of climate change on trade require further clarification. With uncertainties on the pace and extent of the logistical barriers, the lack of infrastructure, harsh weather conditions, short winter days, and on how melting ice may affect the stability of the Arctic climate, it is difficult to predict how large an effect Arctic shipping may have on international trade. Furthermore, infrastructure in developing countries may become more climate-resilient in the future as a result of international development support, not least when donor mainstream climate considerations in their development assistance. These remain key areas for further analysis.
3.2 The indirect consequences of climate change on international trade
3.2.1 The regional economic consequences of climate change14 The report The Economic Consequences of Climate Change (OECD, 2015a)
provides a detailed global quantitative assessment of the costs of inaction on climate change. It presents the projected macroeconomic and sectoral economic consequences of climate change (i.e. climate damages) in absence of new climate policies, for a selected number of impacts: changes in crop yields, loss of land and capital due to sea level rise, changes in fisheries catches, capital damages from hurricanes, labour productivity changes and changes in healthcare expenditures from diseases and heat stress, changes in tourism flows, and changes in energy demand for cooling and heating. As mentioned above, due to a lack of data, this analysis does not include any of the direct impacts on trade and infrastructure discussed in Section 3.1, although qualitatively it is clear that e.g. increased freight costs from climate impacts will imply higher trade costs and therefore affects international trade patterns. A full discussion of the modelling assumptions is given in OECD (2015a); a summary is provided in Annex C.15 Here the main modelling results are summarised, to provide background for the analysis in the next section.
The modelling assessment suggests that market damages from the selected set of impacts are projected to gradually increase over time and rise faster than global economic activity. If no further climate change action will be undertaken, the combined effect of the selected impacts (in the climate damages scenario) on global annual GDP are projected to rise over time to likely levels of 1.0% to 3.3% by 2060, with a central projection of 2% (Figure 4).16 This range reflects uncertainty in the equilibrium climate sensitivity (ECS) – a measure indicating how sensitive the earth’s climate reacts to a doubling of atmospheric CO2 – using a likely range of 1.5°C to 4.5°C (see Box 1 in the next subsection) and a central projection of 3°C. Assuming a wider range of 1°C to 6°C in the ECS, GDP losses could amount to 0.6% to 4.4% in 2060.
14 This section draws heavily on Chapter 2 of OECD (2015a). 15 OECD (2015a) also highlights that there are numerous important impacts of climate change
which could not be included in the modelling analysis and provides the broader context that surrounds these simulations, and the logic for ambitious policy action.
16 Annex D presents some key results presented in this Section at the more disaggregated 25 region.
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The caveats on uncertainties in and incompleteness of these projections notwithstanding, the macroeconomic projections are well-aligned with the literature on quantified economic damages.17 The latest report of Working Group II of the Intergovernmental Panel on Climate Change (IPCC, 2014) surveyed the existing literature and found “global aggregate economic losses between 0.2 and 2.0% of income (“medium evidence, medium agreement”, Ch. 10) for a temperature increase of 2.5°C (this is not linked to a specific date). In the central projection of ENV- Linkages, this threshold is reached just before 2060. Given the relatively large variety of impacts included in this analysis, it is not surprising that the GDP losses projected here are at the higher end of the range provided by the IPCC.
Figure 4. Regional damages from selected climate change impacts in the climate damages scenario (Percentage change in GDP w.r.t. no-damage baseline)
Panel A. Evolution over time
17 Although there are significant differences between the modelling approach and calibration used
here and earlier economic studies of climate damages, similar patterns emerge in e.g. Nordhaus (2007; 2011), Eboli et al. (2010), Bosello et al. (2012), Roson and Van der Mensbrugghe (2012), Bosello and Parrado (2014) and Ciscar et al. (2014). In these studies, global impacts are increasing more than proportionately with temperature increases (and hence over time) and amount to reductions of several percent of GDP by the end of the century. Highest impacts are foreseen in emerging and developing countries, especially in South and South-East Asia and Africa, whereas countries at a high latitude in the Northern hemisphere, especially Russia, may be able to reap some economic benefits from the climatic changes. Studies that focus on a specific region tend to show larger negative impacts on the local economy, but by nature ignore the endogenous adjustment processes that take place within economies, and changes in international trade patterns.
-7%
-6%
-5%
-4%
-3%
-2%
-1%
0%
2010 2020 2030 2040 2050 2060
South & South-East Asia
OECD Pacific
Rest of Europe & Asia
OECD Europe
Latin America
OECD America
World
Sub-Saharan Africa
Middle East & North Africa
uncertainty ranges in 2060 due to uncertainty in ECS
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Panel B. Attribution of global damages to different impacts
Source: OECD ENV-Linkages model.
Some sectors are directly impacted by specific climate impacts (e.g. services sectors are affected by health impacts, energy sectors by energy demand impacts). However, there are also substantial indirect effects that are induced by the full range of price changes that follow climate impacts. For example, impacts on the energy demands affect energy prices and thus induce changes in production in energy- intensive industrial sectors. As another example, capital destruction from sea-level rise affects all sectors through changes in the marginal productivity of capital. Of the impacts modelled in the analysis, changes in crop yields and in health (labour productivity) are projected to have the largest negative consequences on the macro economy, causing loss to annual global GDP of 0.9% and 0.8%, respectively, by 2060 for the central projection of the climate damages scenario (panel B).18
The GDP impacts of climate change damages as projected with the ENV-Linkages model can also be decomposed into changes in each specific primary factor of production. Climate impacts may directly affect labour, capital, land and natural resources. Figure 5 shows the decomposition of GDP losses according to production factor, with shading indicating the direct changes in value added of a production factor. These direct effects have been calculated by multiplying the percentage change in productivity with supply of these production factors at their no-damage baseline levels of use, i.e. before any endogenous market adaptation effects. The indirect effects (hatched in Figure 5) are then calculated as the difference between the total effect and the direct effect.
18 Including a CO2 fertilisation effect reduces the agricultural damages to 0.6%, and the effect is
projected to be especially strong in Africa (reducing agricultural damages from 1.5% to 1.0% by 2060 in Sub-Saharan Africa); see Section 4.2.1. Such effects are excluded from the analysis here.
2035 2060
Coastal Zones Energy Demand Extreme Precipitation Events Health Tourism Demand Agriculture
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Figure 5. Sources of damages from selected climate change impacts by production factor in the climate damages scenario
(Percentage change in GDP in 2060 w.r.t. no-damage baseline)
Note: Plain areas denote direct effects and hatched areas the associated indirect effects. Source: OECD ENV-Linkages model.
In the model, total labour supply is assumed to be fixed, and total land supply is not very flexible and hence direct effects more or less directly translate into GDP loses, although sectoral reallocation can still affect their overall contribution to GDP. The reduction of value added from natural resources in South and South-East Asia is attributed to the decline in production of a number of resource-dependant sectors, which is induced by the changes elsewhere in the economy.
For capital, the situation is different, as its supply is flexible in the long run, since consumers can adjust their savings patterns in response to changes in the economic situation. Thus, there is an additional effect, as changes in income levels affect savings and hence future capital accumulation. Thus, the climate impacts not only affect the level of GDP, but also the growth rate, through reduced capital accumulation. As can be inferred from Figure 5, capital losses are substantially larger than the other factor losses, and this can be attributed to these indirect economic effects. At the global level, almost half of the projected GDP loss of 2% can be attributed to the indirect effects on capital, which may be interpreted as growth effects. In other words, by 2060 the projected economic consequences on GDP levels and on GDP growth are of similar size. This implies that logically, the longer-term consequences of climate change are substantially worse than the short- and medium-term consequences, unless new sources of economic growth can be found.
Adverse impacts of climate change will affect the production of all commodities of the economy, including those that are heavily traded internationally. Agricultural products may be particularly affected by climate change through increase in temperature and more frequent heat extremes. Further, changes in precipitation will in most regions likely lead to significant reductions in crop yields and hence, decreased crop output. For specific crops in specific regions, relatively small temperature
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increases, combined with increased rainfall, may benefit crop production. In order to examine the extent to which economies will be affected by the adverse impact of climate change on crop yields, Figure 6 illustrates the projected changes in yields in the climate damages scenario compared with the baseline scenario in 2060.19 The pure climate shocks on yields implemented in the model are calculated using the climate shocks of the IMPACT model for the AGMIP study (Von Lampe et al., 2014; Wiebe et al., 2015).20 Note that the effective change in crops yields tends to be smaller than the pure climate shocks: farmers can change their production process and to adapt to the pure climatic shocks on yields, and will do so in order to minimise their costs, i.e. market-driven adaptation is endogenously handled inside the economic modelling framework. These effects have been included in Figure 6. But the modelling framework excludes the possibility to increase the fraction of agricultural land that is irrigated. In regions with sufficient water supply for irrigation, this adaptation option can be an important part of the response to climate change (Ignaciuk and Mason- D’Croz, 2014). Still, it is excluded here as markets forces alone are usually insufficient to achieve large-scale expansion of irrigated areas (Ignaciuk, 2015).
19 To avoid drastic changes in percentage terms when the baseline production level is very small,
changes in yields are only shown for production volumes that exceed 1 percent of global production.
20 These exogenous shocks are in most cases negative, such as for wheat in North America, but are occasionally positive due to improved rainfall patterns and milder temperatures, such as for rice in the OECD Asian countries; see also Figure 1.2 in OECD (2015a).
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Figure 6. Impact of climate change on yields for selected crops in the climate damages scenario (Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
3.2.2 Changes in trade patterns due to climate change impacts The economy-wide and sectoral consequences of climate changes discussed in the
previous section have important implications for trade and specialisation across countries. To highlight this, this section compares the baseline with no climate change impacts trade projection with the scenario with climate damages.
The volume of international trade is projected to be affected by climate change to more or less the same extent as global GDP. Figure 7 indicates that world exports may decrease by 1.8% in 2060, relative to the baseline without climate damage, while global imports and GDP would be reduced by 1.6% (expressed in 2010 USD using PPP exchange rates). At the global level, the decline in exports is larger than that of imports, as both are measured in different prices (FOB and CIF, respectively).21 In principle, one could expect that increased trade flows are necessary to compensate for production losses in the most affected economies. However, as indicated by the GDP losses, there is a global contraction of final demands (compared to the no-damage
21 In the model, volumes are expressed in constant 2010 USD; over time the price indexes for
export and imports will deviate, as only flows in current dollars are balanced at the world level.
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baseline), and given the imperfect substitution between domestic and foreign goods and services, this will imply a reduction in both production and trade.
Regional shifts in trade patterns also differ: the African and Asian countries that are most affected by climate impacts (Figure 4) are also those that are projected to record growing importance in world trade over the next 50 years (see Table 1 and Figure 3). Exports contract more than imports especially in India and Sub-Saharan Africa, as their domestic production is severely hit by climate change. In contrast, Canada and the Other Europe region can increase their export volumes. The drivers for these changes are investigated in detail in Section 4.
Figure 7. Changes in trade volumes from climate impacts in the climate damages scenario (Percentage change in 2060 w.r.t. no-damage baseline)
Panel A. By region Panel B. By sector
Source: OECD ENV-Linkages model.
GDP losses are not the only channel through which climate change would impact international trade. The differences in impacts of climate damage on economic sectors also translate in changes in the composition of trade. As indicated in Panel B of Figure 7, trade in agricultural commodities is projected to be relatively strongly
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impacted by the negative climate damages, not least through the direct impact on crop yields. This hides some major differences between various crops: the staple goods rice and wheat are projected to be more traded in response to climate change, while trade in other agricultural crops declines (see Section 4.2.1 for more details). Indirectly food product trade is also significantly affected as – at least in value terms – food is substantially more traded than its primary components, namely ‘raw’ agricultural products.22
These sectoral and regional changes in trade flows are also reflected in changes in global export market shares. Figure 8 presents a country’s potential export share under the baseline scenario as linked to the change in export share in 2060 under the climate change. Countries on the left-hand side are projected to lose export market share in 2060 without climate change, with in the case of Middle East & North Africa climate change projected to lead to even further decline. Generally, those regions that increase their export share in the baseline see a reduction in export shares from climate damages. An explanation is that these regions depend strongly on trade as a source of economic growth, leaving them vulnerable to shocks that negatively affect trade opportunities.
Figure 8. Change in export shares in the no-damage baseline projection and in the climate damages scenario
(Percentage change)
Source: OECD ENV-Linkages model.
22 In the model, processed food is categorised as food products, not as crops. Hence, the food
products category includes meat, milk, vegetable oils, processed rice, sugar, other food, beverages and tobacco products.
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Box 1. Uncertainty on the projections The numerical results presented in this report are surrounded by significant uncertainties. The equilibrium
climate sensitivity (ECS) reflects the equilibrium climate response, i.e. the long-run global average temperature increase, from a doubling in carbon concentrations, and is often used to represent the major uncertainties in the climate system in a stylised way. In Figure 9, the ECS is varied between 1.5°C and 4.5°C in the likely uncertainty range, and between 1°C and 6°C in the wider uncertainty range, in line with the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (Rogelj et al., 2012; IPCC, 2013). The global temperature increase by 2060 that is associated with the likely uncertainty range on the ECS equals 1.6 to 3.6°C, while the larger range is 1.1 to 4.3°C.
Figure 9. Change in trade volumes and in GDP for a range of climate damages scenarios (Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
The blue bars in Figure 9 indicate how much regional damages may fluctuate in the likely range, while the thin black lines highlight that the impacts may be considerably larger (or smaller) when the wider uncertainty range is considered. Thus, by 2060, global annual GDP losses for the likely ECS range are 1.0% to 3.3%, but the possibility that global losses from the selected impacts covered in the model are as low as 0.6% or as high as 4.4% cannot be excluded. Changes in export levels follow a similar pattern for most regions, but the downside uncertainties tend to be amplified. A key difference is that a potential increase in GDP hardly translates into a similar increase in exports; given the global loss in international trade volumes, this is not surprising.
The panel clearly shows that the regional differences in GDP losses in the central projection are relatively small when compared to the uncertainties within a region related to different climate sensitivities. Perhaps equally importantly, the model analysis shows that even at low levels of climate sensitivity there will be significant (albeit smaller) GDP and export losses in many countries.
A more detailed analysis of how uncertainty on the ECS affects the assessment of the macroeconomic consequences of climate change is given in OECD (2015a). The approach to measuring the uncertainties surrounding the numerical results is too crude to reliable present uncertainty margins on the more detailed sectoral results. Nonetheless, these uncertainties should be kept in mind when interpreting the results of the analysis of the central projection in Section 4.
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4. Understanding the Indirect Impacts of Climate Change on International Trade
As mentioned above, the indirect consequences of climate change on international trade patterns can be understood by looking at 4 key channels: (i) changes in transportation costs; (ii) changes in macroeconomic competitiveness (the macroeconomic channel); (iii) changes in comparative advantage at the sectoral level (the sectoral channel); and (iv) changes in policies.
This section focuses on two of these main channels of trade impacts: the macroeconomic channel of the income effect, and the sectoral channels of the compositional effects. Changes in international transportation costs were reviewed separately in Section 3.1, but are not included in the modelling exercise. On balance, Section 3.1 and the wider literature suggest that by 2060 the quantitative effects of direct trade impacts are relatively minor, especially in comparison with the significant indirect trade impacts of climate change, although for specific climate events in specific regions, temporary trade disruptions can be very significant.
Changes in policies, as an endogenous response to the projected macroeconomic and trade consequences of climate change, are explicitly excluded in the analysis. While it may well be the case that countries react to large changes in trade flows and losses to their domestic economies by revising their trade and other policies, these are not easily predicted. An analysis of the role of trade flexibility in responding to climate change impacts, and the interactions between trade policies (incl. trade liberalisation) and climate policies deserves a separate study.23 By excluding an endogenous policy response, the analysis here boils down to an assessment of the costs of inaction. This can then serve as a basis for assessing the benefits of policy action.
There are no direct measures of competitiveness and comparative advantage. For a macroeconomic analysis, changes in trade flows can be linked to a range of macroeconomic variables, including GDP and exchange rates. At the sectoral level, Revealed Comparative Advantage (RCA) is a common technique for providing information on the relative advantage or disadvantage of a country in the supply of certain goods or services on international markets (Box 2).
23 For this study, the assumptions relative to trade policies are very conservative in the ENV-
Linkages baseline projection. No new trade policies or trade agreements are implemented after 2010: tariff rates as well as export tax rates are assumed to stay constant over the horizon. In similar spirit, support to production (in agriculture and energy) that could be seen as indirect subsidies to domestic production are also kept constant, relative to the tax-basis. The only change in policies that is implemented in the baseline are energy and carbon policies as covered by the IEA in its “Current Policies Scenarios” presented in the World Energy Outlook 2013 (IEA, 2013).
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Box 2. The Revealed Comparative Advantage (RCA)
An RCA indicator can be used to show more clearly how changes to factors of production (induced by climate change damages in this case) affect gains and losses from trade that countries derive from domestic factor endowments. RCA is defined as the share of a region’s exports of a set of commodities in the region’s total exports relative to the share of the world’s exports of these commodities in global exports. Technically,
i,j 'all',j
i,'all' 'all','all'
x X X X
ij ij i
ij ij j ij
x x RCA
x x ≡ ≡
∑ ∑ ∑
where xij denotes exports of product i by country j and ‘all’ refers to the relevant group of all products (i.e.
all those that are exported) or all regions (i.e. all those that are exporting product i).
4.1 Income effect: changes in macroeconomic competitiveness of countries
Figure 10 shows that changes in GDP are generally well-aligned with the overall volume changes in trade at the macro level: countries whose national income deteriorates from climate impacts will scale down not only domestic economic activity, but also the volume of trade, both for imports and exports.24
For the regions most affected by climate damages, not least India and Sub-Saharan Africa, exports are projected to contract more than GDP. Given the strong impacts of climate change on these regions (cf. Section 3), their production costs increase much more than those of their trading partners such that their macroeconomic competitiveness will decline.25 As import changes are essentially driven by income change, import reductions in these regions are very close to GDP reduction. In these countries the large drop in domestic production is partially compensated through increasing the import share, in order to keep domestic consumption as little affected as possible.
In contrast, regions whose macroeconomy is less affected by climate change (in this case regions with GDP losses of less than 1% in 2060), can increase their competitive position on their domestic market, i.e. import shares decline and imports fall more than GDP. At the same time, these regions have lower losses (or higher gains) for exports than for imports, which is indicative of their increased competitive position on the international market.
24 Annex D presents some key results presented in this Section at the more disaggregated 25
region. 25 Two other regions that are substantially affected by climate change, Other Asia and the Middle
East, defy this trend and have relatively small impacts on exports. These two regions are characterised by very particular specialisation patterns which partially shelters them against trade impacts.
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Figure 10. Change in trade volumes and in GDP in the climate damages scenario
Note: y-axis presents percentage change in exports and imports, respectively w.r.t. no-damage baseline.
Source: OECD ENV-Linkages model.
Given the assumption of exogenous trade balances in the model, changes in trade flows alone cannot show all the mechanisms at work.26 An important role in adapting to the climate impacts is through adjustments of the real exchange rate, which adjust endogenously to correct any imbalance in trade flows relative to the reference projection. Figure 11 highlights that in general large GDP losses are associated with strong increases in the real exchange rate vis-a-vis the United States. Increases in the real exchange rate in the worst affected countries imply a degradation of their competiveness, or in other words that their exports become more expensive relative to international prices. These results confirm the insights from Figure 8 in Section 3.2: those regions that have strong increases in export volumes in the no-damage baseline (India, Sub-Saharan Africa), have relatively strong degradation of their competitiveness, both in terms of export volumes and exchange rates; countries at the other side of the spectrum, and especially Other Europe, have contracting export shares in the no-damage baseline projection, and much smaller effects of climate damages.
26 The alternative assumption that trade balances adjust in response to climate impacts, instead of
adjusting exchange rates, may influence the quantitative results but would not reverse the general insights.
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Figure 11. Change in real exchange rates in the climate damages scenario (Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
Changes in sectoral import patterns are determined by a combination of macroeconomic and sectoral effects. As illustrated in Figure 12, the total change in sectoral imports could be decomposed in three components. First, sectoral imports depend on the size of the economy; this income effect is calculated as the change in imports due to changes in GDP. Second, total imports in volume as a share of real GDP may adjust to the new equilibrium; this macro trade effect is defined as the change in total imports minus the change in GDP. Finally, sector-specific effects will lead to adjustments of the sectoral composition of imports. In volumes, for each region these sectoral effects add up to zero across sectors.
With the exception of Canada, OECD Asia and the EU, total import volumes follow GDP, and the income effect is larger than the macro trade effect. In most cases, the macro trade effect is of the opposite sign as the income effect, reflecting the mechanism outlined above that regions adjust their imports to compensate for changes in domestic production costs. Given that agricultural trade is a relatively small share of overall imports, a large percentage change in the sectoral effect for agriculture combined with relatively minor opposite changes in the other sectors imply small changes in GDP.
For the aggregate agricultural sector, the sectoral component dominates in almost all regions: changes in agricultural imports are predominantly determined by climate change impacts on the sector itself, not by changes in the macro economy. It is striking to see that the sign of the sectoral effects varies widely across regions. Several regions import more to compensate for the smaller domestic production, including Australia and New Zealand, Brazil, Sub-Saharan Africa and, most prominently, the Middle East
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and North Africa region (where the agricultural sector effect amounts to 6.6 percent, but from a relatively small baseline level). In these regions, the increase in imports for some crops is larger than the decrease in imports for other crops.
Figure 12. Change in sectoral imports in the climate damages scenario (Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
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A similar pattern of less imports of some crops and more imports of others occurs in almost all regions, but in most cases overall agricultural imports decline compared to the baseline. All countries balance the increased domestic production costs (from reduced yields) with the increased world price for agricultural commodities. In order to minimise the consequences of the climate change impacts on welfare, countries change their specialisation in crop production to accommodate the changes in relative comparative advantage, revise their domestic consumption levels to reflect changes in relative prices between all consumption goods, and adjust their imports accordingly.27 This signifies that a detailed sectoral and regional analysis is warranted to explain what happens to sectoral trade flows. The next section will investigate these sectoral effects in more detail, with a focus on how agricultural trade is affected by agricultural impacts of climate change (while Figure 12 shows the consequences from the whole set of climate impacts, including those on agriculture, labour productivity and others).
4.2 Compositional effects: changes in comparative advantage in agriculture and food
The previous section has illustrated the main macroeconomic effects of climate change on trade. This section instead focuses on sectoral and compositional effects by studying changes in comparative advantage. Many effects interact in the model both in terms of sectoral changes and consequences of the different impacts. This makes it hard to analyse the impacts on all sectors and from all climate damages together. As a case study, this section therefore focuses on climate change impacts on crop yields and their impacts on trade in food products. Throughout this section, “agriculture” refers to the land-based production of crops and livestock, while “food” refers to the processed commodities derived from these. Thus, the former includes e.g. wheat and cows, the latter bread, meat and dairy products. The food processing sector depends crucially in the inputs of the agricultural sectors; while it is not directly severely affected by climate change, the indirect effects from the impacts on agriculture are very significant.
4.2.1 Macroeconomic consequences of agricultural impacts The analysis in this section focuses on changes in agricultural exports, and
specifically food products (which includes all processed foods, as noted above). In order to clarify the main mechanisms at work, this analysis is carried out with a simulation in which only agricultural impacts from climate change are included, and the other impacts, such as those on labour productivity, are excluded.
The macroeconomic consequences from considering agricultural impacts only are logically more modest than those of the full set of market damages. In terms of yield shocks, the largest losses are projected to be in Brazil and the Asian regions, especially India (cf. x-axis in Panel A of Figure 13).28 Especially in Asia and Africa, the yield losses translate into reductions in GDP (y-axis in panel A of Figure 13). These results are fairly similar to the macroeconomic consequences of the full set of market damages, as discussed in Section 3.2. In particular, macroeconomic consequences are only very loosely related to the yield shocks imposed on the regional economies.
27 Furthermore, given the modelling assumption of fixed regional trade balances, countries
balance total imports and total exports. 28 Annex D presents some key results presented in this Section at the more disaggregated 25
region.
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Panel B highlights that there are large differences in the consequences of agricultural damages for the volume of trade of selected commodities.29 Especially trade in rice is projected to increase significantly, as some of the main rice consumers are severely hit by climate change. In contrast, some of the other agricultural commodities get less traded internationally; consequently, aggregate trade of agricultural commodities slightly reduces below the no-damage baseline level. Trade in food products declines by 3%, more than the decline in overall trade (which amounts to around 0.6%). The reduction in aggregate trade of agricultural commodities can be explained by a mixture of factors, including the relatively strong reduction in yields compared to the no-damage baseline projection, reduced demand for crops by the food sector, the general contraction of the economy (also implying lower incomes), and the need to meet domestic food demands.
Figure 13. Change in aggregate crop yields and GDP in the agricultural damages scenario (Percentage change in 2060 w.r.t. no-damage baseline)
Panel A. Aggregate crop yields versus GDP at the regional level
29 These changes in trade flows are measured in terms of monetary export volumes. Changes in
aggregate monetary import volumes differ somewhat from these, as import prices differ between regions and changes across regions are hence differently weighed.
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Panel B. Global traded volumes for selected commodities
Source: OECD ENV-Linkages model.
It is worthwhile to highlight that this central projection represents only one scenario, and different assumptions on e.g. the regional climate impacts (especially precipitation) would lead to different numerical results. Figure 14, reproduced from OECD (2015a), shows the full range of possible macroeconomic consequences from agricultural damages from varying the underlying climate model, underlying crop model, and the assumption on the effect of increased carbon concentrations on crop growth (the carbon fertilisation effect).30 These alternative specifications are not further explored in this report, as the analysis serves mostly as a case study to highlight the key mechanisms at work, not as a prediction of changes in agricultural and food trade.
30 Although at this level of aggregation all scenarios lead to GDP losses for all regions, this masks
the potential gains for specific scenarios that are projected in specific countries, such as Brazil, Chile and Russia in 2060, and more widespread potential gains in earlier decades for some scenarios.
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Figure 14. Change in regional GDP in different agricultural damages scenarios (Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
4.2.2 Revealed Comparative Advantage (RCA) in food products Figure 15 shows the change in Revealed Comparative Advantage (RCA) for the
baseline and the “agricultural damages” scenarios. It also shows the change in RCA between the two scenarios (diamonds in the figure). This figure shows, first of all, which regions have a strong specialisation and comparative advantage in food products. Brazil has the highest comparative advantage in both scenarios. A group of other regions, including the Other Latin America region, Indonesia, the Other Europe region, the ASEAN 9 region and Sub-Saharan Africa also have high comparative advantage in food products. Regions with smaller comparative advantage include Australia and New Zealand, Canada, Mexico, Middle East and North Africa, the EU, India and Other OECD countries. Finally some regions, namely the USA, other OECD and non-OECD Asian regions, China and the Caspian regions, do not specialise in trade of food products.31
This distribution of comparative advantage is largely unchanged by climate change damages to the agricultural sector. However, climate change damages lead to changes in RCA for several regions and for some of the most specialised in the sector. Brazil remains the country with highest RCA and actually increases its comparative advantage with the highest increase among all regions. Smaller increases also take
31 This does not necessarily imply that these regions do not export large volumes of food
products, as specialisation is about the share of a specific sector (in this case food products) in total exports.
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place in most other regions, and particularly in Europe, Australia and New Zealand and in North America. The South and South East Asia region is most damaged in its comparative advantage, especially Indonesia and India. The Middle East and North Africa region also loses comparative advantage in food products.
Figure 15. RCA levels for food products and changes due to agricultural damages
Source: OECD ENV-Linkages model.
Changes in crop yields due to climate change are one of the drivers for these changes in comparative advantage. However, as all sectors and regions are linked with each other, a complex set of interactions and endogenous changes is triggered by the yield shocks, leading to adjustments in all sectors of all economies. Considering the correlation between RCA and crop yield changes, as illustrated in Figure 16, panel A, it is clear that crop yield changes alone cannot explain the changes in RCA. For instance, although Brazil and Indonesia have a similar loss in crop yields, Brazil gains in competitiveness, while Indonesia loses part of its competitiveness.
Panel B related the changes in RCAs to changes in the prices and volumes of exports. Those countries that have the largest increase in export prices see the largest drop in export volumes. As expected, the combined effect of these, i.e. changes in food export revenues, does show a clear link with the change in RCA: for those countries where the negative volume effect dominates, the RCA goes down, while for those where the volume effect is positive, the change in RCA is positive. But at the level of individual regions, more complex interactions play a role, and the regional changes in RCAs and export revenues cannot be fully explained at this aggregation level.
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In order to better understand the interaction between trade flow changes, RCAs and competitiveness, it is therefore necessary to look more specifically at trade markets and competition amongst regions for a specific market. This is done in the next section.
Figure 16. Changes in RCAs for food products and agricultural damages (Percentage change in 2060 w.r.t. no-damage baseline)
Panel A. Changes in RCAs and crop yields
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Panel B. Changes in RCAs, export prices and volumes
Source: OECD ENV-Linkages model.
4.2.3 A deeper look at RCAs: food exports to the EU Given that many complex linkages between the trade flows of regions exist, and
there are multiple drivers of trade changes, the overall changes in RCA do not say much. Rather, in order to shed further light on the linkages between GDP losses, changes in trade flows and comparative advantage, one must dive deeper into the model and focus on changes in more specific trade flows. Therefore, this subsection looks at the exports of food products (i.e. not raw agricultural commodities, but the output of the food industry) to the EU, and tries to shed light on the mechanisms at work. This specific case is chosen because Europe is one of the main importers and its main partners are regions that will be confronted with large changes in their RCA.
Figure 17, panel A presents the projected size of exports of food products to the EU by region of origin. The most important trading partner is projected to be Sub- Saharan Africa: 21% in 2060, up from 13% in 2010 (not shown in the figure), to a large extent at the expense of imports from other OECD countries. The growth in exports from Sub-Saharan Africa to the EU is stronger in absence of climate impacts on agricultural production in Sub-Saharan Africa, but even when agricultural damages are accounted for, these exports increase significantly. This is primarily driven by the projected strong increases in agricultural productivity in Sub-Saharan Africa in the baseline (in line with the larger literature, cf. Alexandratos and Bruinsma, 2012, Ignaciuk and Mason-D’Croz, 2014 and Sulser et al., 2015), and the fact that extensive trade links already exist between Europe and many African countries. This implies that the region can simultaneously increase crop production for domestic consumption (to improve food security) and increase food exports. The baseline improvement in yields outweighs the negative impact of climate change, so Sub-Saharan Africa can
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significantly improve its yields compared to 2015 levels even in the agricultural damages scenario; this effect is stronger than in other regions. Furthermore, given the relative abundance of land in many Sub-Saharan African countries, the continent has relatively large possibilities to partially absorb yield shocks by increasing the cultivated area. Finally, one should remember that this analysis is done for the combined group of Sub-Saharan African countries. Within this group, there will be significant heterogeneity, where some countries will struggle with food security, while others export large amounts of food to the European market.
According the modelling projections, Brazil and to a lesser extent other Latin American countries roughly maintain their large export shares in the coming decades. Climate change will have a small negative impact on overall food product imports in the EU, and hence for most countries exports to the EU will be reduced compared to the baseline (but not compared to 2010). Given the relatively small macroeconomic consequences of agricultural impacts from climate change on Europe, total imports by EU hardly change; changes in exports of specific regions to the EU are therefore driven primarily by changes in the region’s comparative advantage.
Panel B presents changes in export flows of food product to EU. It portrays how changes in export prices (or more precisely, the prices that EU must pay for imports from this region) drive a wedge between the changes in export volumes and values (export revenues). Panel A already shows that climate damages to agriculture almost completely wipe out exports of food from India to the EU. Therefore, the percentage changes in exports are extremely large and not presented in panel B. The general picture that emerges from panel B is that the stronger the increase in regional prices, not least related to increases in exchange rates, the bigger the reduction in export volumes. And as it is relative comparative advantage that matters, those regions whose export price levels change least can gain in terms of export volumes. The second clear trend is that the export volume effect clearly dominates the price effect: those regions that see their export volumes decline also see a reduction in export revenues. Putting both mechanisms together implies that stronger price increases imply lower revenues and larger wedges between revenues and volumes.
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Figure 17. Change in food exports to the EU in the agricultural damages scenario Panel A. Volumes of exports of food products to the EU by region of origin
(Percentage change in 2060 w.r.t. no-damage baseline)
Panel B. Changes in volume and value of exports of food products to the EU
Note: In panel B, the percentage changes in India are too large to be meaningfully shown on the graph.
Source: OECD ENV-Linkages model.
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The changes in regional comparative advantage are shown in Figure 18, which shows the projected RCA for exports to the EU (i.e. not based on global exports like in Figure 15, but specifically for exports to the EU only). From comparing Figure 18 to Figure 15 it is clear that the change in RCA for exports to the EU closely resembles the change in the global RCA. It is the same set of countries which are projected to have the strongest change in their RCA, and strong reductions in RCA correlates with strong macroeconomic losses in these countries.
Figure 18. Levels and change in RCA of food exports to the EU in the agricultural damages scenario
(Percentage change in 2060 w.r.t. no-damage baseline)
Source: OECD ENV-Linkages model.
For Brazil, food products are projected to make up almost half of total exports to the EU, according to the baseline projection for 2060. Hence, Brazil’s RCA is very high, and it increases further in the climate scenario (Figure 18), not because it will export more to the EU, but rather because the other trading partners will export less (Figure 16). Furthermore, the domestic market in Brazil is less dependent on agriculture than those of the other major trading partners of the EU: agriculture is projected to make up a relatively smaller share of overall output of the Brazilian economy. As a consequence, the macroeconomic consequences are smaller in Brazil than in the other regions, despite very similar yield shocks (cf. Section 4.2.1).
By contrast, a country like Indonesia, which has relatively smaller yield shocks but given the stronger dependency on agriculture, larger macroeconomic consequences, is projected to see its exports to the EU decline. The price that the EU for food imports increases substantially for imports from Indonesia and e.g. India and the Middle Eastern countries. This implies a shift of competitive position for exporting to the EU from counties like Indonesia towards countries like Brazil.
This case study is an illustration of some of the specific effects that drive changes in trade in the different regions. The numerous interactions that exist between regions and sectors make it impossible to establish a rule of thumb that shows for instance that
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crop yield decreases will lead to a decrease in competitiveness. As illustrated, there may be an increase in competitiveness if other competitors for a certain market are more severely damaged or decide to specialise in the production of other goods. This highlights the need for each region to understand the direct impacts of climate change on their sectoral production and on their trade flows, but also the possible impacts of climate change on regions they are competing with for specific markets. This will help maintain comparative advantage, if possible, and decisions regarding what goods to specialise in the future.
4.3 Sensitivity of domestic consequences to international spillovers
The projections above show how economic consequences of climate change damages in one region affect other regions, and how trade plays a central role in these cross-country linkages. If impacts were identical across countries, all regions would maintain their international competitive position. Simultaneously, they would be negatively affected by the reduced demand for exports following the slowdown in the economies of trading partners that are affected by climate change. However, heterogeneity in impacts means that relative competitive positions start to shift. Further, if climate change is beneficial (or less negative) for other countries, whereas the domestic damages are (more) negative, the competitive position may be worsened by climate change. Thus, there are two key international spillovers between the countries in determining the domestic economic consequences of climate change: (i) damages from climate impacts in other countries; and (ii) changes in international trade patterns due to shifts in competitive positions.
This Section aims to shed further light on the importance of these international linkages, by decomposing the macroeconomic costs of climate change. The point of the exercise is to illuminate to what extent international linkages determine the costs of climate change, but cannot be interpreted as a policy analysis. The decomposition uses two hypothetical alternative cases:
1. damages only affect domestic economies, and other countries are not damaged by climate change; hence, there are no spillovers from damages in other economies; this is labelled “no international damage spillovers”.
2. adjustments of import patterns to adapt to the climate shocks are not allowed, labelled “no import flexibility spillovers”.
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Box 3. Modelling assumptions used for the decomposition For the first type of spillover, “International damage spillovers”, the central projection is compared to a
specific simulation which is carried out for each country separately with damages affecting that region only. Damages in all other regions are excluded. Since climate change has consequences on all regions across the world, it is clear that this is just a hypothetical scenario used for analytical purposes. In a modelling setting, when damages are implemented in all regions, it is impossible to single out specific effects. Applying climate damages individually to each region considered, allows instead isolating the effect of domestic damages on the economy, by cancelling the spillovers from climate damages in other regions. In this scenario, world market prices are hardly affected; in contrast, in the central scenario the climate damages, which are applied to all world regions, affect international trade patterns.
For the second type, “Import flexibility spillovers”, simulations are compared where the import volumes of the country under scrutiny are fixed at the baseline levels or can adjust freely. Fixed imports imply that countries cannot respond to lower productivity of domestic production and lower domestic demand by changing their import levels. As production in certain sectors and regions becomes more costly, in the central projection economies adapt through changing their production and trade patterns. In other words, some resources will be reallocated across sectors in order to alleviate the burden in other sectors.32 In absence of this flexibility, the adjustment of trade patterns is no longer possible, although total imports can still scale with domestic production. 33 Fixed import patterns can be imposed at the local level (i.e. only the country under investigation has fixed import flows), or globally (i.e. import patterns are fixed for all countries). Again, this is a hypothetical set-up that allows decomposing the trade spillover effects by comparing the results with those of the central projection in which trade is not constrained.
Box 3 describes the underlying modelling assumptions in more detail. Together, these two alternative specifications allow decomposing the costs into the domestic costs from domestic impacts, spillovers from foreign damages and spillovers from adjusting trade flows. Because these spillovers are region-specific, this illustrative analysis focuses on the USA, European Union, China, India and Sub-Saharan Africa.
This decomposition is meant only as a theoretical exercise, and does not reflect specific recommendations on climate or trade policies. They may highlight the potential benefits of adjusting trade patterns, but do not reflect any specific policy and cannot be interpreted to imply costs or benefits of policy actions. Any policy change that would aim at achieving a change in international trade patterns along the lines as presented in this decomposition would by definition have more complex economic reactions that are – by construction – absent in this decomposition analysis. Assessing the role of specific trade or trade-related policies would require a much more detailed analysis, and simulations of specific policy instruments. This is left for future analysis.
32 For instance, if agricultural yields drastically decrease due to climate change for a certain crop,
the gap in domestic production can be replaced with imports from abroad. The decision whether to import more will also depend on other factors, such as the changes in import and export prices, the changes in domestic prices, and the possibilities to substitute with other goods.
33 For this analysis, import-to-production ratios and import shares across countries are assumed to be the same than in the baseline without climate feedbacks, and the trade balance is fixed at the baseline level as well. But the volume of imports and trade flows themselves will adjust to take into account changes in income resulting from climate damages, i.e. they scale with domestic production. In order to reproduce the trade structure of the baseline with no climate impacts, this scenario with “fixed import patterns” will assume that the parameters driving these import shares will adjust. Alternative ways of removing import flexibility have been tested, such as changing the responsiveness of trade flows to changes in relative prices. The results of these alternative scenarios are similar to those presented here.
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Figure 19 shows to what extent the international spillovers dominate the costs of climate change. For both types of spillovers, the effects can be positive or negative, depending on the circumstances of the country under investigation. For the international damage spillovers, damages in other regions will, on the one hand, negatively affect the domestic economy, especially because countries cannot protect their consumption levels by importing more (cheaply) from unaffected regions. On the other hand, countries maintain their regional competitive position better when other regions are also affected, at least when the major trading partners are affected in a similar way. For the import flexibility spillovers, the adjustments to import levels can limit shocks to domestic production. But one country’s import flexibility implies changes in exports by another country.
Figure 19. Decomposition of changes in real GDP in 2060 in the climate damages scenario (Shares in change of GDP in 2060 in the central projection)
Note: Shares add up to 100%, which reflects the change in GDP in the central projection. Source: OECD ENV-Linkages model.
The regional results show remarkable differences in the extent to which the two international spillovers affect the costs of climate change. For the United States, a pattern emerges where international import flexibility spillovers contribute a significant portion of the total damages. This reflects a vulnerability of the US economy to changes in its export market: when other countries can adjust their imports, they will reduce imports from the US. Similarly, the contribution of international damage spillovers to the total costs in the USA is negative, as long as there is no import flexibility. In this case, the global level of imports does not contract, and the share of the USA in world trade is larger due to relatively larger impacts in some of its trading partners. But the US economy is hurt by the loss of exports to other countries that is induced by damages in other countries when import patterns are flexible. Domestic import flexibility is much less important for the US economy.34
34 The small additional costs from domestic import flexibility depend on the modelling
assumption that the current account is fixed and exchange rates adjust. Thus, reducing imports will have to be compensated by also reducing exports, which hurts the domestic economy. The Armington assumption on flexibility to change international trade also excludes the
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The results for the European Union and India reflect a different story: local damages with fixed imports are the worst situation; these regions have lower costs to their economy when damages also affect other regions and when imports are flexible, either locally or globally. The reason is that when damages affect all economies in the world, they tend to ‘level the playing field’. A lack of international damage spillovers instead implies a larger shock to the domestic competitive position. The positive effect of adjusting domestic import flows is especially strong in India. The country is projected to have very severe damages from climate change, and international trade plays a major role. As domestic production costs rise due to the lower productivity, the economic consequences can only be limited by relying more on relatively cheap imports. This loss of comparative advantage of the Indian economy is clear from the analysis in Section 4. Whether only India or all countries have their import patterns fixed does not matter, as the additional costs stem from the inability to adjust imports, not from changes in India’s exports.
For China and Sub-Saharan Africa, the spillovers work in the opposite way. Damages in other countries and import flexibility both constitute a positive part of the total damages in these regions, due to the negative consequences on their exports. Damages to the Chinese economy are, however, largely driven by domestic impacts, and international linkages play a very minor role.35 The additional costs from international damage spillovers are more substantial in Sub-Saharan Africa, at least in terms of share of total costs in the central projection. International damage spillovers imply an additional burden for the Sub-Saharan economies as on the one hand their import costs rise due to higher production costs abroad, and secondly their export position weakens as the impacts in Africa are projected to be relatively higher than in most of its trading partners.
These results illustrate that international linkages through world trade markets may significantly differ across countries: an international spillover channel that is positive for one region can be negative to another. In fact, the only result that is robust across all countries is that when damages are local, fixing imports leads to higher costs (not shown in the figure). But in all situations where there are some international linkages, the results are very specific and determined by a region-specific mixture of trade openness, import dependency, relative competitive position, and – last but not least – the relative impacts of climate change vis-à-vis the trading partners.
Finally, it should be re-iterated that this decomposition analysis does not imply a trade policy recommendation. At the global level, adjustments of international trade patterns can keep the costs of climate change limited, but it does pose additional costs for some regions. Without import flexibility, the global costs of climate change are projected to be higher, especially in some of the regions which are most severely affected by climate damages, not least India. This points to the importance of the general principle that various policy domains need to be properly aligned, and that specific policies in one domain can provides significant barriers to accomplishing objectives in other domains (OECD, 2015b).
possibilities that new bilateral trade patterns emerge. Alternative modelling assumptions may lead to different results in this respect. However, this effect is very small.
35 Restricting changes in exports may affect the Chinese economy more. The ENV-Linkages model assumes perfect substitutability in exports. Furthermore, in principle countries cannot control exports as easily as imports. Thus, country-specific export restrictions cannot be simulated without going into detailed trade policy scenarios. This is left for future research.
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5. Concluding Remarks
Providing a plausible projection of bilateral trade flows across many world regions for decades into the future is a daunting task, and then overlaying that with information on the economic consequences of climate change further complicates matters. The uncertainties surrounding these projections are large. Will Brazil by the middle of the century really be able to gain competitive advantage over Indonesia in exporting food to the EU? No-one knows. This paper limits itself to presenting one plausible scenario of future developments, to shed light on the mechanisms at work in explaining how climate change will affect trade. More robust quantitative insights require more elaborate modelling analysis, using multiple scenarios on the major modelling assumptions, and ideally comparing different models and using a risk-based framework. That is beyond the reach of this paper. Nonetheless, a number of general insights that are less sensitive to the exact model specification emerge that are worth highlighting.
First, international trade flows are projected to increase substantially in the coming decades, with increased focus on trade outside the OECD region. But by-and-large the direct impacts of climate change on international trade and infrastructure are negative, implying some of these projected increases in trade will be hampered by climate change. Furthermore, climate damages will put negative pressure on the economies of almost all regions, and trade flows are smaller when considering climate damages than in the naïve baseline that ignores feedbacks from climate change on the economy. These effects are especially strong in Africa and Asia, where the projections show high economic growth rates combined with increased trade dependency and large damages from climate change. In terms of economic sectors, the impacts on agriculture are projected to be relatively strong, and as agricultural goods and food products are heavily internationally traded, changes in agricultural trade flows are projected to be stronger than changes in trade flows for most other commodities.
Secondly, policy makers will need to understand not only the impacts of climate change on their domestic sectoral production patterns, but also the projected impacts on the economies of the regions they compete with on international markets. A national climate change assessment without attention to changes in international trade can lead to misleading conclusions on the effects of climate change on domestic competitiveness. Despite being negatively affected by climate damages, a region may increase its competitiveness if other competitors for a certain market are more severely damaged or decide to specialise in the production of other goods. In the most affected countries exports decline more than imports and GDP. In contrast, producers in the least affected countries can improve their competitive position on both domestic and export markets. “Least affected” in this case is a relative term: what matters more are the domestic damages compared with those of the main trading partners, rather than absolute damage levels.
Thirdly, the mechanisms driving changes in trade patterns are very complex, with mutually reinforcing and dampening effects. Generally, countries that have larger domestic markets and more diversified trade patterns can absorb climate shocks better than countries that are more specialised. Comparative advantage tends to decline in countries where climate damages lead to relatively large reductions in export volumes, while those regions whose export price levels change least can gain in terms of export volumes.
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Fourthly, adjusting trade patterns can help alleviate the burden of climate change impacts on the domestic economy, for instance higher domestic production costs can be compensated through increased imports when these are becoming relatively cheaper. A decomposition analysis reveals that a lack of international damage spillovers, i.e. when damages affect only the domestic economy, implies a larger shock to the domestic competitive position, as damages occurring in all countries ‘levels the playing field’, although exports are hurt more with global damages due to a contraction of the global economy. Similarly, import flexibility can help accommodate relatively severe domestic impacts and thus reduce costs, but at the same time they provide a threat to the exports of other regions.
In this report, it was impossible to include any direct impacts of climate change on international trade in the modelling analysis. However, this could be pursued further if reliable information becomes available on e.g. the change in overall costs from climate impacts on international sea, air and land transport, or more specifically on the changes in transportation costs from opening up of the Arctic shipping route. Especially the latter would ideally be pursued in close collaboration with the International Transport Forum (ITF).
An analysis of the trade policy response to climate change is beyond the scope of this paper. 36 Future work can build on preliminary analysis in Chateau et al. (2015), who show that a "partial multilateral scenario" will benefit all countries, but especially the same less-developed economies which are most threatened by climate change. Many of the countries that are most severely affected, and that have the strongest reduction in revealed comparative advantage, are also those that are rapidly growing economies in the baseline, with significantly increasing world market shares in the major commodities. Future modelling work could therefore potentially look at how differences in regional impacts from climate change can be partially compensated by re-aligning trade agreements to improve the competitive position of the countries that are most negatively affected; similarly, future work could also investigate the sectoral dimensions in more detail, exploring how differences in climate impacts between sectors can be exploited to alleviate the largest climate and economic risks. More in- depth analysis is also warranted on changes in trade in clean technology, as induced by ambitious climate policies.
Even if trade policies are not actively used to reduce the pressure on the climate system, harmful barriers to climate change adaptation can imply significant costs and worsen climate damages and risks. Therefore, it is important that policies are not diametrically opposed on certain aspects, to ensure that least-cost adjustment mechanisms are facilitated (OECD, 2015b). Climate policies and trade policies could therefore be aligned in order to avoid unnecessary barriers in the pursuit of various policy options, offset some of the worst climate damages and alleviate the burden on the most vulnerable economies.
36 Interaction between trade and climate mitigation policies is yet another topic. OECD (2015b)
places an emphasis on the role of international trade to facilitate penetration of low-carbon technology and other mitigation actions. Lanzi et al. (2013) highlight the potential for reducing economic impacts by linking carbon markets.
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Annex A. Description of the ENV-Linkages Modelling Tool
The OECD’s in-house dynamic computable general equilibrium (CGE) model - ENV-Linkages - is used as the basis for the assessment of the economic consequences of climate impacts until 2060. The advantage of using a CGE framework to model climate impacts is that the sectoral details of the model can be exploited. Contrary to aggregated Integrated Assessment Models, where monetised impacts are directly subtracted from GDP, in a CGE model the various types of climate damages can be modelled as directly linked to the relevant sectors and economic activities.
ENV-Linkages is a multi-sectoral, multi-regional model that links economic activities to energy and environmental issues; Chateau et al. (2014) provide a description of the model. The model is calibrated for the period 2013 - 2060 using the macroeconomic trends of the baseline scenario of the OECD@100 project. The ENV- Linkages model is the successor to the OECD GREEN model for environmental studies (Burniaux, et al. 1992).
Production in ENV-Linkages is assumed to operate under cost minimisation with perfect markets and constant return to scale technology. The production technology is specified as nested Constant Elasticity of Substitution (CES) production functions in a branching hierarchy (cf. Figure A.1). This structure is replicated for each output, while the parameterisation of the CES functions may differ across sectors. The nesting of the production function for the agricultural sectors is further re-arranged to reflect substitution between intensification (e.g. more fertiliser use) and extensification (more land use) of crop production; or between intensive and extensive livestock production. The structure of electricity production assumes that a representative electricity producer maximizes its profit by using the different available technologies to generate electricity using a CES specification with a large degree of substitution. The structure of non-fossil electricity technologies is similar to that of other sectors, except for a top nest combining a sector-specific resource with a sub-nest of all other inputs. This specification acts as a capacity constraint on the supply of the electricity technologies.
The model adopts a putty/semi-putty technology specification, where substitution possibilities among factors are assumed to be higher with new vintage capital than with old vintage capital. In the short run this ensures inertia in the economic system, with limited possibilities to substitute away from more expensive inputs, but in the longer run this implies relatively smooth adjustment of quantities to price changes. Capital accumulation is modelled as in the traditional Solow/Swan neo-classical growth model.
The energy bundle is of particular interest for analysis of climate change issues. Energy is a composite of fossil fuels and electricity. In turn, fossil fuel is a composite of coal and a bundle of the “other fossil fuels”. At the lowest nest, the composite “other fossil fuels” commodity consists of crude oil, refined oil products and natural gas. The value of the substitution elasticities are chosen as to imply a higher degree of substitution among the other fuels than with electricity and coal.
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Figure A.1. Production structure of a generic sector in ENV-Linkages
Source: OECD ENV-Linkages model.
Household consumption demand is the result of static maximization behaviour which is formally implemented as an “Extended Linear Expenditure System”. A representative consumer in each region optimally allocates disposal income among the full set of consumption commodities and savings. Saving is considered as a standard good in the utility function and does not rely on forward-looking behaviour by the consumer. The government in each region collects various kinds of taxes in order to finance government expenditures. Assuming fixed public savings (or deficits), the government budget is balanced through the adjustment of the income tax on consumer income. In each period, investment net-of-economic depreciation is equal to the sum of government savings, consumer savings and net capital flows from abroad.
Domestic goods and services
Demand for Intermediate goods and services
Demand for Labour
Value-added plus energy
Imported goods and services
Demand for Capital and Energy
Demand for Energy Demand for Capital and Specific factor
Capital Specific factor (land; resource)
Demand by region of origin
Gross Output of sector i
Production output Non-CO2 GHG Bundle
Non-CO2 GHGs
Fossil fuels Electricity
Coal Liquids
Refined oil Natural gas Crude oil
CO2
CO2 CO2 CO2
CO2
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International trade is based on a set of regional bilateral flows. The model adopts the Armington specification, assuming that domestic and imported products are not perfectly substitutable. Moreover, total imports are also imperfectly substitutable between regions of origin. Allocation of trade between partners then responds to relative prices at the equilibrium.
Market goods equilibria imply that, on the one side, the total production of any good or service is equal to the demand addressed to domestic producers plus exports; and, on the other side, the total demand is allocated between the demands (both final and intermediary) addressed to domestic producers and the import demand.
CO2 emissions from combustion of energy are directly linked to the use of different fuels in production. Other greenhouse gas (GHG) emissions are linked to output in a way similar to Hyman et al. (2002). The following non-CO2emission sources are considered: i) methane from rice cultivation, livestock production (enteric fermentation and manure management), fugitive methane emissions from coal mining, crude oil extraction, natural gas and services (landfills and water sewage); ii) nitrous oxide from crops (nitrogenous fertilizers), livestock (manure management), chemicals (non-combustion industrial processes) and services (landfills); iii) industrial gases (SF6, PFCs and HFCs) from chemicals industry (foams, adipic acid, solvents), aluminium, magnesium and semi-conductors production. Over time, there is, however, some relative decoupling of emissions from the underlying economic activity through autonomous technical progress, implying that emissions grow less rapidly than economic activity.
Emissions can be abated through three channels: (i) reductions in emission intensity of economic activity; (ii) changes in structure of the associated sectors away from the ‘dirty’ input to cleaner inputs, and (iii) changes in economic structure away from relatively emission-intensive sectors to cleaner sectors. The first channel, which is not available for emissions from combustion of fossil fuels, entails end-of-pipe measures that reduce emissions per unit of the relevant input. The second channel includes for instance substitution from fossil fuels to renewable in electricity production, or investing in more energy-efficient machinery (which is represented through higher capital inputs but lower energy inputs in production). An example of the third channel is a substitution from consumption of energy-intensive industrial goods to services. In the model, the choice between these three channels is endogenous and driven by the price on emissions.
ENV-Linkages is fully homogeneous in prices and only relative prices matter. All prices are expressed relative to the numéraire of the price system that is arbitrarily chosen as the index of OECD manufacturing exports prices. Each region runs a current account balance, which is fixed in terms of the numéraire. One important implication from this assumption in the context of this report is that real exchange rates immediately adjust to restore current account balance when countries start exporting/importing emission permits.
As ENV-Linkages is recursive-dynamic and does not incorporate forward-looking behaviour, price-induced changes in innovation patterns are not represented in the model. The model does, however, entail technological progress through an annual adjustment of the various productivity parameters in the model, including e.g. autonomous energy efficiency and labour productivity improvements. Furthermore, as production with new capital has a relatively large degree of flexibility in choice of inputs, existing technologies can diffuse to other firms. Thus, within the CGE framework, firms choose the least-cost combination of inputs, given the existing state
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of technology. The capital vintage structure also ensures that such flexibilities are larger in the long run than in the short run.
The sectoral and regional aggregation of the model, as used in the analysis for this report, are given in Tables A.1. and A.2., respectively.
Table A.1. Sectoral aggregation of ENV-Linkages
Agriculture Manufacturing Paddy Rice Paper and paper products Wheat and meslin Chemicals Other Grains Non-metallic minerals Vegetables and fruits Iron and Steel Sugar cane and sugar beet Metals n.e.s. Oil Seeds Fabricated metal products Plant Fibres Food Products Other Crops Other manufacturing Livestock Motor vehicles Forestry Electronic Equipment Fisheries Textiles Natural Resources and Energy Services Coal Land Transport Crude Oil Air and Water Transport Gas extraction and distribution Construction Other mining Trade Other Services and Dwellings Petroleum and coal products Other Services (Government) Electricity (7 technologies) Fossil-Fuel based Electricity; Combustible renewable and waste based Electricity; Nuclear Electricity; Hydro and Geothermal; Solar and Wind;
Coal Electricity with CCS; Gas Electricity with CCS
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Table A.2. Regional aggregation of ENV-Linkages
Macro regions ENV-Linkages countries and regions OECD America Canada
Chile Mexico United States
OECD Europe EU large 4 (France, Germany, Italy, United Kingdom) Other OECD EU (other OECD EU countries) Other OECD (Iceland, Norway, Switzerland, Turkey, Israel)
OECD Pacific Oceania (Australia, New Zealand) Japan Korea
Rest of Europe and Asia People’s Republic of China (abbr. China) Non-OECD EU (non-OECD EU countries) Russian Federation (abbr. Russia) Caspian region Other Europe (non-OECD, non-EU European countries)
Latin America Brazil Other Lat.Am. (other Latin-American countries)
Middle East & North Africa Middle-East North Africa
South and South-East Asia India Indonesia ASEAN9 (other ASEAN countries) Other Asia (other developing Asian countries)
Sub-Saharan Africa South Africa Other Africa (other African countries)
In the presentation of the results, some smaller regions, and the EU regions, have been regrouped to avoid a false sense of accuracy. Specifically, Chile has been grouped with Other Latin America; Russia has been grouped with Other Europe; Japan and Korea have been aggregated in OECD Asia; EU large 4, Other OECD EU and non-OECD EU have been aggregated in EU; and South Africa and Other Africa have been aggregated in Sub-Saharan Africa.
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Annex B. Details on the Evolution of International Trade in the No-damage Baseline Projection
Trade specialisation patterns or the relative importance of different countries and regions in markets for each good and service markets will change over time, driven by the same four drivers of international trade, but more precisely by the differences across countries in relative productivity (or production costs) changes and by the convergence in consumption patterns.
Changes in consumption patterns
The baseline scenario for the world economy will project convergence in consumption patterns and this for two reasons. Firstly as standards of living are growing the consumption of all kind of services is increasing, as a percentage of total income, while the share of consumption of necessity goods is decreasing. These changes in consumption patterns are more pronounced in fast growing economy than in OECD countries where some levels of satiation would occur. Secondly, the projection also assumes that household’s preferences themselves will converge towards OECD standards. As a result the composition of demand will change over time (Figure B.1).
Figure B.1. Changes in sectoral composition of world trade Panel A. gross exports by aggregate industries as percentage of total exports)
0
5
10
15
20
25
2010 2060
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Panel B. Growth of value-added and Exports by aggregate activities 2015-2060 (average rate)
Source: OECD ENV-Linkages model.
Changes in production patterns
A second force is that production structures also change over time: some sectors in some countries will take advantage of some comparative advantage, associated with the change in endowments of production factor inputs or in their efficiency use relative to other factors. This explains that the changes in production patterns will not necessarily correspond to the changes in demand and follow some changes in trade specialisation patterns (Figure B.2 and B.3).
0 0.5 1 1.5 2 2.5 3 3.5 4
Agriculture
Energy
Manufacturing
Services
Agriculture
Energy
Manufacturing
Services
Ex po
rts (v
ol um
e) G
DP (v
ol um
e)
2015-2060 2040-2060 2015-2040
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Figure B.2. Changes in consumption patterns, selected countries (Demand shares as percentage of total demand, 2010-2060)
Source: OECD ENV-Linkages model.
0 5
10 15 20 25 30 35 40 45 50
USA
2010 2060
0 5
10 15 20 25 30 35 40
Russia
2010 2060
0
5
10
15
20
25 China
2010 2060
0 5
10 15 20 25 30 35
Sub-Saharan Africa
2010 2060
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Figure B.3. Changes in industrial structure, selected countries (Value added shares in total GDP, 2010-2060)
Source: OECD ENV-Linkages model.
0
10
20
30
40
50
60 USA
2010 2060
0 5
10 15 20 25 30 35 40 45
Russia
2010 2060
0 5
10 15 20 25 30 35
China
2010 2060
0 5
10 15 20 25 30 35 40
Sub-Saharan Africa
2010 2060
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Changes in trade specialisation
Figure B.4. Changes in trade specialisation patterns in selected aggregate industries (Trade shares as percentage of global trade, 2010-2060)
Panel A. Transformed Good and Services
0 5
10 15 20 25 30 35 40 45 50
Busines Services
2010
2060
0
10
20
30
40
50
60
70 Other manufacturing
2010
2060
0
5
10
15
20
25
30
35
40 Other EII
2010
2060
0
10
20
30
40
50
60 Electronics
2010
2060
0
5
10
15
20
25
30
35
40
45 Chemicals
2010
2060
0
5
10
15
20
25
30
35
40
45 Motor Vehicles
2010
2060
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Panel B. Raw Product and Transformed raw products
Source: OECD ENV-Linkages model.
Two kinds of goods deserve a closer examination. Energy and Agricultural goods, as they ultimately depend strictly on very unevenly distributed primary factors: natural resources and land factors. Notice also that a large part of the efficiency of these factors are also regional (extraction costs of fossil fuel are function of the quality of the land surface while land yields are function of the climate and geographical position) (Figure B.4).
0
5
10
15
20
25 Fossil fuel products
2010
2060
0
2
4
6
8
10
12
14
16
18 Food Product
2010
2060
0
2
4
6
8
10
12
14
16
18 Textiles
2010
2060
0
1
2
3
4
5
6 Agriculture
2010
2060
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Annex C. Summary of the Approach to Represent Damages from Climate Change in the Model
A key challenge in modelling the link between climate change impacts and economic activities is to adequately capture the heterogeneity of climate change impacts. These vary in character and magnitude across regions and translate into shocks to the economy with some activities and sectors being more severely affected than others, through the channels of different economic variables.
One way to study this complex system in an economic framework is to link each climate impact to different variables in the production function that represents the activity of a specific industry or group of industries in the basic structure of the model. In a production function, output is produced from distinct inputs (e.g. labour and capital), intermediate commodity inputs and primary resources.
By modelling climate change impacts with a production function approach, it is possible to obtain, as for integrated assessment models, the total economic costs of the selected impacts of climate change on GDP. The overall GDP costs are in turn an indicator of the extent to which climate change has an impact on future economic growth; as in this approach damages can also affect capital stocks, it includes a potential direct effect on the growth rate of the economy. Compared to integrated assessment models in which climate damages are subtracted as a total from GDP, the production function approach can also explain how the composition of GDP is affected over time by climate change: what sectors are most affected (for the impacts that have been assessed) and what changes in production factors mostly contribute to changes in GDP.
Explicitly linking climate impacts to the sectoral economic variable works well for those impacts that are directly affecting economic markets. For non-market impacts, such a direct link with a part of the production function does not exist, and the damages need to be evaluated separately. Modelling climate damages in CGE models also means that a certain level of market-driven, reactive autonomous adaptation to the damages is inherently modelled. In models with sectoral details and a complex production and trade structure, a change in the productivity of a particular input will trigger substitution responses by producers that alter the use of the various inputs. Substitution is a powerful form of market adaptation once the level of the economy at which impacts manifest themselves is reached. The presence of market adaptation in the model also means that the final estimated costs of climate change impacts can be expected to be lower (or higher) than those estimated if adaptation is not considered (or considered to be optimal), as is often the case in IAMs. This feature also allows modellers to study both the direct effects of climate change and the indirect ones, such as the impacts that take place after trade effects.
The quantification of climate change impacts in ENV-Linkages relies on available information on how climate impacts affect different economic sectors. The information sources are mostly derived from bottom-up partial-equilibrium models,
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climate impact models and econometric studies.37 Table C.1 provides a summary of the impacts considered and their respective sources from the literature. They refer to the consequences of climate-related changes in agriculture and fisheries, coastal zones, health, and changes in the demand for tourism services and for energy for heating and cooling. Most impacts used are assessed for the specific Representative Concentration Pathway (RCP) 8.5 scenario, which describes a pathway of climate change resulting from a fast increase in global emissions. This scenario is, at least until 2060, similar to the ENV-Linkages model baseline with respect to GHG concentrations. Wherever possible, the central projection uses results from the HadGEM3 model from the Hadley Center of the UK Met Office, for the specification of the climate system variables. However, for certain climate impacts the data was only available from other climate models.
Table C.1. Climate impact categories included in ENV-Linkages
Climate Impacts Impacts modelled Source Time frame Agriculture Changes in crop yields IMPACT model - Nelson et al. (2014) 2050
Changes in fisheries catches
Cheung et al. (2010) 2060
Coastal zones Loss of land and capital from sea level rise DIVA model - Vafeidis et al. (2008) 2100 Extreme events Capital damages from hurricanes
Mendelsohn et al. (2012) 2100
Health Mortality and morbidity from infectious diseases, cardiovascular and respiratory diseases
Tol (2002) 2060
Morbidity from heat and cold exposure Roson and Van der Mensbrugghe (2012) and Ciscar et al. (2014) for Europe
2060
Energy demand Changes in energy demand for cooling and heating
IEA (2013) 2050
Tourism demand Changes in tourism flows and services HTM - Bigano et al. (2007) 2100 Ecosystems No additional impacts covered in the modelling exercise Water stress No additional impacts covered in the modelling exercise Tipping points Not covered in the modelling exercise
Source: Own compilation.
Two broad categories of climate change impacts can be distinguished. The first affects the supply-side of the economic system, namely the quantity or productivity of primary factors. Land and capital destruction from sea level rise, crop productivity impacts in agriculture, and labour productivity impacts on human health belong to this category. The second category of climate change impacts affects the demand side. Impacts on health expenditures38 and on energy consumption are of this kind.
37 Much of the information used is an elaboration of data provided by recently concluded and
ongoing research projects, including both EU Sixth and Seventh Framework Programs (FP6 and FP7) such as ClimateCost, SESAME and Global-IQ and model inter-comparison exercises such as AgMIP. These data have been kindly provided by the researchers involved in these projects.
38 Health impacts are calculated with a cost of inaction approach, which does not account for other costs to society. A valuation of full welfare costs would imply higher values.
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Annex D. Key Simulation Results for 25 Regions
The results presented in this paper have been aggregated to avoid an impression of false accuracy of the analysis for particular small economies and for intra-EU trade. However, in line with OECD (2015a), the underlying analysis is carried out at the 25 region level. Some of the key results for the simulation of all climate damages, as reported in Section 4.1, are reproduced in Table D.1; Similarly, Table D.2 provides the more disaggregated results for the analysis of agricultural damages presented in Section 4.2.
Table D.1. Regional results for the climate damages scenario
(Percentage change in 2060 w.r.t. no-damage baseline)
GDP Exports (volume)
Imports (volume)
Real exchange rate
ASEAN 9 -2.6% -3% -2% 2% Brazil -1.4% -2% -1% 0% Canada 0.9% 0% 0% 0% Chile -0.6% -1% 0% 2% China -2.5% -2% -2% 2% Other OECD EU -0.4% 0% -1% 0% EU large 4 -0.1% 0% -1% 0% Non-OECD EU -0.8% -1% 0% 2% Indonesia -2.3% -3% -2% 5% India -4.3% -6% -4% 12% Japan 0.0% 0% -1% 0% Korea -0.4% -1% -1% 0% Middle East -3.2% -3% -3% 3% Mexico -2.0% -2% -2% 1% North Africa -3.5% -3% -3% 5% Other Africa -4.1% -4% -4% 6% Aus. & NewZ. -0.9% -1% -1% 1% Other Asia -3.0% -2% -3% 3% Other OECD -0.2% -1% -1% 0% Other Europe -0.7% -1% 0% 1% Other Lat.Am. -1.7% -2% -1% 1% Russia 1.4% 1% 0% 1% Caspian region -2.3% -4% -3% 4% USA -0.5% 0% -2% 0% South Africa -2.3% -3% -3% 1%
Source: OECD ENV-Linkages model.
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Table D.2. Regional results for the agricultural damages scenario
(Change in 2060 w.r.t. no-damage baseline)
GDP crop yields RCA Food Food export volume Food export price ASEAN 9 -0.7% -18% -0.13 -3% 6% Brazil 0.0% -26% 0.70 7% 3% Canada -0.1% -1% 0.19 12% 2% Chile 0.3% 12% 0.47 9% 2% China -0.6% -9% 0.00 -2% 6% Other OECD EU -0.1% -12% 0.09 9% 2% EU large 4 0.0% -11% 0.08 13% 1% Non-OECD EU -0.4% -13% 0.06 8% 2% Indonesia -0.7% -23% -0.63 -14% 11% India -2.6% -30% -0.51 -75% 47% Japan 0.0% 2% 0.01 19% 1% Korea 0.0% 0% 0.01 7% 4% Middle East -0.7% -13% 0.02 4% 4% Mexico -0.2% -18% 0.10 9% 2% North Africa -1.6% -15% -0.16 -10% 7% Other Africa -1.7% -18% 0.05 0% 4% Aus. & NewZ. -0.3% -4% 0.18 11% 2% Other Asia -0.5% -23% 0.00 0% 5% Other OECD -0.1% -2% 0.10 12% 1% Other Europe -0.2% -11% 0.63 7% 2% Other Lat.Am. -0.2% -16% 0.09 1% 4% Russia 0.3% -8% 0.02 2% 3% Caspian region -1.4% -7% 0.02 7% 1% USA -0.3% -15% 0.04 9% 2% South Africa -0.2% 1% 0.08 12% 2%
Source: OECD ENV-Linkages model.
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- OECD TRADE AND ENVIRONMENT WORKING PAPERS
- Abstract
- Résumé
- Acknowledgements
- Table of contents
- Acronyms and Abbreviations
- Executive Summary
- 1. Introduction
- 2. The Evolution of International Trade in the Coming Decades
- 2.1 Evolution of regional economic activity and pressure on the climate system
- 2.2 Evolution of international trade flows
- 3. Impacts of Climate Change on Domestic Economies and International Trade
- 3.1 The direct impacts of climate change on international trade
- 3.2 The indirect consequences of climate change on international trade
- 3.2.1 The regional economic consequences of climate change13F
- 3.2.2 Changes in trade patterns due to climate change impacts
- 4. Understanding the Indirect Impacts of Climate Change on International Trade
- 4.1 Income effect: changes in macroeconomic competitiveness of countries
- 4.2 Compositional effects: changes in comparative advantage in agriculture and food
- 4.2.1 Macroeconomic consequences of agricultural impacts
- 4.2.2 Revealed Comparative Advantage (RCA) in food products
- 4.2.3 A deeper look at RCAs: food exports to the EU
- 4.3 Sensitivity of domestic consequences to international spillovers
- 5. Concluding Remarks
- Annex A. Description of the ENV-Linkages Modelling Tool
- Annex B. Details on the Evolution of International Trade in the No-damage Baseline Projection
- Changes in consumption patterns
- Changes in production patterns
- Changes in trade specialisation
- Annex C. Summary of the Approach to Represent Damages from Climate Change in the Model
- Annex D. Key Simulation Results for 25 Regions
- References
20211126015702xie_et_al_2020.pdf
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China Economic Review
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Climate change impacts on China's agriculture: The responses from market and trade Wei Xiea, Jikun Huanga,⁎, Jinxia Wanga, Qi Cuia,c,⁎⁎, Ricky Robertsonb, Kevin Chenb a China Center for Agricultural Policy, School of Advanced Agricultural Sciences, Peking University, Beijing 100871, China b International Food Policy Research Institute, Washington, DC 20005-3915, USA c Beijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China
A R T I C L E I N F O
Keywords: Climate change Food security Market Trade China
A B S T R A C T
China's food security has been facing several challenges, which are likely to be worsened due to climate change. The purpose of this paper is to provide an evidence on the impacts of climate change on China's agriculture, with particular attention to the market and trade responses. Using projected crop yield changes for China and its' main trading partners under changing climate, we employ an agricultural partial equilibrium model (CAPSiM) and a linked national and global equilibrium model (CAPSiM-GTAP) to assess the impacts on food production, price, trade and self-sufficiency of China. Our results show that climate change will have significant effects on crop production though with large differences among crops. Under the worst climate change scenario RCP 8.5, wheat yield in China is projected to decline by 9.4% by 2050, which is the biggest yield reduction among the crops. However, the market can also respond to the climate change, as farmers can change inputs in response to reduced yields and rising prices. As a result, production losses for most crops are dampened. For example, wheat production loss under RCP8.5 reduces to only 4.3% due to market response. The adverse impacts on crop production will be further reduced after accounting for the trade response as farmers adjust production to much higher prices in the more severely affected countries. The paper concludes that we need to learn more from farmers who optimize their production decisions in response to the market and trade signals during climate change. A major policy implication is that policymakers need to mainstream the market and trade responses into national plans for climate adaptation.
1. Introduction
China's agriculture is expected to face challenges in the future mainly due to rising food demand and constraints of land and water resources. Although China has largely ensured its food security in the past 40 years, it has increasingly relied on international markets to ensure its food supply since 2004 (Ali, Huang, Wang, & Xie, 2017; FAO, 2017). With increasing population, higher income and constraints of resources, the pressure on China's food security is going to increase in the future. Huang, Wei, Cui, and Xie (2017) predicted that China's overall food self-sufficiency is likely to fall from 94.5% in 2015 to around 91% by 2025.
Climate change will likely aggravate the challenges China faces on its food security in the future. China's annual average tem- perature has been rising significantly over the past six decades and the warming trend will continue under the future projections (Cui,
https://doi.org/10.1016/j.chieco.2018.11.007 Received 15 December 2017; Received in revised form 21 November 2018; Accepted 26 November 2018
⁎ Corresponding author. ⁎⁎ Corresponding author. E-mail addresses: jkhuang.ccap@pku.edu.cn (J. Huang), cuiqi.ccap@pku.edu.cn (Q. Cui).
China Economic Review 62 (2020) 101256
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Xie, & Liu, 2018; Liang & Yan, 2016; Meehl, 2007; Nakicenovic et al., 2000). It is generally accepted that the mechanism of climate change affecting China's agriculture is mainly through rising temperature and increasing fluctuation in precipitation (Wu et al., 2014; Edition Committee of China's National Assessment Report on Climate Change, 2015).
The impacts of climate change on China's agriculture have been widely studied in the literature through biophysical models (e. g., Li & Geng, 2013; Wang, Huang, & Yang, 2014; Xiong, Matthews, Holman, Lin, & Xu, 2007; Tao, Hayashi, Zhang, Sakamoto, & Yokozawa, 2008; Xiong et al., 2009; Piao et al., 2010). For example, Lin et al. (2005) found that the negative impacts of climate change on wheat yield in China could reach up to 5.6–18.5% under A2 scenario1 by 2020s. Similarly, Tao et al. (2008) suggested that if the temperature increases by 1 °C, rice yield would decline by 6.1–18.6% even after considering the adaptation measures. Xiong, Conway, Lin, and Holman (2009) predicted a moderate decrease in rice yield in the range of 4.9–8.6% in 2050s. Meanwhile, some other studies also provide the evidence on positive impacts of climate change on some of the crops. Lin et al. (2005), for example, concluded that irrigated maize yield would increase slightly by 2020 under B2 scenario1.
A major limitation of the biophysical models for assessing climate change impact is that they tend to overestimate adverse impacts of climate change on agriculture, as they fail to account for the underlying buffering capability of economic system, which the later attains through adjustments in production inputs and structure. For example, Wang, Huang, and Yang (2009) used a general equi- librium economic model to assess the climate change impacts on agriculture in China and found that the percentage decrease in production of rice, wheat, and maize in 2030 would be lower than the yield changes predicted by biophysical crop modelers. Using the global general equilibrium model (AGLINK), Zhai, Lin, and Byambadorj (2009) also found that climate change would cause China's total crop production to decrease only slightly (0.2–0.5%) in 2080. Some global studies on climate change do explicitly cover China while accounting for endogenous response of markets (Calzadilla et al., 2013; Nelson, Valin, et al., 2014; Parry, Rosenzweig, Iglesias, Livermore, & Fischer, 2004; Zhai et al., 2009; Zhou et al., 2017). Nevertheless, these studies either lack empirically based data on yield shocks for main crops in China or do not apply the detailed national economic model for China that can reflect China's agriculture market accurately.
International trade is also another important factor affecting China's food market but few studies have considered the role of trade while assessing climate change impacts on China. Around 2004, China turned a net importer of agriculture products form previously a net exporter, so much so that in 2016 > 80 million tons (Mt) of soybean was imported. At the same time, China became the world's largest importer of rice (Huang et al., 2017). There are several important global studies on the role of international trade in climate change on agriculture. For example, Reilly and Hohmann (1993) made the first attempt to discuss the role of international trade in assessing climate change impacts. Later, Baldos and Hertel (2015) explored the potential for a more freely functioning global trading system to maintain improved food security in the long run (i.e. by 2050). More recently, Brown et al. (2017) suggested that global trade would continue to play a central role in assuring that global food system adapts to a changing climate in that it is likely to facilitate the movement of food from areas of surplus to areas of deficit. However, there is no China-focused study that assesses climate change impacts on China's agriculture while accounting for the role of international trade.
The overall purpose of this paper is to provide an updated and more reliable evidence on the impacts of climate change on China's production, prices, trade and self-sufficiency of major crops, with particular focus on the market and trade responses. Our study aims to give some perspective to the studies that (i) focus only on the impacts of climate change on national food markets (ii) use single region model and (iii) fail to consider the price transmission from the rest of world. Our study examines the climate change impacts on major crops towards 2050 under the worst climate change scenario (measured with representative concentration pathway, i.e., RCP 8.5) and the best climate change scenario RCP2.6.2 To achieve this purpose, we use the econometrically estimated projected changes in the yields of major crops in China, while we derive the projected crop yield changes for China's main trading partners from a process-based biophysical method. Next, we employ a widely-used agricultural partial equilibrium model (China Agriculture Policy Simulation Model, CAPSiM) of China to assess the climate change impacts on agriculture, thus considering the domestic market responses. Then we use the linked national and global equilibrium model (CAPSiM-GTAP) to assess the climate change impacts on agriculture, wherein we consider both the market and trade responses. The linked model approach effectively transmits the effects of foreign countries' climate shocks on agriculture to China via trade, while allowing us to use a more precise and detailed national economic model.
Our results show that the effects of climate change on crop production are significant but have large variations among crops. Under the worst climate change scenario i.e., RCP 8.5, among all crops in China, wheat yield is projected to experience the largest decrease of 9.4% by 2050. After taking into account the market response, production losses for most crop are dampened (e.g. wheat production loss reduces to only 4.3%) because of the growers' response to changes in agricultural prices under climate change. Moreover, if we consider the impacts of climate change from the rest of the world, which affect China's trade and therefore domestic production, the severity of climate change impacts on China's agricultural production will be further reduced, e.g. to around 4% for wheat. The study concludes that we need to learn more from farmers who respond to changing climate according to the market and trade signals, and further mainstream these lessons into national adaptation development plan.
The rest of the paper is organized as follows: Section 2 introduces data sources for yield changes under different climate change
1 A2 and B2 scenarios represent different carbon emission pathways and correspondingly different temperature increase in the future. 2 RCP2.6 and RCP8.5 are named after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+2.6
and + 8.5 W/m2, respectively). We can easily see that RCP2.6 and RCP8.5 represent low and high carbon emission pathways and correspondingly low and high temperature increase, respectively, in the future (IPCC, 2014).
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scenarios for China and its main trade partners. Section 3 describes the simulation methodology, baseline scenario and climate change scenarios. Section 4 presents and analyzes the results for climate change impacts on China's agriculture and the role of market and trade. Section 5 concludes the study with policy implications.
2. Climate change shocks for biophysical yields of crops in China and the rest of world
2.1. Climate change shocks for biophysical yields of crops in China
In this study we cover rice, wheat, maize, soybean, cotton, rapeseed, peanut and sugar beet as they are the major crops produced in China. We began with extracting the changing trends of temperature and precipitation for China from the downscale simulation of Liang and Yan (2016), based on the RCP scenarios of Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) (IPCC, 2014). Both RCP 8.5 and RCP 2.6 scenarios are modeled in this study as the worst and best climate change scenario, respectively. In Liang and Yan (2016), several global circulation models (GCM), provided by CMIP5, are applied to project monthly temperature and precipitation during 2010–2100 in each province of China with base year of 1980–2010. Then we estimate both annual average and standard deviation of temperature and precipitation during each crop's growth season. The projections show that compared to 2012, the annual average temperature and precipitation during growing season of each crop will increase significantly in 2020–2050, while the standard deviation of annual precipitation will increase significantly for each crop (see Appendix Fig. 1). This shows both temperature and precipitation will increase, but the latter will have more annual fluctuation during crop growing seasons of the future years.
We obtain the changes in annual crop yield under climate change in China from a unique econometric estimation of Wang (2016). The study used China's provincial panel data to estimate climate change impacts on the yields of different crops in terms of changes in annual temperature, precipitation and their standard deviations during the growth season of major crop producing provinces, while controlling for differences in agriculture inputs and technology progress (Appendix Table 2). The study finally illustrated a nonlinear correlation between climate variables and crop yield, and extrapolated the annual changes of China's crop yields under IPCC's four RCP scenarios for the period 2010–2050 (Table 1).3
The physical impacts of climate change on crop yields in China vary considerably among crops and are shown in Table 1. Wheat, rice, peanut, and sugar beet are projected to experience yield reductions under both RCP 8.5 and RCP 2.6 scenarios, with wheat expected to bear the highest yield loss. Specifically, wheat yield would decline significantly in 2050 i.e., by 4.83% under RCP 2.6 and 9.39% under RCP 8.5. Next to wheat, rice yield would have moderate yield reduction in 2050 of 1.34% under RCP 2.6 and 2.60% under RCP 8.5. Due to the changing climate, the yields of peanut and sugar beet are projected to drop only marginally. Other crops, including cotton, rapeseed, soybean and maize may see positive yield impacts from climate change. Among these crops, cotton will have the most significant increase in yield due to climate change, followed by rapeseed, soybean, and maize. Under RCP 8.5, cotton yield is projected to increase by 1.74% in 2030 and 4.24% in 2050. Compared with cotton, the positive impacts of climate change on soybean and maize yield are rather small such that their yields would increase by <0.5% in 2050 under RCP 8.5. To concord with crop sectors in CAPSiM model, we estimate oilseed yield change as the average of changes in rapeseed and peanut yields weighted by harvest area in 2015.
Table 1 The crop production (Mt) and climate change impacts on crop yield of China under RCP 2.6 and RCP 8.5 (%).
Production (2012) 2030 2050
RCP2.6 RCP8.5 RCP2.6 RCP8.5
Wheat 121.02 −2.28 −3.39 −4.83 −9.39 Rice 204.24 −0.56 −0.78 −1.34 −2.60 Maize 205.61 0.33 −0.01 0.25 0.31 Soybean 13.60 0.26 0.08 0.31 0.42 Cotton 6.84 0.73 1.76 1.74 4.24 Rapeseed 14.00 −0.17 0.18 0.03 0.61 Peanut 16.69 −0.20 −0.20 −0.37 −0.20 Sugar beet 11.74 −0.35 −0.14 −0.65 −0.38
Note: The base year is 2012. Source: The production in 2012 comes from the CAPSiM database; the yield change comes from Wang, 2016.
3 The interested readers can contact the corresponding author about the data source, detail estimation method and results.
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2.2. Climate change shocks for biophysical yields of crops in the rest of World
The estimates on climate change impacts on crop yields for other countries are based on the biophysical simulations of a process- based crop model by the International Food Policy Research Institute (IFPRI). Annual yield changes of major crops, i.e., wheat, maize, rice, and soybean, in response to climate change are listed in Table 2 for the world's14 regions/countries. The yield changes of these crops are estimated using the Decision Support System for Agrotechnology Transfer (DSSAT) model and IPCC RCP scenarios for 2011–2050 with the base year of 2010. As shown in Table 2, climate change is projected to cause different yield changes among major crops in other countries under RCP 2.6 and RCP 8.5. Most countries would see a reduction in their annual average crop yields in response to climate change. Notice that USA, Argentina and Brazil, who are also the main exporters of maize and soybean, would have serious yield losses by 2050. Particularly, the annual average decrease in maize yield will be >0.4% for USA and Brazil, and 0.2% for Argentina under RCP 8.5. While the soybean yield will fall by >0.3% for Brazil, and >0.15% for USA and Argentina under RCP 8.5. The supply of these crops, also regarded as the major agricultural commodities imported by China, will be significantly threatened by climate change, ensuing a major global hike in their respective prices. Furthermore, we can also find that Canada would benefit from climate change in both maize and soybean yield by 2050 (increase by 0.05% per annum (p.a.) for maize and 0.14% p.a. for soybean under RCP 8.5). Moreover, most countries are found to have negative impacts of climate change on rice and wheat yields by 2050. Australia and New Zealand are projected to experience the biggest decrease in wheat yield (−0.40% p.a.), while USA have the biggest decrease in rice yield (−0.27% p.a.) under RCP 8.5. At the same time, Japan and European Union would benefit from climate change in terms of both rice and wheat yields under RCP 8.5.
Here we want to note that using climate shocks for China and the rest of world from dissimilar sources in this study can result in some inconsistencies. In fact, our motive is to include the effects of adaptation by farmers to reflect the real impacts of climate change, which we do through our econometric estimation for China. As China is our main study region, our priority is to make sure that the results of China have high accuracy. However, due to the unavailability of data for all the other countries, it is impossible to do the econometric estimation for the rest of world, for whom we use the simulation results from a biophysical model. Additionally, a comparison between our econometric results and the biophysical simulation results for China could reveal if there are large dis- crepancies between both sets of yield shocks. Here, we find that the two methods have similar results for the impacts (for example, under RCP8.5 in 2050, the econometric results for rice and soybean are −2.6% and 0.42% respectively; while the biophysical simulation results for rice and soybean are −3.2% and − 0.87% respectively). We think that the crop yield losses for other countries without consideration of adaptation might be slightly overestimated, so the results of our study might also be somewhat over- estimated in our simulations of economic models.
3. Simulation methodology and scenarios
3.1. Simulation Model
In order to consider the domestic market responses to climate change impacts on China's agriculture, we have used a widely recognized agricultural partial equilibrium model (China Agriculture Policy Simulation Model, CAPSiM). The model was developed at the China Center for Agriculture Policy (CCAP) in the mid-1990s as a partial equilibrium model for analyzing policies affecting
Table 2 The annual impacts of climate change on crop yield in the rest of the world to 2050 (%).
RCP 2.6 RCP 8.5
Rice Wheat Maize Soybean Rice Wheat Maize Soybean
Australia & New Zealand −0.02 −0.27 −0.12 −0.09 −0.19 −0.40 −0.28 −0.28 Japan 0.15 −0.09 −0.04 0.01 0.16 0.09 −0.18 −0.01 Korea −0.01 0.23 −0.35 −0.03 −0.06 0.24 −0.66 −0.07 Indonesia −0.01 0.00 −0.17 −0.08 0.00 0.00 −0.33 −0.13 Malaysia −0.04 0.00 −0.17 0.12 −0.08 0.00 −0.42 −0.05 Philippine −0.03 0.00 −0.18 −0.11 −0.05 0.00 −0.38 −0.24 Thailand −0.05 0.05 −0.38 −0.23 −0.14 −0.00 −0.79 −0.31 Vietnam −0.09 0.03 −0.22 −0.12 −0.18 0.03 −0.57 −0.26 Canada 0.00 0.05 0.15 0.17 0.00 −0.03 0.05 0.14 USA −0.09 0.06 −0.22 −0.03 −0.27 0.07 −0.63 −0.21 Argentina −0.03 −0.10 −0.05 −0.01 −0.12 −0.15 −0.25 −0.15 Brazil −0.07 −0.04 −0.14 −0.13 −0.18 −0.18 −0.41 −0.34 EU_28 0.07 0.13 −0.03 0.08 0.04 0.06 −0.20 0.00 Rest of World −0.07 0.05 −0.13 −0.15 −0.20 −0.01 −0.37 −0.37
Note: The base year is 2012. Source: Simulation results from IFPRI.
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China's agricultural production, consumption, prices, and trade (Huang & Li, 2003; Li & Huang, 2004). Since then, CAPSiM has been periodically updated and expanded, while the recent versions of the CAPSiM are designed to track changes in trade liberalization, urbanization, and climate change (Yang, Huang, Rozelle, & Martin, 2012; Huang et al., 2017). In CAPSiM, the crops sectors are more disaggregated and account for >90% of China's agricultural output. The model covers 21 agricultural commodities: including rice, wheat, maize, other coarse grain, sweet potato, potato, soybean, edible oil crops, cotton, vegetables, fruits, other crops, as well as six livestock products and three fishery sectors. The accompanying database of CAPSiM has been updated to 2015 according to the official statistics from China's National Bureau of Statistics and National Customs. CAPSiM can investigate the climate change impacts on China's agriculture to reveal the response from local markets, with the assumption that climate change effects from other countries do not transcend to China via trade.
Then to consider both the market and trade responses simultaneously, we also used the linked national and global equilibrium model (CAPSiM-GTAP) to assess the climate change impacts on agriculture. GTAP (Global Trade Analysis Project) model is a well- recognized multi-country, multi-sector computable general equilibrium model, and is often used for international trade analysis (Hertel, 1997). GTAP model has the advantage of simulating global price changes of agricultural commodities in response to climate change. However, in contrast to the China module of GTAP model, the CAPSiM model also has the following advantages: first, CAPSiM is a partial equilibrium model of China's food market presenting the supply and demand in volumetric (quantity) terms. Whereas, GTAP model is a general equilibrium model using dollar values for supply and demand relationships. For food markets, quantity impacts are very important for capturing the effects of climate change or any other shocks. This is one of the reasons that researchers usually rely on partial equilibrium models to project the quantity level results for the future (for example, FAO-OECD Agricultural Outlook; USDA Agricultural Projections). Second, most of the key parameters of CAPSiM model are derived from the empirically based studies conducted by CCAP, in contrast to generalized parameters used in GTAP model. Third, the base data of CAPSiM has been updated to more recent year (2015) reflecting China's market structure more precisely, while the latest database of GTAP model is based on market conditions in 2011. Moreover, the CAPSiM based projections on future food market for China are also widely accepted in China. We, therefore, have higher confidence in CAPSiM results in comparison to results from the China module of GTAP model. Overall, a linked model between CAPSiM and GTAP offers the best of both individual models such that we can transmit the effects of other countries' climate shocks to China via trade, while simultaneously using a more precise and detailed national economic model. Finally, to map the sectors between the CAPSiM and GTAP model, the GTAP version 9 database is aggregated into 15 regions and 25 sectors (Appendix Table 1).
Following Horridge and Zhai (2005), we established a linkage module between CAPSiM and GTAP model to evaluate the climate change impacts while considering both the responses of market and trade concurrently. The key idea of CAPSiM-GTAP linking method, as proposed by Horridge and Zhai (2005), is to transmit the global price changes from GTAP model into the national model through trade. Specifically, in CAPSiM, the global demand price for China's food export and the global supply price for China's food import are exogenous and are updated using the projection of OECD-FAO agricultural outlook (OECD/FAO, 2018) under the baseline scenario. Under our proposed first scenario (climate change scenario considering only domestic market response under RCP 2.6 and RCP 8.5 using CAPSiM), as we do not consider the global price changes caused by climate change in other countries, we keep the global demand price for China's food export and the global supply price for China's food import same as the baseline scenario (only use CAPSiM model as given in Appendix Table 3). Under our proposed second scenario (climate change scenarios considering both the domestic market response and the trade response using linked CAPSiM – GTAP model), we proceeded in three steps: 1) we assume that climate change only affects China and therefore we only shock China's crop yields and keep the crop yields for all other countries unchanged in GTAP model. Ideally, if the structure for China's economy in both CAPSiM and GTAP model were similar to each other, we would expect to have the same results from this simulation as in the first scenario. However, as China is represented differently in both models, we anticipate that our CAPSiM model can better reflect China's food market than the China module in GTAP model. 2) We assume that climate change strikes all over the world, so we shock all countries' crop yields in GTAP model. 3) We take the difference of global food prices between step 1 and 2 (step 2 - step 1) as akin to the impacts of climate change in other countries on China's food market. Thus, we incorporate the difference in global price between the two steps into CAPSiM to reflect the impacts of climate change in other countries on China's food market through trade (of course, under the second scenario, we shock both the crop yields and the global food prices– the global demand price for China's food export and the global supply price for China's food import– in CAPSiM) (see Appendix Table 3).
3.2. Baseline scenario
For analyzing the impacts of climate change on China's agriculture, we establish a baseline scenario towards 2050 for both GTAP model and CAPSiM. The GTAP baseline is constructed by recursively updating the database such that given GDP targets are met through given exogenous estimates of factor endowments i.e. skilled labor, unskilled labor, capital, natural resources, and population. The procedure and the exogenous macro assumptions are discussed in details in Hertel (1997) and Walmsley, Dimaranan, and McDougall (2006). For the baseline in CAPSiM, several key assumptions are used for the baseline scenario concerning GDP growth, population growth, urbanization rate, urban and rural households' income growth, and agricultural technology advancement (for in depth discussion see Huang et al., 2017).
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In the baseline projection, China's agricultural production will continuously increase in the future, with a simultaneous and significant rise in the imbalance between agricultural production and demand. Demand for feed grains will grow faster than their domestic production, leading to declining self-sufficiency rates.4 By 2050, domestic production of rice and wheat will almost meet China's domestic demand, both reaching high self-sufficiency rates of over 95%. However, for maize, which experienced over-supply in recent years mainly due to policy interventions in China, the demand will increase significantly in coming decades due to rising demand by livestock production. If China does not implement tariff rate quota (TRQ)5 in the future, China's maize import is projected to surpass 40 million tons by 2050, leading to a self-sufficiency rate of <85%. Similar to maize, soybean import is projected to cross 100 million tons in 2050, resulting in a self-sufficiency rate of <10% for China. Demand for sugar and edible oils will be significantly higher than their respective domestic productions, leading to decreasing self-sufficiency ratios for both commodities. In contrast, domestic production of vegetables and fruits is projected to increase in pace with domestic demand, ensuring almost full self- sufficiency in the future.
China's livestock supply-demand balance mostly depends on policies governing feed grain trade and grassland development. CAPSiM projection shows that aquatic products will almost keep supply-demand balance requiring minimal import. However, li- vestock self-sufficiency may undergo significant changes due to many uncertainties surrounding its demand and supply. If China were to remove the import limitations on feed grain and thus make way for domestic livestock production fed by cheap imported feed grain, pork and poultry could retain high self-sufficiency rates. In contrast, livestock imports in China will significantly increase mainly due to maize import limitation (e.g. TRQ) and inadequate grassland development. In the latter situation, CAPSiM projections show that in addition to considerable pork and poultry imports, China will import large quantities of beef, mutton and dairy by 2050, and will have self-sufficiency rates ranging over 70–80% across different livestock products.
3.3. Climate change scenarios
In the CAPSiM settings, percent change of crop yield is a linear function of the percentage change of crop price, input prices (including fertilizer, land, and labor), as well as other factors (such as climate change conditions). Thus, climate change impacts on crop yields discussed in section 2 are transmitted into the crop production module in the CAPSiM through shifting the crop yield changes. Meanwhile, crop yield changes are simulated in GTAP model as the shift to total factor productivity of the crop sectors. In Roson and Mensbrugghe (2010), variations in agricultural yield are modeled as changes in multifactor productivity for agricultural activities, so that output volumes vary despite using the same mix of production factors (they used the ENVISAGE model—a general equilibrium economic model). In Nelson, Mensbrugghe, et al. (2014), for the general equilibrium economic models, the yield shocks of climate change are implemented as shifts in the land efficiency parameters of the sectoral production functions; while for the partial equilibrium models, the shocks were introduced as additive shifters in a yield or supply equation. Robinson, van Meijl, Valin, and Willenbockel (2014) also discussed the incorporation of yield shocks into general/partial equilibrium models. It can thus be concluded that regardless of the model type i.e. general or partial equilibrium, some studies chose to shock TFP; while the others shock land efficiency. In our study, for the CAPSiM, the shocks are introduced as additive shifters in crop yield; for GTAP model, crop yield changes are simulated as changes in total factor productivity (TFP) of these crop sectors. Because we used the linked model, we kept the shock methods consistent between CAPSiM and the China module in GTAP.
We constructed two separate climate change scenarios to simulate the impacts of climate change on China's food supply, prices, trade and self-sufficiency, and examine the market and trade responses. 1) Climate change scenarios with considering market re- sponse (using CAPSiM) under RCP 2.6 and RCP 8.5; 2) climate change scenario with considering both the market response and the impacts on rest of the world (ROW) (using linked CAPSiM-GTAP model). Comparing changes in biophysical crop yields with changes in crop production estimated using CAPSiM only could reveal the response of domestic market in buffering climate change impacts, because CAPSiM model keeps food import and export prices unchanged. The linked CAPSiM–GTAP model, on the other hand, allows the food import and export prices to change with changes in global food prices, which are projected by the GTAP model. A com- parison between the results from CAPSiM and CAPSiM-GTAP model could reveal the response of global trade in buffering climate change impacts (Appendix Table 3).
4. Simulated results for climate change impacts on China's agriculture
The following section describes simulated results for climate change impacts on China's agricultural production, prices, and trade based on the CAPSiM and the linked CAPSiM-GTAP simulations in 2015–2050. Comparing the CAPSiM results with the biophysical impacts of climate change can reveal the response of domestic market in buffering climate change impacts. Then the assessment on climate change impacts considering the response of global trade will be discussed based on the simulation results from the linked CAPSiM-GTAP model. To this end, percentage changes indicated in the text refer to the difference of agricultural production, prices and trade without and with climate change.
4 The self-sufficiency rate is defined as the ratio of domestic food production to food supply (production plus net import) 5 The maize import quota is set at 7.2 Mt. in 2017, and a 65% tariff will be imposed on the imported maize beyond the quota.
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4.1. Climate change impacts on China's agricultural production
Climate change will have varying impacts on China's future crop production. From the CAPSiM simulations, rice, wheat, and sugar will have production losses due to climate change both under the RCP 2.6 and RCP 8.5 (Row 1–2, Table 3), wherein wheat is projected to have the highest production reduction by 2050 (−1.61% under RCP 2.6 and − 4.28% under RCP 8.5). Notice that the climate change impacts on production of these crops are less than the yield losses estimated by the econometric model. Wheat production loss in 2050 under RCP 8.5 (4.28%) is less than half the yield loss due to climate change (9.39%). This indicates that the domestic market evidently plays an important role in dampening climate change impacts. When the climate change hits crop pro- duction, the farmers improve their production practices in light of their previous experience under similar situations, which at least partially reduces the production losses caused by climate change. Farmers are likely to increase frequency and strength of field management, such as irrigation, weeding, adopting drought-resistant varieties, among others. These results signify the important role that the domestic market can play in buffering climate change impacts.
More interestingly, some crops with positive yield changes will end up having production reduction (Table 3). For example, by 2050 maize will have slight yield increase under RCP 8.5 (0.31%, Table 1), however, its production is projected to decrease under RCP 8.5 (−0.64%, Table 3). The mechanism at action is that rice and wheat are mostly domestically produced and their yields, in contrast to maize, are more seriously affected by climate change in China. Keeping in mind the importance of rice and wheat, the farmers would increase their production by not only improving field management, but also by taking agricultural inputs (e.g., land and labor) away from the positively affected crops (like maize). As a result, the positive impacts of climate change on maize yield would be offset by declining inputs of land and labor, and even render maize output to decline. Moreover, both soybean and oilseed crops have slightly positive output impacts due to climate change, except in 2050 under RCP 8.5 (Table 3). Similar to maize, the substitution effects between crops would offset the slight yield increase for soybean and oilseed crops brought by climate change. In
Table 3 The impacts of climate change on crop production in China under RCP 2.6 and 8.5 (%).
RCP 2.6 RCP 8.5
CAPSiM-GTAP CAPSiM CAPSiM-GTAP
2050 2030 2050 2030 2050 2030 2050 Rice −0.27 −0.46 −0.28 −0.55 −0.30 −0.67 −0.21 −0.22 Wheat −0.92 −1.61 −0.97 −2.21 −1.95 −4.28 −1.92 −4.03 Maize 0.24 0.20 0.40 3.58 −0.11 −0.64 1.01 1.93 Soybean 0.38 0.29 1.48 2.98 0.00 −1.47 4.26 16.75 Cotton −0.48 0.74 −0.10 2.35 2.07 3.57 2.49 9.30 Oilseed 0.06 0.17 0.11 0.23 0.19 −0.10 0.41 0.72 Sugar −0.11 −0.21 −0.15 −0.45 −0.32 −0.66 −0.53 −1.53
Note: The base year is 2012. Source: CAPSiM and CAPSiM-GTAP simulations.
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Fig. 1. Comparison of crop physical impacts and production changes (%) in the CAPSiM and CAPSiM-GTAP model in 2050 under RCP 8.5 (the base year is 2012).
W. Xie, et al. China Economic Review 62 (2020) 101256
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addition, cotton production would benefit from climate change by 2050 by relatively lower margins under both RCP 2.6 (0.74%) and RCP 8.5 (3.57%). This is because cotton yield increases are much large by 2050 (1.74% for RCP 2.6 and 4.24% for RCP 8.5) although partly offset by the substitution effects.
Further, the climate change impacts in other countries will cause cross border ripple effects and will further soften the impacts of climate change on China's agriculture (Fig. 1). For example, while soybean production in 2050 will decrease slightly under RCP 8.5 (−1.47%) in the CAPSiM results, the same is projected to significantly increase (16.75%) in the CAPSiM-GTAP linkage model. This effect could be attributed to opposite impacts of climate change on soybean yields in China and the other countries. While soybean yield is projected to increase slightly in China (Table 1), the yields for main exporters, such as Brazil, Argentina, and USA, are all projected to decrease significantly (Table 2). Soybean output reduction in the aforementioned global exporters would cause severe shortage in the global market, which will further incentivize the farmers in China to improve soybean production. Consequently, China's soybean production would expand in the CAPSiM-GTAP results. A similar effect of international trade can also be found on maize production, which has a slight decrease in the CAPSiM results in 2050 under RCP8.5 (−0.64%) but a slight increase in CAPSiM-GTAP results (1.93%) (Fig. 1). Although both rice and wheat outputs would decline in CAPSiM simulation under RCP 8.5, the output reductions in the CAPSiM-GTAP results are lower than those in the CAPSiM results (Fig. 1). For example, wheat output would reduce by 4.28% in the CAPSiM results in 2050 under RCP8.5, and by 4.03% in the CAPSiM-GTAP results. This set of results shows that after we consider the role of international trade in climate change assessment, the negative impacts of climate change on China's agriculture will be further reduced, at least partially.
4.2. Climate change impacts on China's agricultural prices
The prices of the negatively affected crops under climate change would increases in domestic market by 2030 and 2050 both under RCP 2.6 and RCP 8.5. In CAPSiM simulation, the market clearing mechanism dictates that when climate change causes yield
Table 4 The impacts of climate change on crop price in China under RCP 2.6 and 8.5 (%).
RCP 2.6 RCP 8.5
CAPSiM CAPSiM-GTAP CAPSiM CAPSiM-GTAP
2030 2050 2030 2050 2030 2050 2030 2050 Rice 1.75 2.92 1.52 2.91 1.61 4.55 2.81 7.41 Wheat 3.85 6.83 3.71 7.17 5.49 15.47 8.56 22.91 Maize 0.16 0.31 0.23 6.35 0.00 0.23 2.95 8.86 Soybean −0.02 −0.02 1.64 6.73 −0.03 0.10 7.06 30.27 Cotton 0.28 −0.27 1.02 3.82 −0.58 −0.77 0.54 10.42 Oilseed −0.05 −0.15 0.11 0.42 −0.27 0.10 0.56 3.00 Sugar 0.24 0.32 0.24 0.58 0.23 0.51 0.81 2.06
Note: The base year is 2012. Source: CAPSiM and CAPSiM-GTAP simulations
Table 5 Impacts of climate change on crop net import under RCP 2.6 and 8.5 (%).
RCP 2.6 RCP 8.5
CAPSiM CAPSiM-GTAP CAPSiM CAPSiM-GTAP
2030 2050 2030 2050 2030 2050 2030 2050 Rice 9.86 22.16 16.31 61.88 15.35 43.93 1.66 −50.37 Wheat 10.86 20.33 13.22 43.11 22.87 56.81 16.69 30.87 Maize −1.11 −0.47 −1.50 −10.87 0.64 1.69 −4.74 −1.70 Soybean −0.06 −0.05 −0.99 −3.31 −0.02 0.28 −3.75 −13.71 Cotton 0.60 −0.62 0.12 −1.97 −2.60 −3.00 −3.12 −7.81 Oilseed −0.11 −0.35 −0.15 0.80 −0.38 0.46 −0.62 0.14
Note: The base year is 2012. Source: CAPSiM and CAPSiM-GTAP simulations
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reduction, domestic production of the crops will decrease, and consequently, the inadequate domestic supply will raise the local prices. Rice, wheat, and sugar would have their local price to increase by highest margins in response to yield reduction caused by climate change. For example, wheat would have the largest price increase in 2050 of around 6.83% under RCP 2.6 and 15.47% under RCP 8.5 (Table 4), because it would experience the worst yield damage. Rice will see a moderate price hike by 2050 of 2.92% under RCP 2.6 and 4.55% under RCP 8.5. Moreover, the domestic prices of all other crops will also increases in 2050 under RCP 8.5 except for cotton. Consistent with its positive yield shock, cotton would have a reduction in local price of 0.58% in 2030 and 0.77% in 2050 under RCP 8.5. However, while maize will experience slight yield increase under both RCP 2.6 and RCP 8.6, its domestic price for China will increase marginally, mainly due to the substitution effects mentioned in section 4.1.
As compared to the CAPSiM results, the domestic prices for all the crops will increase by much higher margins if we consider the response of international trade using linked CAPSiM-GTAP model (Table 4). Climate change have significant impacts not only on crop prices in China, but also on the crop prices in other countries. The global prices would increase sharply for the crops with high negative yield changes due to climate change such that China will be unable to import these crops at the new price levels. As a result, the reduced supply will lead to a sharp rise in China's domestic crop prices. Our results show that domestic prices of wheat and soybean would further increase greatly in the linked CAPSiM-GTAP results, mainly because China's main trading partners will suffer more severe yield reduction for these crops.
4.3. Climate change impacts on China's agricultural trade and self-sufficiency
In addition to crop production and prices, climate change will also significantly affect China's trade in these agricultural com- modities. In the CAPSiM results, the crops with negative yield shocks, especially rice and wheat, will see increase in their net imports in 2030 and 2050 (Table 5). Wheat is projected to have the most significant increase in net import in 2050 both under RCP 2.6 (20.33%) and RCP 8.5 (56.81%). Compared with around 4% production reduction of wheat in 2050 under RCP8.5, the seemingly large percentage increase (56.81%) in wheat net import is not actually large in volume terms as wheat import has very small share in China's total wheat demand. Other crops, including cotton, oilseed, and soybean, are expected to have slight reductions in their net imports in response to climate change, as their respective yields would increase slightly in China.
On the other hand, net imports of the crops in the CAPSiM-GTAP results differ from those in the CAPSiM results. Though China's domestic prices of crops would rise due to climate change, global crop prices would also increase due to reduced production in several major producing countries. If the global crop prices increase more than the increase in China's crop prices, China would inevitably reduce its net imports of the crops. For example, in 2050 under RCP 8.5, China's net import of wheat is projected to increase by 56.81% in the CAPSiM results, but the increase is reduced to 30.87% in the CAPSiM-GTAP results (Table 5). Similar to wheat, other crops also have lower net imports in the linked CAPSiM-GTAP results, e.g., China's net import for soybean will fall by 13.71% (>10 Mt) in 2050 under RCP 8.5 as compared to 0.28% increase of net import for soybean in the CAPSiM results.
Though climate change would threaten China's self-sufficiency in many agricultural commodities, the crop self-sufficiency rates will increase when considering the climate shocks in other countries. Compared to the baseline scenario, crops experiencing negative yield shocks will have decreasing self-sufficiency rates in the CAPSiM results (Table 6). Among these crops, wheat has the largest decrease in self-sufficiency rate in 2050 (by 0.48 percentage points under RCP 2.6 and 1.37 percentage points under RCP 8.5), which is consistent with the fact that wheat happens to be the crop with the most significant output reduction and net import increase. Under RCP 8.5 scenario, all other crops will have lower self-sufficiency rates by 2050 compared to 2010, except for cotton, which benefits most from climate change. The overall self-sufficiency rate of major cereals6 in 2050 would decrease by 0.21 percentage points under RCP 2.6, and 0.65 percentage points under RCP 8.5. On the other hand, in the CAPSiM-GTAP results, all crops would have higher self-sufficiency rates as compared to the corresponding numbers in the CAPSiM results. For example, soybean's self-
Table 6 Impacts of climate change on crop self-sufficient rate under RCP 2.6 and 8.5 (absolute percent change).
RCP 2.6 RCP 8.5
CAPSiM CAPSiM-GTAP CAPSiM CAPSiM-GTAP
2030 2050 2030 2050 2030 2050 2030 2050 Rice −0.05 −0.11 −0.08 −0.29 −0.08 −0.21 −0.01 0.23 Wheat −0.24 −0.48 −0.28 −1.00 −0.50 −1.37 −0.37 −0.78 Maize 0.16 0.13 0.23 2.85 −0.09 −0.46 0.70 0.71 Soybean 0.06 0.04 0.31 0.77 0.00 −0.21 1.04 4.02 Cotton −0.27 0.34 −0.05 1.07 1.15 1.63 1.38 4.24 Oilseed 0.02 0.07 0.04 −0.08 0.08 −0.08 0.15 0.08 Sugar −0.06 −0.13 −0.08 −0.26 −0.18 −0.40 −0.27 −0.83
Note: The base year is 2012. Source: CAPSiM and CAPSiM-GTAP simulations
6 Major cereals include rice, wheat, and maize.
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sufficiency rate would increase by 0.46 percentage points (0.77–0.31) in 2050 under RCP 2.6 and 2.98 percentage points (4.02–1.04) under RCP 8.5 when considering the climate shocks in other countries. These results further show that when considering the climate shocks in other countries, China's agricultural self-sufficiency will increase.
5. Conclusions and policy implications
Agriculture, an important sector in China, is mandated to feed over 1.3 billion people of the country and provide important inputs for many industries. Such prospect, however, is likely to be threatened by the yield damages caused by climate change. The previous studies on climate change effects on agriculture in China did not account for the buffering capability of local market and international trade. To fill this gap in the literature, we assess climate change impacts on China's agriculture and responses from market and trade using an agricultural partial equilibrium model, CAPSiM, and its linkage model with GTAP model (CAPSiM-GTAP). In this paper, the climate change impacts are examined during 2020–2050 under RCP 2.6 and RCP 8.5 scenarios. Our results show that climate change would have significant effects on agriculture production of China but with large variations among crops. Under the worst climate change scenario, i.e., RCP 8.5, wheat production is projected to decline by around 9.4% by 2050, the biggest production reduction among the crops. The results also suggest some evidence of the adaptation capability of market response to climate change wherein farmers intensify agronomic inputs, improve field management and adjust production structure. When we add the market response to the mix, production loss for wheat under RCP8.5 reduces to only 4.3%. Global agricultural trade provides additional adaptation capability to climate change damage for China, where the country can further avoid crop production losses and raising its self- sufficiency of important food crops, at least partly. When considering both domestic market and international trade responses si- multaneously, wheat production loss under RCP 8.5 would reduce further to around 4%.
Our results have important policy implications for national adaptation plans. First, the adaptation policies should prioritize the crops based on the severity of production losses. Specifically, the investments in adaptation measures should be channeled to more negatively affected crops and to the ones that play more vital role in national food security. Secondly, the policies facilitating market integration and free trade would help to buffer climate change impacts. In general, when climate change strikes, farmers intrinsically increase agronomic inputs (labor, irrigation, pesticide, and others) to adapt to climate change, because they expect high prices in light of their previous experience of high price due to climate change. More so, if the domestic market and international trade are free of distortions and barriers, wherein the price will increase to some reasonable extent in times of climate change. Then in the subsequent crop season, farmers will increase inputs as high as they can to prevent production losses based on their experience with the price increase during previous climate changes. On the contrary, if the market is cluttered with interventions or the trade is restricted, farmers cannot experience general price change due to previous climate changes, and when climate continues to change, they may not increase inputs to that extent. Thus, in addition to the hard measures for adaptation (such as investment in irrigation system), the soft measures (e.g. reducing market intervention, reducing import tariffs and import quotas or other trade barriers) are recommended in order to reduce production damages caused by climate change. Thirdly, to optimize adaptation plans, we need to learn more from farmers who respond to changing climate according to the market and trade signals, and then improve and mainstream the practices adopted by farmers into national adaptation development plans. The farmer's adaptation measures carried by themselves are much important in adapting to climate change, including increasing number of irrigations and other field man- agement measures. The only thing we need to do is to keep markets free and remove trade barriers.
Acknowledgements
The authors acknowledge their respective financial supports from Ministry of Science and Technology, China (2012CB955700), National Natural Sciences Foundation of China (71503243; 71873009; 71333013), NSFC-CGIAR (71161140351), Australian Centre for International Agricultural Research (ADP/2010/070), and National Social Science Fund of China (16AJL009).
W. Xie, et al. China Economic Review 62 (2020) 101256
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W. Xie, et al. China Economic Review 62 (2020) 101256
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Appendix Fig. 1. Average and standard deviation change of temperature and precipitation for different crops during their growing season by 2050 (Base year: 2012).
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- Climate change impacts on China's agriculture: The responses from market and trade
- Introduction
- Climate change shocks for biophysical yields of crops in China and the rest of world
- Climate change shocks for biophysical yields of crops in China
- Climate change shocks for biophysical yields of crops in the rest of World
- Simulation methodology and scenarios
- Simulation Model
- Baseline scenario
- Climate change scenarios
- Simulated results for climate change impacts on China's agriculture
- Climate change impacts on China's agricultural production
- Climate change impacts on China's agricultural prices
- Climate change impacts on China's agricultural trade and self-sufficiency
- Conclusions and policy implications
- Acknowledgements
- Appendix
- References
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The Pacific Review
ISSN: 0951-2748 (Print) 1470-1332 (Online) Journal homepage: https://www.tandfonline.com/loi/rpre20
Linking trade and environment in emerging economies: Korea’s ambition for making green free trade agreements
Annie Young Song
To cite this article: Annie Young Song (2021) Linking trade and environment in emerging economies: Korea’s ambition for making green free trade agreements, The Pacific Review, 34:2, 321-350, DOI: 10.1080/09512748.2019.1672771
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Linking trade and environment in emerging economies: Korea’s ambition for making green free trade agreements
Annie Young Song
The University of Hong Kong, Hong Kong, Hong Kong
ABSTRACT In trade negotiations, developed countries have been frontrunners in advo- cating environmental protection whereas developing countries were reluctant to link environmental protection to their trade agreements. However, the recent trend of including environmental provisions (EPs) in free trade agree- ments (FTAs) shows a policy change in some of the emerging economies. Not only did they adopt EPs but they also actively introduced a comprehen- sive set of EPs – environmental chapters. Using the Republic of Korea (Korea)’s FTAs, this study explores why emerging economies come to include environmental chapters in their FTAs. Existing studies have offered explana- tions focusing on domestic politics. Yet, they do not specify the impact of the interaction between domestic and international politics. Through the lens of the two-level games, this study finds that the combination of domestic and international pressures plays an important role in making green FTAs. First, Korea has been pursuing environmental leadership at the world stage during the Korea-US FTA (KORUS) negotiation. Against this background, the US’ proposal to include an environmental chapter has reverberated within domestic politics. Second, the costs of implementing this environmental chapter were low. After the KORUS, Korea has been including similar environ- mental chapters because these chapters were cost-effective ways to promote its enduring environmental leadership. This finding has far-reaching implica- tions for facilitating emerging economies’ green FTAs.
KEYWORDS Environment; free trade agreements; Korea; emerging economies; environmental leadership
Introduction
Traditionally, developed countries have been leaders in international envir- onmental politics. The European Union (EU) took on a leadership role in mitigating the effects of climate change (Kilian & Elgstr€om, 2010; Parker &
CONTACT Annie Young Song aysong@connect.hku.hk The University of Hong Kong, Room 825, The Jockey Club Tower, Centennial Campus, Pokfulam Road, Hong Kong, Hong Kong. � 2019 Informa UK Limited, trading as Taylor & Francis Group
THE PACIFIC REVIEW 2021, VOL. 34, NO. 2, 321–350 https://doi.org/10.1080/09512748.2019.1672771
Karlsson, 2010; Schreurs & Tiberghien, 2007; Vogler, 2005) and introducing sustainable development policies (Burchell & Lightfoot, 2004; Vogler & Stephan, 2007). Similarly, the United States (US) also provided the leader- ship on multilateral efforts of environmental protection (Ivanova & Esty, 2008; Sussman, 2004). Meanwhile, developing countries were believed to share a disapproving tendency toward international environmental policies (Jinnah, 2017, p. 287). They were mainly concerned that the same environ- mental standards across countries would increase production costs and thereby reduce economic gains from international trade (Bhagwati, 2003; Shahin, 2002).
However, emerging economies have been playing a growing role in environmental governance recently (Jinnah, 2017; Koo & Kim, 2018; Papa & Gleason, 2012; Perkins, 2013; Sommerer & Lim, 2016). They began to eagerly participate in multilateral environmental negotiations (Najam, 2005). Over the last few decades, this evolving role has been also evident in the trade-environment nexus. Emerging economies have been including environmental provisions (EPs) in free trade agreements (FTAs). Furthermore, they began to actively introduce environmental chapters cov- ering a comprehensive set of EPs as a must-element in their FTAs.
EPs describe how countries commit to protect environmental quality upon signing FTAs. They can be included in trade-related chapters or com- prise a whole chapter in an FTA text. For example, the most common type of EPs – general exceptional clauses – indicates a situation where guaran- tees the sovereign rights to implement environmental measures in trade between FTA countries (OECD, 2007). In the Korea-US FTA (KORUS), article 23.1. general exceptions in the chapter 23 exceptions states that ‘the Parties understand that the measures referred to in Article XX(b) of the General Agreement on Tariffs and Trade (GATT) 1994 include environmental measures necessary to protect human, animal, or plant life or health, and that article XX(g) of the GATT 1994 applies to measures relating to the con- servation of living and non-living exhaustible natural resources’. By incorpo- rating the GATT’s Article XX, this EP ensures that a policy measure can be implemented for the purpose of environmental protection when trading products and services deteriorate environmental quality. In other cases, sev- eral EPs together constitute an environmental chapter including commit- ments to implement domestic laws and international environmental treaties. In the KORUS, the first EP of the environmental chapter outlines that both countries should ensure ‘levels of protection’ in order to maintain environmental quality by adopting or modifying environmental laws and policies (article 20.1) while other EPs in the chapter address environmental issues beyond local environmental problems by establishing environmental cooperation projects (article 20.8) and reaffirming the commitments to multilateral environmental agreements (article 20.10). The design of
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environmental chapters may vary from one FTA to the others depending on negotiating countries. Regardless, they signify important meanings that FTA countries give to environmental protection in the context of trade (Bastiaens & Postnikov, 2017). In this sense, the inclusion of environmental chapters has a noteworthy implication on how countries prioritize environ- mental protection in trade policies.
Whether EPs are legally binding or simply symbolic depends on their contents (Abbott & Snidal, 2000). Their wordings are important determi- nants for deciding their legal bindingness. Ambiguous wordings make it dif- ficult to enforce and monitor the commitments made in EPs. For example, when they use encouraging languages such as ‘encourage’, ‘promote’, and ‘endeavor’, they are not mandatory and therefore cannot be legally binding (IISD & UNEP, 2017). Other EPs with less ambiguous wordings may impose legal obligations. When they make specific commitments such as providing opportunities for public participation by submission, they are legally bind- ing (IISD & UNEP, 2017). This nature of EPs in legal bindingness may affect whether countries include them in the trade agreements. Symbolic and non-binding EPs would incur low risks while legally binding EPs may create high risks in the case of violating the commitments made in EPs.
During the FTA negotiation, countries can modify and manipulate the wordings of EPs in environmental chapters. In this sense, developing coun- tries may choose the inclusion of non-binding EPs in the chapters in order to minimize the potential costs of implementation and enforcement.1 This option may provide more room for developing countries to include envir- onmental chapters. Despite this expectation, some of emerging economies still hesitate to include environmental chapters in FTAs. They tend to include environmental chapters when they sign FTAs with advanced econo- mies (Morin et al., 2018). For example, Singapore included an environmen- tal chapter in its FTA with the US in 2003. However, this adoption did not continue in Singapore’s next FTAs. Similarly, The US-Colombia FTA signed in 2006 did not lead to the inclusion of environmental chapters in Colombia’s subsequent FTAs.
In contrast, the Republic of Korea (Korea) has been including environ- mental chapters in its FTAs since the KORUS. Initially, Korea appeared to be unfavorable for incorporating EPs, let alone environmental chapters, into FTA. Developing countries were concerned that the inclusion EPs may lead to introducing new environmental regulations, which consequently require the implementation of new technology or investment in existing produc- tion sites (Copeland & Taylor, 2013). To their view, EPs represent protection- ist interests from developed countries ‘in order to reduce competition from imports’ (Bøas, 2000, p. 416). Given their inadequate capability to comply with environmental regulations, developing countries had been reluctant to include EPs in FTAs (Bhagwati, 2003; Shahin, 2002). Korea was one of these
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countries struggling to adopt the trade-environment nexus. As a country relying on trade-led economic growth, it was difficult to agree on the inclu- sion of EPs in an earlier trade negotiation. In 1994, Korea had argued that EPs in trade deals should aim to resolve a specific environmental issue at a minimal level if necessary. Otherwise, there was no need to link the envir- onment and trade (Korean Department of Environment, 1994, p. 49). In fact, prior to the Korea-US (KORUS), Korea’s FTAs – with Chile (2003), Singapore (2005), European Free Trade Association (2005), Association of Southeast Asian Nations (2009) and India (2009) – did not include environmental chapters. Deviating from this earlier FTAs, Korea included an environmental chapter in the KORUS for the first time. Thereafter Korea constantly includes a full-fledged chapter on the environment without specified laws or poli- cies. (refer to Figure 1).
The case of Korea’s FTAs provides useful insights into emerging econo- mies. Korea is the first emerging economy2 from a non-Western hemi- sphere, which has been actively initiating the inclusion of environmental chapters in FTAs without a legal mandate. I do not argue that it is the first case of including EPs given that existing studies have shown that develop- ing countries adopted EPs in their FTA with the US or the EU. I argue that it is the first case of including environmental chapters without a legal man- date in all FTAs including the ones with developing countries after the KORUS. Despite the lack of supporting laws or policies incorporating the trade-environment nexus, Korea has demonstrated a consistent practice of
Figure 1. Negotiation chronology of Korea’s FTAs. Note 1: Countries in highlighted squares are Korea’s FTA partners. Note 2: The FTA partners in bold-lined boxes included environmental chapters in their FTAs with Korea.
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including environmental chapters in FTAs. To this date, besides Korea’s FTAs, there is no case in a non-Western hemisphere where a country has been consistently including environmental chapters in its FTAs.
Existing scholarship highlighted the role of developed countries from a Western hemisphere in diffusing the norm of including EPs in FTAs (Bastiaens & Postnikov, 2017; Jinnah, 2011; Jinnah & Lindsay, 2016; Jinnah & Morgera, 2013). For instance, developed countries like the US and the EU pressure their FTA partners to include environmental chapters in FTAs. Their motivations to include the chapters are traced back to domestic poli- cies of linking environment protection to their trade policies. By signing FTAs, developed countries diffuse their norms to their partners which are usually developing countries. While these studies have focused on the diffu- sion process in FTAs between developed and developing countries, they do not fully explain what happens to the trade-environment nexus in FTAs between developing and developing countries or between countries with- out a clear legal mandate. By examining Korea’s FTA, this study intends to fill this research gap.
To examine the factors affecting Korea’s decision to include environmen- tal chapters, this study examined major newspapers3 and government documents published during 2000–2015. This period covers the KORUS negotiation when Korea included an environmental chapter in an FTA for the first time. The case study approach complements the current scholar- ship in the following way. Existing studies employed quantitative methods to illustrate the relationship between trade and the environment (Cole, 2004; Copeland & Taylor, 2013; Frankel & Rose, 2005; Nemati et al., 2019). Although they established a solid understanding of the cases across coun- tries, we do not have a clear understanding of why an emerging economy decides to introduce environmental chapters in their FTAs. In order to delve deeper into this, within-case analysis is an appropriate method.
In the following section, this study overviews existing explanations on the reasons to include environmental commitments in FTAs. The next sec- tion presents a theoretical framework drawing upon Putnam’s two-level analysis (1988). Then, this study demonstrates how domestic and inter- national pressures play an important role in shaping a policy choice to include an environmental chapter in the KORUS. Finally, this study con- cludes with a summary of the finding and implications.
Green FTAs: linking environmental protection and free trade agreements
There are opposing views on linking environmental protection to trade agreements. On one hand, economists insist that trade agreements are not
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‘an appropriate venue for handling international environmental issues’ (Colyer, 2011, p. 2). When the environment and trade are linked in a single agreement, it is difficult to satisfy both demands (Bhagwati, 2008). Besides, they argued that economic development can resolve environmental prob- lems to some extent. According to the environmental Kuznets, an increase in national income eventually leads to reduce environmental pollution. While higher domestic incomes in poorer economies cause further additional envir- onmental damages, higher incomes in richer economies mitigate environ- mental degradation (Aklin, 2015; Dasgupta et al., 2002). In this sense, trade as a driver of economic growth can contribute to protecting the environment (Grossman & Krueger, 1991). On the other hand, environmentalists argue that trade deteriorates environmental quality in the following ways. First, trade causes irreversible environmental degradation because transporting goods and services leaves carbon footprints (Lang & Hines, 1993). Second, trade lowers environmental standards across countries (Copeland & Taylor, 2013). Firms located in countries with higher environmental standards incur higher production costs compared to those in countries with lower environ- mental standards. In order to minimize the environmental compliance costs, these firms are likely to move to the countries with the lower standards (Copeland & Taylor, 2013). Consequently, countries are likely to relax their environmental regulations in order to attract foreign investments. In order to mitigate this race to the bottom phenomenon, the inclusion EPs is a neces- sary policy measure (Rosenberg & Miller, 2000).
The environmental concern had been successfully incorporated into the North America Free Trade Agreement (NAFTA) (Fox, 1995; Johnson & Beaulieu, 1996). It is the first US trade agreement to include a legally bind- ing environmental chapter. Although the effectiveness of this chapter remains debatable, it is an unprecedented case of addressing environmen- tal issues in FTAs. Since the NAFTA, it became an obligation to include environmental chapters for some of the advanced economies including the US and the EU. For others, the inclusion of EPs – at least a general excep- tional clause – has become a norm (Colyer, 2011; Esty, 1994; Repetto, 2000).
Similarly, the recent FTAs in East Asian region demonstrate slow but positive progress towards adopting the trade-environment nexus (Koo & Kim, 2018). This evolving trend speaks to a rise in environmental leadership in developing countries. Their environmental policies have become more active and innovative in tackling global and local environmental problems (Sommerer & Lim, 2016). For instance, Korea has adopted the green growth initiative to show its aspiration for environmental leadership (Blaxekjaer, 2015; Han, 2015; Watson & Pandey, 2014). Similarly, China has been taking on an increasingly active role in climate change governance (Jinnah, 2017). Yet, there has been little research on the causes of the trade-environment
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nexus in developing countries in the context of the rise of environmen- tal leadership.
Existing studies have offered explanations on the inclusion of EPs in FTAs by drawing upon theories of economics and environmental politics. First, protectionist motives influence the policy-making process of linking the environment and trade. The inclusion of EPs affects the economic inter- ests of various interest groups. Importing and exporting industries have a large share of interests in the inclusion of EPs. To these interest groups, EPs are policy measures to level the playing field for environmental regulations cross FTA members (Mah�e, 1997; Rosenberg & Miller, 2000). The implemen- tation of EPs indicates the possibility of an increase in production costs. The industries located in countries with higher environmental standards would have disadvantages compared to the ones with lower standards. For this reason, the former is likely to push for the inclusion of EPs in the FTA (Lechner, 2016). This line of argument relates to the compliance costs in implementing EPs. While low costs of compliance are likely to lead to including EPs, higher costs decrease the chance of including EPs (Milewicz et al., 2016; Morin et al., 2018). In the latter case, the higher costs would drive up the price of exporting products. Consequently, they are likely to lose competitiveness in the international market (Copeland, 2010). Thus, when countries gain economic benefits from exporting products with less stringent environmental regulations, they are likely to oppose the inclusion of EPs. At the other end, countries with stringent environmental regulations are likely to favor the inclusion because EPs can ensure a similar level of environmental standards among FTA countries.
Second, democratic regimes are more likely to have an environment- friendly policy choice than autocratic regimes (Morin et al., 2018). Voters in both developed and developing countries tend to prefer to link the envir- onment and trade (Bechtel et al., 2012; Bernauer & Nguyen, 2015). Their preferences are likely to be translated into electoral pressures in democratic regimes than autocratic regimes. Because elected politicians in democratic regimes inherently seek to remain in power, they are more responsive to voters’ environmental considerations (Carbonell & Allison, 2015; Midlarsky, 1998; Neumayer, 2002). The environmental awareness among the public is closely connected with the advocacy of environmental NGOs (ENGOs). They play a vital role in promoting environmental protection in democracy (B€ohmelt et al., 2015). In this sense, ENGOs can play a crucial role in facilitat- ing environment-friendly trade policies (Fox, 1995; Gallagher, 2004).
Third, advanced economies have been diffusing their environmental norms in FTA negotiations (Horn et al., 2010; Leeg, 2018). Most often, they are the ones actively advocating the inclusion of EPs in FTAs between advanced and developing economies. For example, the US and the EU have
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been promoting their norms on the trade-environment nexus by combining an FTA and EPs which follow with it. The EU established ‘East Asia Policy Guidelines’ in order to diffuse sustainability policies to its FTA partners including Singapore, Korea and ASEAN (Cuyvers, 2014). Likewise, the US has been demanding the inclusion of specific EPs covering the policies on pub- lic participation and effective enforcement of environmental laws in FTAs (Jinnah & Lindsay, 2016).
These studies offered an insightful understanding of why countries include EPs in FTAs. However, their focus on domestic preferences does not explain the diffusion process beyond the first adoption of environmental chapters in developing countries. What happens to their next set of FTAs after they adopt an environmental chapter in an FTA with advanced coun- tries? Once they included the EPs, this choice may have a long-last impact because it takes fewer efforts to repeat the same decision a subsequent trade negotiation (Milewicz et al., 2016). In this sense, they are likely to favor the inclusion of EPs in their subsequent trade deals.
Nevertheless, the impact of the FTA negotiations may not last for subse- quent negotiations. After the inclusion of environmental chapters in the FTAs with the US and the EU, not all countries include the chapters in their subsequent FTAs. For instance, Colombia signed an FTA with the US in 2006 but did not include environmental chapters in later trade negotiations (except in the Colombia–Korea FTA). By contrast, Korea has been including environmental chapters in its FTAs after the KORUS. This sporadic pattern of including environmental chapters among emerging economies alludes that signing FTAs with the US or the EU does not necessarily lead to an environmental-friendly policy choice in subsequent trade negotiations.
In order to provide a fuller picture, this study analyzes the interaction between domestic and international politics. Without the understanding of this domestic-international linkage, it is difficult to identify how emerging economies – once unfavorable to the adoption of EPs – have come to embrace an environmental chapter in their FTAs. This study intends to fill this gap by examining how domestic and international pressures together shape an environment-friendly policy choice in FTAs.
Two-level games: domestic and international pressures
This study draws upon Putnam’s logic of two-level games (1988) to explain Korea’s policy shift from none to the consistent inclusion of environmental chapters. At the domestic level, domestic interest groups pressure the gov- ernment to shift the policy in their favorable direction. Simultaneously, poli- ticians engage in coalition-building with one of these interest groups in order to empower their political influence. At the international level,
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national representatives seek to satisfy domestic pressures and avoid the unexpected consequences of foreign development (Putnam, 1988, p.433). Both domestic and international pressures are crucial factors for policy shifts in international negotiations.
The framework of two-level games has been widely applied to assess the interdependent relationship between domestic and international pressures in environmental negotiations (Evans et al., 1993). For example, negotiating parties to climate change negotiations developed their strategies depend- ing on domestic politics that are shaped by constituents’ interests, eco- nomic status and political regimes (Bailer, 2012). Their strategies are further tailored by the international ambition for climate change mitigation (Falkner, 2016). In this manner, the strategic interaction between domestic and international pressures is a crucial factor in explaining international environmental policies across countries.
Inspired by Putnam’s work (1988), this study demonstrates how both domestic and international pressures have influenced Korea’s policy shift to include environmental chapters in its FTAs. In the context of the interplay of these pressures, the way that the win-sets at domestic and international lev- els overlap impacts on the adoption of a new policy. The win-sets indicate the list of options that a negotiating country can accept in international negotiations. In a given set of domestic pressure or interests, the win-sets define the ‘set of all possible international agreements’ that would ‘win’ the majority of voters (Putnam, 1988, p. 437). In this study, the win-sets represent the possible combination of EPs that Korea or the US can accept in the KORUS given their domestic political situations. When domestic and inter- national win-sets meet at some point and create intersection areas, then a new policy proposed at an international negotiation is likely to be adopted. However, it is less likely to adopt a new policy when they do not overlap.
In order to create overlapping sections, domestic win-sets need to be sufficiently large. The larger size has a higher chance of overlapping with the win-sets of negotiating partners. When the domestic win-sets are smaller, they may have fewer probabilities to overlap and thereby a policy shift is not likely to be viable. This study focuses on the following two fac- tors that influence the size of win-sets: domestic policy preferences and the reverberation of international pressure within domestic politics. First, the size of domestic win-sets determines how much negotiators can offer at a negotiating table. The size can be altered by the distribution of preferences and power among domestic constituents (Putnam, 1988, p. 442) and state- led policies reflecting the preferences of sector-specific interest groups (da Conceiç~ao-Heldt, 2013). When a small group of politicians liaises with domestic interests, they can influence domestic win-sets. Given the size of domestic win-sets, negotiators develop their strategies. The smaller size
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leads to a shorter list of options for negotiators, whereas the larger size presents a longer list (Putnam, 1988, p. 440).
Second, international pressure reverberates within domestic politics and thereby facilitates agreement (Putnam, 1988, p. 456). Reverberation refers to an interaction between domestic and international factors that affect domes- tic win-sets. In this process, international pressure may tip the domestic bal- ance and thus influence the international negotiation (p. 454). For instance, the pressure can alter public opinion or the preference of politicians in a cer- tain way during a negotiation process. By trading their options and making give-and-take deals, politicians may shift their position in order to achieve an agreement. However, their altered preferences are still closely associated with domestic interests. If international pressure reverberates within domestic politics, this process expands the size of domestic win-sets. Therefore, adopt- ing a new policy are likely to produce greater domestic benefits than before.
In the case of Korea’s FTAs, the domestic win-sets for the FTAs prior to the KORUS (X without EP) did not overlap with the win-sets of FTA partners (Y FTA partners with EPs) and were not large enough to cover the win-sets of the US (YUS). However, the combination of international and domestic pres- sures expands the domestic win-sets to the extent of covering the US’ win- sets during the KORUS negotiation (2006–2012) (refer to the highlighted range from YUS to X with EP in Figure 2). As a result, negotiators from Korea and the US could agree to include an environmental chapter in the KORUS. This out- come also affected how Korea’s win-sets were set up in the subsequent FTAs after the KORUS negotiation. The inclusion of an environmental chapter in the KORUS could offer the information on the balance of benefits and costs from including the environmental chapter. This information determines the size of win-sets in the next trade negotiations because the successful outcome influ- ences the future choice of institutional design (Copelovitch & Putnam, 2014). If benefits are higher than costs, the size of win-sets is likely to remain the same. But if costs are higher than benefits, then it is likely that the size of win-sets would be readjusted. The environmental chapter in the KORUS has created larger benefits to Korea, thereby Korea could maintain the expanded size of win-sets in the subsequent trade negotiations after the KORUS.
International level: reverberating within domestic politics during the KORUS negotiation
This section discusses the process of overlapping the win-sets of the US and Korea by examining how the inclusion of environmental chapters has reverberated within Korea’s politics during the KORUS negotiation. First, it describes political voices in the US and Korea shaping the win-sets on the inclusion of environmental chapters. The next part explains how Korea has
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promoted environmental leadership at the world stage during the KORUS negotiation. Finally, it analyses how the inclusion of the environmental chapter in the KORUS negotiation reverberated within domestic politics.
Political voices shaping win-sets of the US and Korea
During the KORUS negotiation, the US pressured Korea to include an envir- onmental chapter. It has been including environmental chapters in its FTAs after the NAFTA signed in 1994. The NAFTA is the first US FTA to include EPs in an environmental side agreement. Gradually, the US incorporated EPs into a chapter of the main text. The 2002 Trade Act has mandated the inclusion of environmental chapters in the US FTAs. Beginning from the FTAs with Chile and Singapore, the US has been including full-fledged environmental chapters in FTAs.
In May 2007, the US Congress passed the Bipartisan Trade Deal (BTD), which further strengthened and expanded the scope of environmental chapters in the US FTAs. There were two main forces shaping the BTD: electoral pressure and domestic industries. First, the BTD represented Democrats’ pro-environmental voice. The Democratic Party has a long his- tory of advocating environmental protection as one of the important polit- ical stances (https://www.ontheissues.org). Since the beginning of the Bush administration in 2001, Democrats have been pressuring the government for the better protection for labor rights and the environment (Palmer, 2007). When Democrats dominated the US Congress after the 2006 November election, they could empower their proposal to push for fairer and greener trade deals. As a result, the Bush administration and Congress reached the BTD, which required to include EPs in FTAs such as the imple- mentation of environmental laws and international environmental agree- ments in the US as well as FTA partners. In this sense, pro-environmental voices from electoral pressure have been translated into US’ policies. Second, the economic downturn shaped domestic supports to include EPs in the US FTAs (Weisman, 2007). The US has suffered a net loss in trade,
Figure 2. Domestic win-sets during the KORUS negotiation. Source: adapted from Putnam (1988) and modified by the author.
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especially during the negotiation period of the BTD. In March 2007, the total value of exports was 126.2 billion US dollars with an increase of 2.2 bil- lion dollars from the total value of exports in February. Despite the increase, the higher total value of imports (190.1 billion US dollars) resulted in a trade deficit (Weisman, 2007). Against this background, there have been preferences to adopt non-tariff barriers in FTAs as a way to reduce the trade deficit.
The BTD introduced new EPs to the environmental chapter of the KORUS: adding a list of multilateral environmental agreements (MEAs), bal- ancing the responsibilities between MEAs and trade, ensuring public partici- pation, and applying a general dispute mechanism to the environmental chapter. When the US proposed to include these EPs in the KORUS, the draft of the agreement had been already concluded – but not officially signed yet (Choe, 2007).
When the US alluded reopening of the trade negotiation to incorpor- ate the requirements of the BTD, Korean protesters became concerned that renegotiation may create the possible changes on trade provisions related to beef products and automobiles (“Man Sets Himself”, 2007). In order to appease public concern, the Korean government announced that there would be no renegotiations on these controversial issues (Choi, 2007). Instead, it adopted the EPs and labor provisions in exchange with concessions from the US (Jin, 2007). First, Korea pro- posed to include a clause specifying the detailed evidence on the nega- tive impacts of EPs and labor provisions on trade and investment in order to prevent the misuse of the dispute mechanism. Second, Korea asked the US to relax the regulation of intellectual property on generic medicine. This demand implied that Korean pharmaceutical companies could reproduce medicines in the case of epidemic diseases and with the 18 months-delay in the application of intellectual property rights. Finally, the US promised to increase the number of visa permissions for Korean professionals. In the face of the US’ pressure to include new EPs in the environmental chapter, Korea could gain its demands which were difficult to attain in the previous rounds of the KORUS negotiation. Overall, both sides have been engaged in an exchange of offers to some extent in the renegotiation process (Jung, 2007).
The inclusion of new EPs did not create much public resistance in Korea; therefore, it has not been a top priority to Korean negotiators. At the begin- ning of the KORUS negotiation, ENGOs aimed to enhance the societal understanding in the trade-environment nexus and to defend the right to protect the environment as public goods (KFEM, 2006a). Yet, their advocacy did not attract much public attention because EPs were related to less tan- gible environmental issues. Their focus on the trade-environment linkage
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quickly shifted towards controversial beef imports by framing food safety as an environmental issue along with other environmental concerns. For instance, they claimed that the misuse of Investor-State Dispute Settlement would threaten a sovereign right to protect the environment. They also expressed concerns over applying lenient emission regulations to the US automobile and ensuring food safety of genetically modified organic food imports (KFEM, 2006b). In order to pressure the government, Korean ENGOs created an alliance with the US’ ENGOs including Defenders of Wildlife, Environmental Health Coalition, Sierra Club, and Friends of the Earth US Branch, Food First/Institute for Food and Development Policy and Washington Fair Trade Coalition (KEA, 2006a). Additionally, they found ways to communicate with the government. In a direct way, they invited govern- ment officials to their forums and shared their plans and views on environ- mental negotiation in the KORUS (KEA, 2006b). In an indirect way, they advocated their concerns in a press conference and submitted the request for an enquiry to the Office of Prime Minister (KEA, 2006c).
While environmental advocacy continued throughout the KORUS negoti- ation, ENGOs focused less on EPs – environmental chapters – and more on negative impacts of trade provisions and imports. There was less attention on improving the understanding of the trade-environment nexus in society. By the time when the amendment to the environmental chapter was heav- ily discussed and concluded in 2007, the trade-environment nexus was not a key issue in domestic politics.
Aspiration for environmental leadership
This section traces the development of environmental policies in Korea from 2000 to 2015 in order to demonstrate how a gradual change in envir- onmental policies affects the size of domestic win-sets. The former President Lee Myung-bak developed environmental policies to advance Korea’s environmental leadership at a world stage beginning in 2008. This renewed preference marked a departure from previous environmental poli- cies. In the past, domestic environmental policies focused on the imple- mentation of domestic environmental policies and aimed to balance economic development and environmental protection. It was not until the late 2000s that Korea explicitly manifested the interests in establishing lead- ership in international environmental politics. This strong aspiration expanded the size of domestic win-sets for international negotiations cover- ing a policy choice to include environmental chapters in FTAs.
The notion of sustainable development permeated the domestic policy- making process beginning in early 2000 under the leadership of the former President Kim Dae-jung (1998–2003). During this period, policy initiatives
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incorporated environmental protection into other policy arenas. For instance, sustainable development policies became overarching national strategies, which balance the priorities between environmental protection and economic development (MOE, 2001). The next administration under the former President Roh Moo-hyun (2003–2008) continued to focus on strategies for promoting sustainable development at the domestic level (MOE, 2006). For instance, the 2007 sustainable development basic law mandated the development of the national and local strategies incorporat- ing sustainable development.
During this period, there were two sources shaping environmental poli- cies. First, governments became more responsive to environmental concerns. Upon the inauguration of the new democratic governments, various political groups and non-state actors began to engage in environmental advocacy (Lim & Tang, 2002). In response to the emerging voices, elected politicians in democratic governments began to incorporate the notion of sustainable development in the policy-making process. Second, there was pressure from the international community on the Korean government to increase environ- mental standards. For instance, Korea’s accession to the Organization for Economic Co-operation and Development (OECD) increased the stringency of domestic environmental policies (“Strong Willingness”, 1993). In the OECD’s initial assessment of Korea’s eligibility, 12 out of 40 categories of environmental policies did not meet the required standards. Upon its acces- sion, Korea adopted environmental regulations proposed during the assess- ment of the OECD membership and reported the progress of environmental policies to the OECD (Yoon, 2000). Additionally, Korea’s participation in inter- national environmental negotiations provided an opportunity to further strengthen domestic environmental policies by complying with international environmental treaties. Prior to the 1992 Rio Summit, Korea did not expect to sign the Climate Change Convention and the Convention on Biological Diversity. However, upon the pressure from the international community, it signed these environmental treaties during the Summit (Kim, 1992) and amended domestic environmental plans in accordance to environmental commitments in the treaties (Gwon, 1992).
Yet, environmental policies during the Kim and Roh administrations were insufficient for shaping a leading role in international environmental governance. They largely focused on resolving domestic environmental issues instead of actively taking environmental initiatives at the world stage. Beginning in 2008, the interests of sustainable development have evolved into the aspiration for environmental leadership. Korea assumed the leader- ship role in international environmental politics (Blaxekjaer, 2015; Han, 2015; Watson & Pandey, 2014). Korea’s environmental policies promoted the aspiration in two ways. Firstly, Korea used the green growth paradigm
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in order to advance the ambition for environmental leadership at a world stage. Second, voluntary commitments to tackle climate change included ambitious targets for emission reduction.
First, Korea has promoted the image of an environmental leader in the following way: advancing a green growth strategy as a domestic policy pri- ority. Green growth refers to as the policy idea that aims to balance eco- nomic growth and development and simultaneously to ensure the suitability of natural resources and environmental services on which humans depend (OECD, 2011). Several international organizations – the World Bank, the OECD, the United Nations Environment Programme and other multilateral development banks – has been supporting the imple- mentation of green growth policies. For instance, OECD members and can- didates joined the Declaration on Green Growth in 2009, which highlighted ‘their efforts to pursue green growth strategies as part of their responses to the crisis and beyond, acknowledging that green and growth can go hand- in-hand’. In the following year, G8 political leaders adopted the Seoul Action Plan embracing the commitments to green growth (Morgera, 2010). Against this background, Korea underlined green growth policies in the national paradigm. The label of ‘green growth’ came along with all types of policies including renewable energy, construction, research, and develop- ment sectors (MOE, 2006).
This environmental aspiration came from strong political leadership. On the 60th Anniversary of the Founding of the Republic of Korea in 2008, the former president Lee Myung-bak (2008–2013) prioritized low carbon green growth policies and the goal of becoming a global green pioneer.
Today, on the occasion of the 60th anniversary of the founding of the Republic of Korea, I want to put forward ‘Low Carbon, Green Growth’ as the core of the Republic’s new vision … It is also especially important for Koreans to win respect in the international community … the very first images that come to foreigners’ minds are labor-management disputes and street rallies. In this context, if the nation wants to be labeled as an advanced country, it will be necessary to improve its image and reputation significantly (The Blue House, 2008).
In addition to domestic policies, international cooperation policies ech- oed with the green growth strategy. At the 2008 G8 Tokyo Summit, Korea proposed the launch of the East Asia Climate Partnership (Na, 2008), which was a way of ‘showing the country’s dedication to combating global cli- mate change and promoting green growth’ and ‘promot[ing] the image of an advanced country contributing to environmental assistance’ (Korean Government, 2012, p. 8). Taking on a leadership role in this Partnership, the President announced the financial contribution of 200 million US dollars during 2008–2012 in order to assist the implementation of green growth strategies in developing countries in East Asia and beyond.4
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This goal of garnering the leadership reputation led to an increase in the cooperation projects on green growth with other emerging economies (MOE, 2009). The bilateral or multilateral cooperation strategies reflected green growth projects ‘to a greater degree’ (Korean Government, 2012). For instance, they aimed to transfer green growth strategy and environmental policies in Vietnam, to install of smart-grids in Ghana, and to cooperate on sustainable development strategy and forestry restoration policies in the Solomon Islands (Korean Government, 2012).
In order to further solidify its reputation as an environmental leader, Korea actively participated in bidding for hosting green growth organiza- tions. At the 2009 Copenhagen conference, the US proposed to establish the Green Climate Fund to assist ‘low-emission and climate-resilient devel- opment’ for developing countries. Korea actively participated in the bidding and obtained an international endorsement for becoming the Fund’s host- ing country (MOSF, 2013). During this period, a national think-tank, Global Green Growth Institute, became an international research agency. This insti- tution started as a national research agency in June 2010. With Korea’s lead- ership aspiration, the institution’s regional branches were set up in Denmark and the UAEs (MOSF, 2013). To Korea, obtaining international endorsement was equivalent to garnering international legitimatization for Korea’s aspiration. (MOSF, 2013). Indeed, Korea seemed to achieve the goal of becoming an environmental leader. For instance, the OECD described that Korea has been ‘inspiration behind the OECD Green Growth Strategy’ (Girouard, 2010). The United Nations of Environment Programme indicated that Korea’s green growth policies were recognized as the best practice (UNEP, 2011).
Second, Korea’s ambitious targets for emission reduction in voluntary pledges advertised the aspiration for environmental leadership. In the 2008 G8 Summit, Korea declared the determination to become an early mover in fighting against global climate change. The pledge was aimed to reduce greenhouse gases by 30% relative to a business-as-usual scenario (BAU) by 2020 in the 2009 Copenhagen Climate Change Conference. This was the highest level recommended by the Intergovernmental Panel on Climate Change to non-Annex 1 countries.
The process of domestic implementation for this goal revealed that Korea did not leave the pledge at a voluntary level. In the domestic imple- mentation, the Framework Act on Low Carbon Green Growth and the Presidential Decree came into effect in April 2010. First, a new regulation laid out a national goal of reducing greenhouse gas emission 30% below BAU level by 2020. Second, the greenhouse gas target management system specified detailed emission targets at a firm-level including industrial, power generation, transportation, building, agriculture, food, and waste
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sectors. Third, the Framework Act provided the legal foundation for intro- ducing an Emission Trading Scheme (ETS). Initially, the Scheme was planned to commence in January 2013. However, this plan faced fierce opposition from a large industry coalition – the Federation of Korean Industries – because there were concerns that new Scheme would lead to an increase in production costs and subsequently to a loss of competitiveness in inter- national markets (Kim, 2016). In 2012, the bill on implementing the ETS had passed successfully in the National Assembly with 148 approving votes out of 151 votes (Cho, 2012). This overwhelming number of favor-votes demon- strates political supports for Korea’s environmental leadership despite industry resistance.
There are two main factors shaping the aspiration for leadership: prefer- ences of an elite group and strategic interests. First, green growth policies have been shaped by a top-down approach (Han, 2015). In February 2009, the Lee administration launched the Presidential Committee on Green Growth (PCGG), which consisted of 47 members of higher-ranking govern- ment officials and experts. PCGG played a major role in the policy-making process of green growth strategies (Han, 2015). In fat, ENGOs did not seem to content with the elite-led green growth strategy. They argued that green growth would not distribute the benefits of environmental protection equally among all Koreans (Watson, 2012, p. 533). In this sense, most of the green growth policies reflected the preferences of an elite group rather than civil society (Jung & Ahn, 2010). Second, the aspiration for environ- mental leadership is potentially associated with national security in the Korean peninsula (Watson, 2012). Korea’s green growth strategy focuses on ‘green development’ which links environmental protection and develop- ment while it abandons a counter-productive brown growth approach (Watson, 2012; Kim & Thurbon, 2015). This elite-led view on developmental- ism holds the assumption that economic development promotes ‘national security and prestige’ (Kim & Thurbon, 2015, p. 216). Economic growth sup- ports the process of nation-building, which then promotes economic stabil- ity and national security in the long run (Kim & Thurbon, 2015). In response to the on-going threat from North Korea, Korea has been attempting to establish an evolving role to address national security issues (Watson, 2018). In this sense, a new paradigm on economic development – green growth strategy – has been closely connected to the goal of ‘establishing long-term national security’ (Watson, 2012, p. 539).
In sum, departing from the earlier policies, Korea’s environmental poli- cies in the post-2008 period appealed to the international community. This distinction is crucial for explaining the process of expanding domestic win- sets during the KORUS negotiation (2006–2012). Prior to the KORUS, Korea’s win-sets did not embrace environmental issues. Only after domestic
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environmental policies crystallized unequivocal aspiration for environmental leadership in 2008, environmental chapters in FTAs could generate polit- ical benefits.
Reverberating within domestic politics
The US’ proposal to include an environmental chapter reverberated within domestic politics. This resonance provided an opportunity for Korea’s win- sets to overlap with the US’ win-sets. The KORUS negotiation was the first time when Korea has faced the option of including an environmental chap- ter in an FTA. Prior to the KORUS, Korea’s FTA partners including Chile, Singapore, EFTA, ASEAN, and India were not interested in having full- fledged environmental chapters in their FTAs. When the US proposed the inclusion of an environmental chapter, Korea could embrace this new way of linking environmental protection in FTAs because of its aspiration for environmental leadership.
By including the environmental chapter, Korea could avoid a high risk of ‘no-agreement’ of the KORUS. It does not imply that the inclusion of EPs was a top priority of the KORUS negotiation. In fact, Korean environmental- ists have shifted their focus mainly on food safety from the trade-environ- ment nexus. Despite the lack of public attention in the inclusion of the environmental chapter, Korea was aware of the 2002 Trade Act which man- dated the inclusion of the environmental chapter. It would have conceded that the resistance to the inclusion of the environmental chapter would not lead to a successful outcome. In this sense, Korea was aware of difficulties if it did not agree to include the environmental chapter in the KORUS.
There are three main reasons that Korea agreed to sign the KORUS with the environmental chapter. First, the Korean government forecasted eco- nomic gains from signing the KORUS. The real GDP was expected to increase by 0.02% and 5.66% in the short and long run respectively. In add- ition to economic benefits, the KORUS would contribute to the domestic labor market by creating new job opportunities (Korean Government, 2011). Second, Korea sought to promulgate international environmental leadership by including the full-fledged environmental chapter in the KORUS (MOE, 2007). The environmental chapter in the KORUS reflected Korea’s desire to appear different from other emerging economies and to become an environmental leader in international environmental politics (personal communication, March 24, 2017). In the World Trade Organization (WTO), emerging economies were hesitant to address international environ- mental standards (Bhagwati, 2003; Shahin, 2002; Vogel, 2013). Because higher environmental standards could increase production costs for their exports, emerging economies have been concerned that their products
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might lose competitiveness in an international market. When it became dif- ficult to incorporate environmental protection in the WTO, advanced economies like Canada, the US, the EU, and New Zealand began to include EPs in their bilateral or regional trade deals. For instance, the US specified the list of EPs to be included in environmental chapters in FTAs as a law (USTR, 2007). In this sense, environmental chapters in FTAs represent the higher environmental standards that environment-pioneering countries would adhere to. Following this policy initiative means that Korea could demonstrate its determination to become an environmental leader and separate itself from other emerging economies. This motivation created an impetus for the KORUS negotiation to penetrate domestic politics.
Finally, the KORUS was expected to offer strategic benefits. Korea’s trad- ing relationship with the US is inherently linked to national security (Sohn, 2019). The diplomatic relationship between Korea and the US dates back to the 1950 US–ROK Mutual Defense Treaty. In response to the on-going threat from North Korea, this partnership is critical for Korea’s national security. Under this condition, the KORUS strengthened the alliance through knot- ting their commercial ties. Additionally, the military vested interests linking to Korea’s environmental leadership would increase bargaining leverage in the security issues on the divided Korean peninsula. Currently, China and the US are the major players of shaping and influencing security policies in both Koreas (Chang & Lee, 2018). In this context, Korea’s evolving role in international affairs becomes an important factor in shaping strategic poli- cies regarding the threat of North Korea (Shin, 2016; Watson, 2018). The leadership aspiration may pave the way for improving Korea’s influence on the Korean peninsula by increasing barging leverage in regional secur- ity issues.
Domestic level: low implementation costs
Starting with the KORUS, Korea included environmental chapters in its FTAs. This consistent practice, however, requires consent from an FTA part- ner because an FTA involves more than one country. In FTA negotiations, Korea’s preferences for environmental chapters were largely reflected in its FTAs. This assumption is possible because Korea has a consistent preference to include the chapters and not all partner shared a similar interest. Among Korea’s FTA partners, Canada, the EU, and New Zealand have a clear trade policy direction highlighting the trade-environment nexus. Other trading partners did not have similar interests in incorporating environmental issues to trade. Australia did not include an environmental chapter in its FTAs, even after the first inclusion in the Australia-US FTA in 2004. Colombia also signed an FTA with the US in 2006 and included an environmental chapter. But it did not continue to include the chapter in a subsequent set of FTAs.
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Their FTAs with Korea were the only cases where they included environ- mental chapters beside FTAs with the US. Regardless of the interests of FTA partners, Korea included similar environmental chapters in its FTAs.
This section explains a domestic factor affecting the implementation and maintenance of the environmental chapters in the KORUS and subsequent FTAs: low implementation costs. This study captures the implementation costs by tracing the introduction of a new domestic regulation followed by institutionalizing an environmental chapter.5 The introduction of new domestic regulations indicates high costs meanwhile no new regulations are measured as low costs.6 This measurement is based on the concept of treaty implementation, which is not equivalent to compliance. While com- pliance indicates conformity of the actual behavior to prescribed behavior, treaty implementation refers to the situation where governments adopt new domestic regulation (Erickson, 2015; Simmons, 1998; Young, 1979). Following the previous studies, the implementation costs are measured by the adoption of domestic rules or regulations that meant to facilitate com- pliance with the environmental chapter.
The environmental chapter in the KORUS incurred low implementation costs. Implementation costs are based on whether changes in domestic regulations are required by wordings of EPs. If an EP requires a change in regulations, implementation costs are high. Otherwise, they are low. When EPs include the phrase such as ‘recognize’, ‘shall ensure’, ‘shall promote’, ‘shall consider’ and ‘are committed to’, they tend to indicate the under- standing of policy actions in the future rather than the immediate imple- mentation of domestic regulations. In the environmental chapter of the KORUS, this wording was included in the following articles: levels of protec- tion, application and enforcement of environmental laws, procedural mat- ters, mechanisms to enhance environmental performance, institutional arrangements, environmental cooperation, environmental consultations, and panel procedure, and relations to multilateral environmental agree- ments. Their implementation costs were low because there was no need to introduce for new regulations as a follow-up measure. For instance, article 20.2. environmental agreements listed a number of multilateral environ- mental agreements and called for the fulfillment of obligations. Because Korea was already a signatory party to the listed MEAs, no additional regu- lations or laws was needed for its implementation. In this sense, these EPs in the chapter did not expect to bring changes to environmental regula- tions and laws directly because Korea’s environmental policies seem to be in line with the environmental standards proposed by the KORUS (Choi, 2012) (Table 1).
Meanwhile, article 20.7. opportunities for public participation is likely to incur a high cost. Because there was no equivalent regulation on FTAs,
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Korea introduced a domestic regulation specifying the procedure of public participation in 2011 accordingly (MOE, 2011). This new regulation detailed an administrative process of reporting a violation on EPs and obligations of governments in response to public submissions. It was difficult to predict the implementation costs of this regulation because there was no compar- able regulation (Choi, 2012). Besides the article on public participation, other EPs did not require high costs. For this reason, the overall implemen- tation costs of the environmental chapter were perceived to be low.
The implementation costs determined Korea’s policy direction in main- taining environmental chapters after the KORUS. Korea included the similar environmental chapters in the subsequently signed FTAs in the post-KORUS FTAs. These chapters included a similar set of EPs and incurred low costs as in the KORUS. In this sense, the expanded size of domestic win-sets did not incur additional burden to Korea.
The low costs paved a cost-effective way to promote environmental lead- ership after the KORUS. The aspiration for environmental leadership remained unchanged after the KORUS. The green growth paradigm was downplayed after the Lee administration (2008–2013). The succeeding administration under the former President Park (2013–2017) forwarded a new national slo- gan, Creative Economy. Despite the lack of domestic political attention, Korea has been attempting to preserve its international reputation as an environmental pioneer even after a change of political leadership.
Korea followed through with the international promises that were car- ried over from the previous government. It continued to pursue the pledges for financial contributions to the Green Official Development Assistance (GODA). The share of the GODA in the total amount of ODA illus- trates Korea’s determination to keep up the international reputation of environmental leadership. The GODA intends to provide financial supports for environment-friendly development in developing countries. The amount has peaked during the Lee administration. In 2012, the share of GODA in the total ODA accounted for 23%, the highest volume since 2006. However, the 2013 figure was decreased to 12.6%, less than half of the previous
Table 1. Implementation costs of EPs in the environmental chapter of the KORUS. EPs in the Environmental Chapter Implementation Costs
20.1 Levels of Protection Low 20.2 Environmental Agreements Low 20.3. Application and Enforcement of Environmental Laws Low 20.4. Procedural Matters Low 20.5. Mechanisms to Enhance Environmental Performance Low 20.6. Institutional Arrangements Low 20.7. Opportunities for Public Participation High 20.8. Environmental Cooperation Low 20.9. Environmental Consultations and Panel Procedure Low 20.10. Relations to Multilateral Environmental Agreements Low
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year’s record. This number was still higher than the level prior to proclaim- ing environmental leadership (in 2006). In this sense, the annual figures of the GODA show the adherence to the previously committed contribution7
and to the aspiration for environmental leadership at a world stage. In sum, the inclusion of environmental chapters in FTAs has been one of
the cost-effective ways to fulfill the aspiration of environmental leadership. The low costs of implementing the environmental chapter explain its inclu- sion in the KORUS. Yet, they attributed to Korea’s decision partially. The low implementation costs alone cannot fully explain the inclusion of EPs. Although the costs of implementing environmental chapters were low, countries did not include the environmental chapters after their first adop- tion in their FTAs. For instance, Singapore signed an FTA with the US including an environmental chapter in 2003. However, a subsequent set of Singapore’s FTAs did not include the chapters. Similarly, Colombia signed an FTA with the US in 2006 but did not include the chapters in its next FTAs. Both countries included environmental chapters in their FTAs with the US because of the low implementation costs. However, they did not continue to include them in subsequent FTAs. Their cases demonstrate that low implementation costs alone do not sufficiently explain the continued inclusion of environmental chapters. Without persisting environmental lead- ership, the expanded win-sets cannot be maintained in subsequent trade negotiations.
Conclusion
This study explores the question of why emerging economies include envir- onmental chapters in their FTAs by examining the case of Korea. Korea included environmental chapters in the KORUS and subsequent FTAs because enduring aspiration for environmental leadership and low costs of implementing environmental chapters expanded and maintained Korea’s win-sets in trade negotiations. First, Korea’s aspiration for environmental leadership at the world stage played a role in expanding Korea’s win-sets. With this evolving aspiration, the US’ proposal to include an environmental chapter in the KORUS has reverberated within domestic politics by generat- ing economic, political and security benefits. During this reverberating pro- cess, Korea’s win-sets overlap with the US’ win-sets. Second, the low costs of implementing the environmental chapter contributed to the expansion of Korea’s win-sets. The inclusion of environmental chapters has been a cost-effective way to promote environmental leadership of emerg- ing economies.
Besides bilateral FTAs, this study may have implications for regional trade deals. EPs can address trade or non-trade related environmental prob- lems. The first type of EPs is included in trade chapters of FTAs. For
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instance, EPs in sanitary and phytosanitary chapters address potential envir- onmental problems in agriculture or food-related industries by granting the rights to implement domestic environmental regulations. Non-trade related EPs address environmental problems within a country or across countries. They may reaffirm the commitments to multilateral environmental agreements. Environmental chapters in this study relate to the latter type of EPs – linking global environmental protection and trade agreements. Emerging economies can include this type of EPs in regional trade deals as a new means to show their increasing environmental leadership.
By discussing the case of Korea’s FTAs, this study shed insights on the fac- tors shaping green FTAs of emerging economies. Echoing with recent studies on the environmental leadership of developing countries, this study finds that there is an evolving trend of including environmental chapters in FTAs of emerging economies from a non-Western hemisphere. This behavior departs from the earlier behavior of emerging economies in trade negotiations. Unlike advanced economies, they were skeptical of linking environmental protection to trade agreements. Yet, an increasing number of emerging economies began to sign green FTAs recently. Following Korea’s FTAs, China began to include environmental chapters in its FTA. For the first time, it included envir- onmental chapters in the FTAs with Switzerland signed in 2013. The subse- quent FTA with Georgia in 2017 also included an environmental chapter. Similarly, the Eurasian Economic Union has joined to make an environmental commitment in trade agreements. In 2016, it signed an FTA with Vietnam including an environmental chapter. As such, emerging economies will con- tinue to sign green FTAs in the future. In this context, environmental chapters can provide a viable way to incorporate environmental protection into FTAs between developing–developing countries.
Notes
1. In the China–Georgia FTA signed in 2016, an environmental chapter (chapter 9. Environment and Trade) has nine provisions regarding levels of protection, enforcement of environmental measures including laws and regulations, multilateral environmental agreements, review of environmental impact, cooperation, and consultations. None of them is legally binding.
2. This study focuses on Korea’s position in the global trade system and therefore identifies Korea as an emerging economy for the following reasons. First, Korea’s capital–labor ratio shows that it is yet to be referred to as a developed country. The capital–labor ratio captures productivity and has been used as an indicator of economic development in the field of economics (Mason & Sakong, 1971). The higher ratio indicates a more intense use of capital than labor whereas the lower ratio indicates vice versa. Typically, developed countries are likely to have higher ratios whereas developing countries tend to have lower ratios. Korea has achieved rapid economic development through industrialization. Yet, its capital–labor ratio (2519.74) did not reach the level of other advanced economies such as the US or the UK (3333.61, 2573.86 in 2006, respectively, in the Penn World Table from Feenstra et al., 2015). Second, Korea has identified itself as a developing country in the World Trade Organization (WTO). Countries can choose their status in the WTO as developed or developing countries. Their responsibilities and benefits vary depending on their status as developed or
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developing countries. For instance, the WTO Agreements offer special and differential treatment provisions, which allow developing countries to delay the implementation of the Agreements for the purpose of enhancing their trading opportunities (https://www. wto.org/english/tratop_e/devel_e/dev_special_differential_provisions_e.htm). Although Korea is a member of OECD and demonstrates the soft power as a middle-power country, the above features highlight Korea as an emerging economy within a group of developing countries in the topic of trade.
3. I used a search platform provided by Korea Press Foundation (www.bigkinds.or.kr) in order to retrieve archived articles from Korean newspapers. A full URL was not available in some of the referenced articles.
4. The commitment was made in the East Asian Climate Partnership (http:// 17greengrowth.pa.go.kr/).
5. This cost does not include administrative costs of setting up the committee or drafting environmental cooperation projects. In this sense, low costs do not mean there was no cost of implementation at all. Instead, I argue that there are relatively low costs of implementation by focusing on adoption of new regulation in Korea’s legal system.
6. I traced the policy process from ratification to implementation of the KORUS by searching on Korean law database, the National Assembly reports and the list of bill proposals.
7. The government pledged to increase the share of the GODA of the total ODA to 25% achieved by 2013, and 30% by 2020.
Acknowledgments
I am indebted to participants of the trade and the environment panel at the ISA Annual Convention 2018, San Francisco for their valuable feedback to further advance this study. I also want to thank Dr. Injoo Sohn, Dr. Jung Eun Kim, Dr. Richard Hu, and Dr. Debby S.W. Chan for reading an earlier draft and offering encouragement and comments. Finally, thanks to the anonymous reviewers for their valuable and constructive comments.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes on contributor
Annie Young Song completed a doctoral study at the Politics and Public Administration Department at the University of Hong Kong and will be conferred her Ph.D. degree in December 2019. Her research interests cover environmental politics and policy, energy governance, international political economy and envir- onmental politics in East Asia.
ORCID
Annie Young Song http://orcid.org/0000-0002-2530-1251
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- Abstract
- Introduction
- Green FTAs: linking environmental protection and free trade agreements
- Two-level games: domestic and international pressures
- International level: reverberating within domestic politics during the KORUS negotiation
- Political voices shaping win-sets of the US and Korea
- Aspiration for environmental leadership
- Reverberating within domestic politics
- Domestic level: low implementation costs
- Conclusion
- Acknowledgments
- Disclosure statement
- References
20211126015701sakurai_et_al_2020.pdf
sustainability
Article
Systemic Risk in Global Agricultural Markets and Trade Liberalization under Climate Change: Synchronized Crop-Yield Change and Agricultural Price Volatility
Yoji Kunimitsu 1,*, Gen Sakurai 2 and Toshichika Iizumi 2
1 Institute for Rural Engineering, National Agriculture and Food Research Organization, Tsukuba 305-8609, Japan
2 Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba 305-8604, Japan; sakuraigen@affrc.go.jp (G.S.); iizumit@affrc.go.jp (T.I.)
* Correspondence: ykuni@affrc.go.jp; Tel.: +81-29-838-7542
Received: 26 October 2020; Accepted: 15 December 2020; Published: 21 December 2020 ���������� �������
Abstract: Climate change will increase simultaneous crop failures or too abundant harvests, creating global synchronized yield change (SYC), and may decrease stability in the portfolio of food supply sources in agricultural trade. This study evaluated the influence of SYC on the global agricultural market and trade liberalization. The analysis employed a global computable general equilibrium model combined with crop models of four major grains (i.e., rice, wheat, maize, and soybeans), based on predictions of five global climate models. Simulation results show that (1) the SYC structure was statistically robust among countries and four crops, and will be enhanced by climate change, (2) such synchronicity increased the agricultural price volatility and lowered social welfare levels more than expected in the random disturbance (non-SYC) case, and (3) trade liberalization benefited both food-importing and exporting regions, but such effects were degraded by SYC. These outcomes were due to synchronicity in crop-yield change and its ranges enhanced by future climate change. Thus, SYC is a cause of systemic risk to food security and must be considered in designing agricultural trade policies and insurance systems.
Keywords: agricultural trade liberalization; computable general equilibrium (CGE) model; crop model; food security; simultaneous crop failure; social welfare
1. Introduction
Agricultural production is highly influenced by climate conditions [1]; therefore, climate change may add volatility to agricultural production due to crop failures [2,3] or too abundant harvests [4]. Simultaneous crop failures and abundant harvests would enhance synchronized yield change (SYC) for major grains and increase global imbalances in food supply and demand, resulting in extremely volatile agricultural prices [5]. The Intergovernmental Panel on Climate Change (IPCC) suggested that global agricultural prices could increase up to 29% from current levels by 2050, due to the synergy of a decline in agricultural production under future climate change and an increase in the world population [6]. Agricultural price surges should be a risk to food security in the global economy. Thus, clarifying the influence of such risk is an important academic and agricultural policy issue.
Wright [7] analyzed the causes of past spikes in agricultural commodity prices and showed that speculation and rising oil prices were not reasons behind price spikes. The actual explanation was the imbalance in supply and demand, in addition to changes in the grain stock level of global markets. Meanwhile, Headey and Fan [8] stated that the rise in agricultural prices in 2007 was strongly
Sustainability 2020, 12, 10680; doi:10.3390/su122410680 www.mdpi.com/journal/sustainability
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influenced by factors other than the supply–demand balance in the food market, and that the impact of supply shocks caused by climate change was relatively small. However, they could not ignore climate shocks as a causative factor. Based on these previous findings, it is useful to apply analytical methods that consider supply and demand in the food market in any analysis of agricultural price volatility.
The computable general equilibrium (CGE) model is a powerful analytical tool for analyzing the supply–demand equilibrium and equilibrium prices simultaneously. Many previous studies in the field of agricultural and environmental economics have applied CGE models to assess agricultural trade policy [9], earthquake disasters [10,11], climate change [4,12–15], and environmental policy [16–19]. Notably, in the field of environmental evaluation, some previous studies employed a method that combined global climate model (GCM), crop model, and CGE model to evaluate climate impacts on economies [12,15,19]. However, there seem to be a few quantitative studies that applied CGE model to analyze the systemic risks in global food markets under future climate change.
Nevertheless, Tanaka and Hosoe [20] and Hosoe [21] used a global CGE model to examine the impact of productivity shocks and the effects of trade liberalization on global food markets by performing a Monte Carlo simulation analysis. Their results showed that food-importing countries further increased imports after trade liberalization, but the decline in agricultural prices raised the social welfare level and eliminated negative influences of domestic production decline. Therefore, these studies concluded that trade liberalization could decrease domestic agricultural price volatility despite overseas productivity changes and would not reduce the food security level of importing countries such as Japan.
The conclusions of the research by Tanaka and Hosoe [20] and Hosoe [21] are consistent with portfolio theory in that overall fluctuation can be reduced by diversifying the combination of products from different areas with different price fluctuation patterns, regardless of import and domestic production. However, these studies have not analyzed the impact of correlated shocks among major grains or with other countries. Theoretically, shocks correlated across traded products increase fragility in the market and can be a “systemic risk” [22]. In other words, risk hedging to mitigate future price fluctuations can be accomplished by combining stocks whose prices fluctuate independently. However, the combined stock price may drastically fall if there is a correlation in the price fluctuations. Systemic risk originates in tightly coupled systems and is characterized by interlocking effects, tipping points and nonlinear developments [23]. If climate change enhances a correlation among crops or among producing countries, agricultural trade liberalization may not be useful as a mechanism to hedge risk under climate change.
This study analyzes whether future climate change increases systemic risk via SYC. When and if such systemic risk exists, this study attempts to evaluate quantitively the influence of SYC (a source of systemic risk) on the global food market with consideration of trade liberalization. The methodological features of this study are as follows. First, we integrate the CGE model and two crop models to treat prediction results of five GCMs for a more general prediction-based assessment. Second, economic impacts of climate shocks generated by these models are compared to the results from the stochastic simulation method to quantify the difference between SYC and non-SYC situations. Policies for addressing systemic risks may include the allocation of production sources, insurance systems to mitigate crop failures, and government involvement to improve forecast accuracy.
The remainder of this paper is organized as follows. Section 2 explains the methods used in this analysis, including the crop model and the CGE model. Section 3 demonstrates the chronological robustness of the SYC structure in the global production systems of four major grains and detects the impacts of systemic risk on food markets caused by SYC, considering future climate change and agricultural trade liberalization. Based on these results, Section 4 notes some policy implications. Finally, Section 5 summarizes the results of the analysis and presents the conclusions.
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2. Materials and Methods
The analysis measures the degree of SYC among countries and among crops under future climate change using the predictive results of the crop model, and it verifies the chronological robustness of SYC. Subsequently, fluctuations in agricultural prices are calculated by inputting the results of the crop model into a global CGE model. These economic results are then compared with a no-correlation case, in which SYC among countries and crops does not exist, by applying a Monte Carlo simulation analysis according to Hosoe [21]. Figure 1 shows these analytical procedures.
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The analysis measures the degree of SYC among countries and among crops under future climate change using the predictive results of the crop model, and it verifies the chronological robustness of SYC. Subsequently, fluctuations in agricultural prices are calculated by inputting the results of the crop model into a global CGE model. These economic results are then compared with a no-correlation case, in which SYC among countries and crops does not exist, by applying a Monte Carlo simulation analysis according to Hosoe [21]. Figure 1 shows these analytical procedures.
Figure 1. Analytical framework. T1 period is 1961–2014, and T2 and T3 periods are, respectively, 2015–2050 and 2051–2100. FAO statistics refer to the crop-yield statistics of the Food and Agriculture Organization (FAO). GCM is global climate model, and CGE model is computable general equilibrium model. EV means equivalent variation.
2.1. Crop Model and Data
The possibility of simultaneous crop failure has already been verified by previous studies using historical data [3], as well as future prediction data [2]. This study instead examines the chronological robustness of correlation coefficients on annual yield change, including small fluctuations in addition to crop failure and too abundant harvests.
Crop-yield data of four crops—rice, wheat, maize, and soybeans—were prepared over three periods: T1 (1961–2014), T2 (2015–2050), and T3 (2051–2100). T1 corresponds to the period covered by the crop-yield statistics of the Food and Agriculture Organization (FAO); FAO data were detrended to remove technological progress (Appendix A). T2 and T3 are yield periods under future climate change; yield data for these periods were produced by crop models of four major crops. The crop models employed in this study are PRYSBI2 [24] and pDSSAT [25,26]. The PRYSBI2 is a hybrid type of process-based and the empirical model, consisting of biological equations with observed parameters from field experiments, as well as uncertain parameters estimated using the Markov chain Monte Carlo method with statistical yield data. Meanwhile, pDSSAT is a pure process-based model that replicates crop growth stages based on biological functions. These process-based models can estimate yields by considering the changes in daily climate conditions predicted by GCMs. Both models were employed in the Agricultural Model Intercomparison and Improvement Project (AgMIP), and the yield data of these crop models were estimated according to the AgMIP common protocol [25]. Müller et al. [27] showed that the reproducibility of PRYSBI2 was 0.260 n.s., 0.303 n.s., 0.527 **, and 0.279 n.s for maize, wheat, rice, and soybean, respectively, whereas that of pDSSAT was 0.888 ***, 0.652 ***, 0.215 n.s., and 0.496 ** (in the same order). Here, ***, **, and n.s. indicate significant at p < 0.001, significant at p < 0.05, and not significant at p < 0.1. These numbers were calculated from time series of crop-yield estimations and FAO’s statistical data in the global scale after detrending. Some crops had insignificant correlations due to small sample size, but generally temperature and precipitation can only explain approximately 30% of year-to-year variations in the average global yields of measured crops [1], which corresponds to a correlation coefficient of 0.55. Considering this value, the reproducibility of the two crop models meets the level available for analysis.
T1 period stage
FAO statistics
Crop yields (5 major grains)
c h ro
n o lo
g ic
a l
ro b u s tn
e s s
o f
S Y C
E c o n o m
ic in
fl u e n c e s
o f
S Y C
T2, T3 period
Food price, Social welfare level (EV)
Climate conditions (5 GCMs)
2 Crop models
random shocksGlobal CGE model
Figure 1. Analytical framework. T1 period is 1961–2014, and T2 and T3 periods are, respectively, 2015–2050 and 2051–2100. FAO statistics refer to the crop-yield statistics of the Food and Agriculture Organization (FAO). GCM is global climate model, and CGE model is computable general equilibrium model. EV means equivalent variation.
2.1. Crop Model and Data
The possibility of simultaneous crop failure has already been verified by previous studies using historical data [3], as well as future prediction data [2]. This study instead examines the chronological robustness of correlation coefficients on annual yield change, including small fluctuations in addition to crop failure and too abundant harvests.
Crop-yield data of four crops—rice, wheat, maize, and soybeans—were prepared over three periods: T1 (1961–2014), T2 (2015–2050), and T3 (2051–2100). T1 corresponds to the period covered by the crop-yield statistics of the Food and Agriculture Organization (FAO); FAO data were detrended to remove technological progress (Appendix A). T2 and T3 are yield periods under future climate change; yield data for these periods were produced by crop models of four major crops. The crop models employed in this study are PRYSBI2 [24] and pDSSAT [25,26]. The PRYSBI2 is a hybrid type of process-based and the empirical model, consisting of biological equations with observed parameters from field experiments, as well as uncertain parameters estimated using the Markov chain Monte Carlo method with statistical yield data. Meanwhile, pDSSAT is a pure process-based model that replicates crop growth stages based on biological functions. These process-based models can estimate yields by considering the changes in daily climate conditions predicted by GCMs. Both models were employed in the Agricultural Model Intercomparison and Improvement Project (AgMIP), and the yield data of these crop models were estimated according to the AgMIP common protocol [25]. Müller et al. [27] showed that the reproducibility of PRYSBI2 was 0.260 n.s., 0.303 n.s., 0.527 **, and 0.279 n.s for maize, wheat, rice, and soybean, respectively, whereas that of pDSSAT was 0.888 ***, 0.652 ***, 0.215 n.s., and 0.496 ** (in the same order). Here, ***, **, and n.s. indicate significant at p < 0.001, significant at p < 0.05, and not significant at p < 0.1. These numbers were calculated from time series of crop-yield estimations and FAO’s statistical data in the global scale after detrending. Some crops had insignificant correlations due to small sample size, but generally temperature and precipitation can only explain approximately 30% of year-to-year variations in the average global yields of measured crops [1], which
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corresponds to a correlation coefficient of 0.55. Considering this value, the reproducibility of the two crop models meets the level available for analysis.
In the estimation, daily climate conditions and CO2 fertilizer effects were considered according to the representative carbon pathway (RCP) scenario, RCP8.5, that corresponds to the highest CO2 concentration among scenarios mentioned in the IPCC report. Although there are many objections in the field of crop science regarding the magnitude of the effects of CO2 fertilizer [28], such effects can ease the future degradation risk of crop yield and avoid overestimating risk; therefore, this study considered the effects of CO2 fertilizer. The technology level was fixed at its 2000 level, which was before the start year of the simulation, to eliminate the effects of afterward technological progress, such as breed improvement. Influences of flood damage caused by heavy precipitation were ignored, although the effects of drought were considered through changes in soil moisture caused by rainfall.
Daily climatic conditions forecasted by the five GCMs (i.e., HadGEM2-ES, IPSL-5 CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, and NorESM1-M) were entered into the two crop models for the annual crop-yield forecast of each country from 2007 to 2099. Five GCMs’ data on the RCP 8.5 scenario were obtained from the Coupled Model Intercomparison Project 5 (CMIP5) [29], and these were bias corrected according to Hempel et al. [30].
The analysis, then, calculated the correlation coefficients of yields in T1, T2, and T3 periods. As it was useless to compare the climate data of each GCM in a specific year, the correlation coefficient was calculated from the sample corresponding to each crop model and each GCM (2 crop models × 5 GCMs), consisting of year periods in T1, T2, and T3. Therefore, the sample size was the total number of 2 models × 5 models × period, excluding the year when the crop models’ estimation was not successful.
Thereafter, we selected statistically significant combinations of regions and/or crops for which the correlation coefficients of annual yields are significant in each period and investigated whether these combinations in one period can continue to the next period. The number of combinations, which retain significance in correlation coefficients between two periods, was thought to show the degree of SYC robustness.
The analyzed area is a total of 38 countries and/or regions, including 29 countries that are major producers and importers of the four target grains and 9 regions that are integrated based on geographical proximity (Table 1). These integrations are due to a limitation in the ability of CGE model explained later. For simplicity, each country and/or region is simply referred to as a “region” hereinafter.
Table 1. Aggregated regions for analysis.
No. Identifier Country or Region No. Identifier Country or Region
1 AUS Australia 20 URY Uruguay 2 CHN China 21 XSM Rest of South America 3 JPN Japan 22 XCA Rest of Central America 4 KOR Korea Republic of 23 FRA France 5 IDN Indonesia 24 DEU Germany 6 PHL Philippines 25 GBR United Kingdom 7 THA Thailand 26 XEF Rest of Western Europe 8 VNM Vietnam 27 ROU Romania 9 BGD Bangladesh 28 RUS Russian Federation
10 IND India 29 UKR Ukraine 11 PAK Pakistan 30 XER Rest of Europe 12 XAS Rest of ASIA 31 IRN Islamic Republic of Iran 13 CAN Canada 32 TUR Turkey 14 USA United States of America 33 XWS Rest of Middle East 15 MEX Mexico 34 EGY Egypt 16 ARG Argentina 35 XAC South Central Africa 17 BOL Bolivia 36 XEC Rest of Eastern Africa 18 BRA Brazil 37 ZAF South Africa 19 PRY Paraguay 38 XTW Rest of the World
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2.2. CGE Model for Estimation of Agricultural Price Volatility
Annual agricultural price is estimated by a static global CGE model [31] based on the Global Trade Analysis Project (GTAP) 9 database [32], considering the supply–demand balance in the global crop trade markets. The detailed structure of the model is explained by Lanz and Rutherford [31]; hence, this study notes only the following points as the model’s primary features.
First, production is formulated by a nested constant-elasticity-of-substitution (CES) function. Value added is produced by four input factors (i.e., labor, capital, farmland, and natural resources), and total gross production is calculated by combining value added with intermediate inputs, comprised of import and domestic goods. Domestic production and export are further divided from total gross production based on the constant-elasticity-of-transformation (CET) function. Imports are inserted into the production process by a CES-type function based on Armington’s assumption [33]. Second, consumption is formulated by a linear expenditure system (LES)-type function that treats basic consumption and variable consumption separately. Variable consumption is determined by considering the substitutability of each consumption good that is produced from domestically produced and imported goods according to Armington’s assumption. Similarly, investment and government consumption are defined by a Leontief-type function that combines consumption goods comprised of domestic and imported goods. Third, production tax, production factors tax, intermediate input tax, consumption tax, public sector purchase tax, investment tax, export subsidy, and import tariff are all considered to cover the tax systems of each country in the world.
Original GTAP industrial sectors are combined into 12 sectors (Table 2), and countries in the world are merged into 38 regions (Table 1). The parameters of each function are calibrated by the data of 2011 in the GTAP 9 database. The substitution elasticities for production, consumption, government consumption, and trade are also derived from the GTAP 9 database, as well as the Frisch parameter in consumption.
Table 2. Aggregated industrial sectors for simulation analysis.
No Identifier Industrial Sectors No Identifier Industrial Sectors
1 PDR Paddy rice 7 MIN Forestry, fishery, and mining 2 WHT Wheat 8 VOL Vegetable oils and fats 3 GRO Other cereal grains (including maize) 9 PCR Processed rice 4 OCR Other crops 10 OFD Other food products 5 OSD Oil seeds (including soybeans) 11 MAN Manufacturing
6 OAP Animal products and
other agriculture 12 SEV Service
2.3. Simulation Method
The global CGE model is exogenously subjected to disturbances caused by the yield changes of four crops. We assume that these disturbances affect the production of each crop through effective farmland productivity (EFP) in the CES-type cost function (Appendix B). The disturbances here are seemingly time-series data corresponding to the yearly data produced randomly or predicted by crop models based on GCMs’ prediction. However, the shock of the disturbances is assumed to converge in 1 year; therefore, the simulation performs repeatedly static analyses according to the number of disturbance data. The simulation cases considered are as follows.
Case 1 (Random and regionally independent disturbances, non-SYC): The yield changes create random and regionally independent disturbances in EFP, and the current trade structure is maintained (no change in import tariffs and export subsidies). One thousand random shocks are generated based on a lognormal distribution as an assumption. The standard deviation given at the time of random number generation is set to the standard deviation of the estimated yield by the crop models for the period 2007–2014 to match the initial disturbance range with other cases. This case is an estimate during the T2 and T3 periods without SYC and is used as reference for the subsequent Cases 2–6.
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Case 2 (SYC during T2): This case considers the SYC of the four crops that would be expected during the T2 period under climate change. Crop yields with SYC are then assumed to change EFP as follows:
EFPi,r,t = ( YEi,r,t/YEi,r
) , (1)
where, i, r, and t represent the four crop categories, countries, and year, respectively. Further, YE is the yield estimated by the crop model, YE is an average of YE during the years 2007–2014 and is used as the referenced level in the simulation. As there are 36-year estimations by 2 crop models with 5 GCMs, the total number of iterations is 353 (=36 years × 2 crop models × 5 GCMs’ inputs −7 as unobservable data) in the simulation. Furthermore, the current trade structure is maintained.
Case 3 (SYC during T3): Yield data estimated by crop models are during T3, and all other settings are the same as in Case 2. The total number of iterations is 467 (49 years × 2 crop models × 5 GCMs’ inputs −23 as unobservable data).
Cases 4, 5, and 6 (Agricultural trade liberalization cases): These cases correspond to agricultural trade liberalization in Cases 1, 2, and 3, respectively. All regions’ import tariffs and export subsidies for agriculture and food sectors (PDR, WHT, GRO, OCR, OSD, OAP, OFE, VOL, and PCR) are set to 0. Other settings are the same as in Cases 1, 2, and 3, respectively.
Table 3 summarizes the setting conditions for each described simulation case. From the difference between any two cases in this table, the effects of SYC or trade liberalization can be calculated when another condition is set equal. For example, the difference between Case 3 and Case 1 shows the effect of strong SYC when trade liberalization is not considered; meanwhile, the difference between Case 6 and Case 3 shows the effect of trade liberalization with strong SYC. Hereinafter, the notation “Cases X1/X2” implies the ratio of Case X1 against Case X2, whereas the notation “Cases X1 − X2” indicates difference between Cases X1 and X2.
Table 3. Summary of simulation conditions.
Simulation Conditions Case 1 Case 2 Case 3 Case 4 Case 5 Case 6
Synchronized yield change None Weak Strong None Weak Strong Trade liberalization None None None Adopted Adopted Adopted
3. Results
3.1. Robustness of the SYC Structure
Table 4 presents the frequency of the correlation coefficients between regions by magnitudes and periods, calculated using available data from regions on annual crop-yields. The percentage values are the ratios of regions’ number classified according to the magnitude of the correlation coefficient against the total combination number, nt. Similarly, the values presented in Table 5 are calculated based on correlation coefficients between crops.
As shown in Table 4, correlation coefficients were statistically significant in many combinations of regions, showing a high occurrence possibility of similar climatic conditions between two regions, i.e., SYC among regions was recognized. Correlation coefficients between crops (Table 5) also show the existence of SYC between crops. Furthermore, there were more combinations of regions or crops with a positive correlation than a negative correlation. This happened due to the following two influences. First, when climate change progressed worldwide, yields in each region simultaneously decreased or increased, creating the chronological similar trend of yield change in each region where the same crop was planted. Second, in addition to an increase in fluctuation of climate conditions in many regions estimated by GCMs, crop-yield changes became more sensitive to changes in climate conditions. When climatic conditions were close to the biological threshold level, even a small change in temperature caused growth disorders and increased yield variability. Such tendency led to simultaneous crop failures in many regions. Hence, the degree of SYC was increased with the progress of global warming.
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Table 4. Frequency in magnitude of correlation coefficient (r) between regions by crops and period.
Crops Periods n/nt r <−0.4 −0.4 < r <−r_NZ −r_NZ < r < r_NZ r_NZ < r < 0.4 0.4 < r
Rice T1 35/595 16.6% 4.0% 54.5% 4.5% 20.3% T2 35/595 0.3% 5.2% 40.0% 21.5% 32.9% T3 35/595 11.3% 7.7% 13.3% 23.2% 44.5%
Wheat T1 35/595 4.9% 1.7% 82.8% 4.2% 6.4% T2 35/595 4.9% 19.2% 35.6% 30.3% 10.1% T3 35/595 15.3% 9.4% 11.3% 15.6% 48.4%
Maize T1 37/666 9.5% 3.5% 55.1% 3.3% 28.7% T2 36/630 0.0% 6.7% 59.7% 30.3% 3.3% T3 36/630 0.2% 4.6% 9.0% 30.0% 56.2%
Soybeans T1 34/561 4.5% 2.1% 86.1% 2.5% 4.8% T2 36/630 0.0% 7.3% 31.4% 34.8% 26.5% T3 36/630 6.5% 7.8% 16.0% 24.6% 45.1%
Note: The percentage value is a ratio between the number of correlation coefficients falling within the range and the total number of calculations. Further, n is the total number of regions where data were obtained, nt is total number of combinations that are calculated by n · (n − 1)/2, and r_NZ shows the magnitude of correlation coefficients that are significantly different from 0 as compared to the t-statistic value at a 1% significance level. The sample size for calculation in each period is T1: 54 (1961–2014), T2: 356 = 36 (2015–2050) × 5 (GCMs) × 2 (crop models) −4 (unobserved data), and T3: 468 = 49 (2051–2099) × 5 (GCMs) × 2 (crop models) −22 (unobserved data).
Table 5. Frequency in magnitude of correlation coefficient (r) between crops by period.
Periods n/nt −0.4 <r <−r_NZ −r_NZ < r < r_NZ r_NZ < r < 0.4 0.4 < r
T1 4/6 0.0% 50.0% 50.0% 0.0% T2 4/6 0.0% 0.0% 83.3% 16.7% T3 4/6 33.3% 0.0% 50.0% 16.7%
Note: n is the total number of crops, and the sample size for the calculation in each period is T1: 972 = 54 (years in 1961–2014) × 18 (regions), T2: 9720 = 36 (years in 2015–2050) × 5 (GCMs) × 2 (crop models) × 27 (regions), T3: 12,000 = 50 (years in 2051–2100) × 5 (GCMs) × 2 (crop models) × 24 (regions). Other notations are the same as in Table 4.
The stability of the crop yield’s correlation structure was verified by selecting the statistically significant correlation coefficients from each period. We counted combination numbers where the correlation coefficients between T1 and T2 or between T2 and T3 were statistically nonzero at the 1% probability level (“rr_NZ”) and positive (“rr_+”) in both periods. Then, we calculated the ratio of these combination numbers against the total number of combinations (nt). Among these, combinations with higher correlation coefficients in the later period than in the earlier period (“rr_1”) indicate that SYC globally became stronger.
Table 6 summarizes the robustness of the correlation coefficients among regions between T1 and T2 and between T2 and T3. In both T1–T2 and T2–T3, the ratios of “rr_NZ” were obvious, and specifically, the ratios of T2–T3 in analyzing the four crops cross sectionally were 37.5–60.7%. The ratios of “rr_+” in T2–T3 were higher than in T1–T2 for all crops, showing that synchronicity in yield change became stronger over time. Actually, T1 was based on observed crop yield and included short time disturbances that could not be eliminated by detrending, such as rising oil prices [34] and effects of conflicts [7]. Therefore, the correlation in T1 was weaker than in T2 or T3 in which yield changes were influenced only by climate conditions. In T2–T3, rice and soybean had a higher percentage of combinations in “rr_+,” as well as percentages of “rr_1” than for the other two crops.
Tigchelaar and Battisti [2] found that the likelihood of simultaneous crop failure in maize production increased chronologically due to future climate change. Their findings are consistent with our analysis, though only about maize, as shown by the high correlation coefficients in Table 4 and the chronological change (ratios of rr_1) in Table 6.
Table 7 shows the robustness of the correlation coefficients among crops in the same way as Table 6. Here, rr_NZ and rr_+ were higher in T2–T3 than in T1–T2, showing a similarity with the inter-regional tendency. From the results on inter-regional and intercrop correlations, our analysis demonstrates that the yields of four crops in each region of the world tend to fluctuate in the same
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direction. In other words, the SYC structure is robust not only in the past period but also in the future under climate change.
Table 6. Degree of corresponding regions with positive correlations between two periods.
Crops Periods nt rr_NZ rr_+ rr_1
Rice T1→T2 595 30.9% 16.8% 5.9% T2→T3 595 55.8% 50.1% 41.5%
Wheat T1→T2 595 12.6% 6.2% 2.5% T2→T3 595 60.7% 38.3% 37.3%
Maize T1→T2 630 14.6% 10.3% 0.8% T2→T3 630 37.5% 33.5% 32.9%
Soybeans T1→T2 561 6.4% 3.4% 1.6% T2→T3 630 50.0% 46.5% 42.7%
Note: “nt” is the total number of combinations, and “rr_NZ,” “rr_+,” and “rr_1” show which correlation coefficients are statistically nonzero in both periods at the 1% probability level, which are positive in both periods, and which are bigger in latter periods than in former periods, respectively.
Table 7. Degree of corresponding crops with positive correlations between two periods.
Periods nt rr_NZ rr_+ rr_1
T1→T2 6 50.0% 50.0% 50.0% T2→T3 6 100% 66.7% 33.3%
Note: The total number of combinations, nt, was 6 (=3 × 4/2). Other notations are the same as in Table 6.
3.2. Initial Shocks in EFP Given to the CGE Model
Figure 2 shows the average and the standard deviation of EFP. FAO_T1 is the detrended FAO’s yield data, and Cases 1 and 4 are the random disturbance cases without SYC (non-SYC). Cases 2 and 5 and Cases 3 and 6 are SYC cases for the T2 and T3 periods, respectively. In the simulation, the same EFP was set for the case numbers connected by commas (i.e., Cases 1 and 4, Cases 2 and 5, and Cases 3 and 6). Results from the entire world average and only five major regions (i.e., the United States, China, Brazil, Japan, and France) are presented in Figure 2 due to space reasons. These regions were major producers and major importers in the world for four crops targeted in this study.
The EFP values of all 38 regions and those estimated by each crop model (PRYSBI2 and pDSSAT) are shown in the Supplementary Materials (Figures S1–S8). By comparing the results of two crop models, although the ranges of EFP by PRYSBI2 were bigger than those by pDSSAT, similar tendencies of EFP change were found in two models on the four crops. Therefore, we used the average value, standard deviation, calculated by averaging the two models’ estimations, and maximum or minimum values, which were the maximum or minimum values of each model’s estimations as the variable of interest. When calculating these indices, we were, of course, careful not to mix data from different crop models, as well as different GCMs, and to treat data from each model separately.
In this figure, FAO_T1 and Cases 1 and 4 marked almost the same level in average EFP, although these two cases were not reproductions of T1 represented by FAO’s actual data. The coincidence in average values between Cases 1 and 4 and FAO_T1 indicates that these cases did not deviate significantly from the past actual situations and were reasonable predicted values. The standard deviations in the two cases were different in some regions, but on a world average, the ratio of the standard deviations between each case and FAO_T1 was within the range of 0.6–1.6. In the simulation, the non-SYC disturbances were generated based on the values of the crop model from 2007 to 2014, not based on FAO’s actual data, and consequently, differences from FAO_T1 did not affect the subsequent simulation results.
By comparing the non-SYC case of Cases 1 and 4 with the SYC case of Cases 2 and 5 and Cases 3 and 6, the average EFP of rice (PDR) and soybean (OSD) were higher in the SYC case, but that of
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wheat (WHT) and maize (GRO) were lower than in the non-SYC case. In particular, these tendencies were remarkable in Cases 3 and 6 due to the differences in the reflection characteristics of each crop to climatic conditions.
The fluctuation range shown by the standard deviation of EFP became larger in the SYC cases, and especially, Cases 3 and 6 marked the widest fluctuation range. As the average world temperature continues to rise toward the T3 period, which corresponds to Cases 3 and 6, the above tendency implies that future climate change will widen the fluctuation range of crop yield. Among crops, the fluctuation range of OSD was the largest, while that of GRO was small compared to other crops, due to the differences in climatic characteristics of each crop.
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2007 to 2014, not based on FAO’s actual data, and consequently, differences from FAO_T1 did not affect the subsequent simulation results.
By comparing the non-SYC case of Cases 1 and 4 with the SYC case of Cases 2 and 5 and Cases 3 and 6, the average EFP of rice (PDR) and soybean (OSD) were higher in the SYC case, but that of wheat (WHT) and maize (GRO) were lower than in the non-SYC case. In particular, these tendencies were remarkable in Cases 3 and 6 due to the differences in the reflection characteristics of each crop to climatic conditions.
The fluctuation range shown by the standard deviation of EFP became larger in the SYC cases, and especially, Cases 3 and 6 marked the widest fluctuation range. As the average world temperature continues to rise toward the T3 period, which corresponds to Cases 3 and 6, the above tendency implies that future climate change will widen the fluctuation range of crop yield. Among crops, the fluctuation range of OSD was the largest, while that of GRO was small compared to other crops, due to the differences in climatic characteristics of each crop.
Figure 2. Average and standard deviation of effective farmland productivity (EFP) by crops. “FAO_T1” refers to detrended actual yield of the Food and Agriculture Organization (FAO) statistics during the T1 period. Cases 1 and 4 correspond to random disturbances (non-SYC); Cases 2 and 5 and Cases 3 and 6 correspond to synchronized yield change (SYC) produced by the crop models. The values of the whole world (WLD) are the entire world average of each region’s values, and values of “4 Crops” are the average of four crops’ EFPs. The standard deviation of the France’s rice (PDR) is by far the largest, and the value is shown between parentheses above the bar graph.
3.3. Influence of SYC on Agricultural Price
Figure 3 compares the (a) average level, (b) standard deviation, and (c) highest level of estimated agricultural price (P_Agr) for Cases 1, 2, and 3. In this figure, according to the settings of CGE model, the price is represented by an index with 2011 as 1.0.
By comparing the non-SYC case (Case 1) with the SYC cases in T2 period (Case 2) and the T3 period (Case 3) in Figure 3, the average, standard deviation, and highest price levels show the similar tendency. Cases 1 and 2 remained at almost the same level, but Case 3 significantly became highest. In other words, if future global warming progresses within 2 °C as in the T2 period, price and its fluctuation will moderately increase. However, when the average temperature reaches to the high level of 4 °C or higher in the T3 period, the price will rise sharply, and the instability of price fluctuation will significantly increase.
Comparing the differences in these indices, the standard deviation and the highest price in Figure 3 marked large difference between cases than the average price. For example, in the United
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Figure 2. Average and standard deviation of effective farmland productivity (EFP) by crops. “FAO_T1” refers to detrended actual yield of the Food and Agriculture Organization (FAO) statistics during the T1 period. Cases 1 and 4 correspond to random disturbances (non-SYC); Cases 2 and 5 and Cases 3 and 6 correspond to synchronized yield change (SYC) produced by the crop models. The values of the whole world (WLD) are the entire world average of each region’s values, and values of “4 Crops” are the average of four crops’ EFPs. The standard deviation of the France’s rice (PDR) is by far the largest, and the value is shown between parentheses above the bar graph.
3.3. Influence of SYC on Agricultural Price
Figure 3 compares the (a) average level, (b) standard deviation, and (c) highest level of estimated agricultural price (P_Agr) for Cases 1, 2, and 3. In this figure, according to the settings of CGE model, the price is represented by an index with 2011 as 1.0.
By comparing the non-SYC case (Case 1) with the SYC cases in T2 period (Case 2) and the T3 period (Case 3) in Figure 3, the average, standard deviation, and highest price levels show the similar tendency. Cases 1 and 2 remained at almost the same level, but Case 3 significantly became highest. In other words, if future global warming progresses within 2 ◦C as in the T2 period, price and its fluctuation will moderately increase. However, when the average temperature reaches to the high level of 4 ◦C or higher in the T3 period, the price will rise sharply, and the instability of price fluctuation will significantly increase.
Comparing the differences in these indices, the standard deviation and the highest price in Figure 3 marked large difference between cases than the average price. For example, in the United States, the average price in Case 3 was 1.17 times higher than that in Case 1, while its standard deviation was 10 times larger, and its highest price was 2.5 times higher than in Case 1. The average price is related to
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the average yield, while the standard deviation and highest price are related to the fluctuation range of the yield. Therefore, it can be said that the factors that result in price instability are the expansion of the fluctuation range of yields and the synchronicity of fluctuations under global warming, not the level of average crop yields.
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States, the average price in Case 3 was 1.17 times higher than that in Case 1, while its standard deviation was 10 times larger, and its highest price was 2.5 times higher than in Case 1. The average price is related to the average yield, while the standard deviation and highest price are related to the fluctuation range of the yield. Therefore, it can be said that the factors that result in price instability are the expansion of the fluctuation range of yields and the synchronicity of fluctuations under global warming, not the level of average crop yields.
Figure 3. Influence of SYC on the price of agriculture products (P_agr). “WLD” shows the world weighted average of the domestic agricultural price by the production value of each country. Similar comparison by all regions and two crop models is shown in the Supplementary Materials (Figures S9 and S10).
3.4. Effects of Agricultural Trade Liberalization on Agricultural Price Volatility
Figure 4 shows the changes in agricultural price (P_agr) in major countries before and after trade liberalization. To illustrate the net impact of trade liberalization, this figure focused on the ratios of two cases, such as Cases 4 and 1 (Cases 4/1), Cases 5 and 2 (Cases 5/2), and Cases 6 and 3 (Cases 6/3). Cases 4/1, Cases 5/2, and Cases 6/3, respectively, show the net effect of trade liberalization under non- SYC, weak SYC in T2 period, and strong SYC in T3 period.
Although the values are only shown in the Supplementary Materials (Figure S11) due to space considerations, net exports (i.e., exports minus imports) of the agriculture and food products increased in most food-exporting regions and decreased in food-importing regions, and agricultural trade expanded after trade liberalization. Hence, an increase in imports caused a reduction in domestic production in most food-importing countries.
Based on these results, the following can be observed in Figure 4. As shown by Cases 4/1 in the case of non-SYC, trade liberalization increased benefits of food-importing regions. For example, in Japan, as one major importers, agricultural price was decreased by 10% (1–0.90) after trade liberalization, and the range of price fluctuations was narrowed by 97% (1–0.03). As a decrease in
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Figure 3. Influence of SYC on the price of agriculture products (P_agr). “WLD” shows the world weighted average of the domestic agricultural price by the production value of each country. Similar comparison by all regions and two crop models is shown in the Supplementary Materials (Figures S9 and S10).
3.4. Effects of Agricultural Trade Liberalization on Agricultural Price Volatility
Figure 4 shows the changes in agricultural price (P_agr) in major countries before and after trade liberalization. To illustrate the net impact of trade liberalization, this figure focused on the ratios of two cases, such as Cases 4 and 1 (Cases 4/1), Cases 5 and 2 (Cases 5/2), and Cases 6 and 3 (Cases 6/3). Cases 4/1, Cases 5/2, and Cases 6/3, respectively, show the net effect of trade liberalization under non-SYC, weak SYC in T2 period, and strong SYC in T3 period.
Although the values are only shown in the Supplementary Materials (Figure S11) due to space considerations, net exports (i.e., exports minus imports) of the agriculture and food products increased in most food-exporting regions and decreased in food-importing regions, and agricultural trade expanded after trade liberalization. Hence, an increase in imports caused a reduction in domestic production in most food-importing countries.
Based on these results, the following can be observed in Figure 4. As shown by Cases 4/1 in the case of non-SYC, trade liberalization increased benefits of food-importing regions. For example, in Japan, as one major importers, agricultural price was decreased by 10% (1–0.90) after trade liberalization,
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and the range of price fluctuations was narrowed by 97% (1–0.03). As a decrease in standard deviation indicates stabilization of price volatility, the above influences are positive effects for Japan’s consumers.
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standard deviation indicates stabilization of price volatility, the above influences are positive effects for Japan’s consumers.
Meanwhile, in food-exporting regions, such as the United States and Brazil, agricultural price rose after trade liberalization, and the fluctuation range of the price increased. As the export volume increased due to trade liberalization, these agricultural exporters were more affected by the high price traded in importing countries. In China and the whole world (WLD), the prices of agricultural products after trade liberalization were almost the same as those before liberalization, and the price fluctuation range was slightly increased. This is because the effects of liberalization in export and import regions were offset in WLD, and those of liberalization in export and import agricultural products were offset in China that exports maize but imports wheat and soybean. In France, the average price change was slight, similar to China, but the standard deviation decreased after liberalization. Even in France, a similar offset between import and export products was present, but its degree was different from China’s.
Figure 4. Net effects of trade liberalization in each situation (non-SYC, SYC in T2, and SYC in T3). Cases X1/X2 means ratio of Case X1 against Case X2. Net effects of trade liberalization comparing by regions and two crop models are shown in the Supplementary Materials (Figures S12 and S13).
The comparison between Cases 4/1, 5/2, and 6/3 shows that the decrease degree in average price in Japan became lower, indicating that the ratio of two cases changed from 0.9 (10% decrease) to 0.91 or 0.92 (8% decrease). The ratio of two cases in the standard deviation of price fluctuations also changed from 0.03 to 0.06 or 0.38, and the decrease degree in the fluctuation range by trade liberalization became lower in the SYC case than the non-SYC case. Note that a ratio close to 1 means that little change occurred in the fluctuation range of the latter case and a small ratio indicates that
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Figure 4. Net effects of trade liberalization in each situation (non-SYC, SYC in T2, and SYC in T3). Cases X1/X2 means ratio of Case X1 against Case X2. Net effects of trade liberalization comparing by regions and two crop models are shown in the Supplementary Materials (Figures S12 and S13).
Meanwhile, in food-exporting regions, such as the United States and Brazil, agricultural price rose after trade liberalization, and the fluctuation range of the price increased. As the export volume increased due to trade liberalization, these agricultural exporters were more affected by the high price traded in importing countries. In China and the whole world (WLD), the prices of agricultural products after trade liberalization were almost the same as those before liberalization, and the price fluctuation range was slightly increased. This is because the effects of liberalization in export and import regions were offset in WLD, and those of liberalization in export and import agricultural products were offset in China that exports maize but imports wheat and soybean. In France, the average price change was slight, similar to China, but the standard deviation decreased after liberalization. Even in France, a similar offset between import and export products was present, but its degree was different from China’s.
The comparison between Cases 4/1, 5/2, and 6/3 shows that the decrease degree in average price in Japan became lower, indicating that the ratio of two cases changed from 0.9 (10% decrease) to 0.91 or 0.92 (8% decrease). The ratio of two cases in the standard deviation of price fluctuations also changed from 0.03 to 0.06 or 0.38, and the decrease degree in the fluctuation range by trade liberalization became lower in the SYC case than the non-SYC case. Note that a ratio close to 1 means that little change
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occurred in the fluctuation range of the latter case and a small ratio indicates that the fluctuation range was more significantly narrowed in the latter case. Thus, the positive effect of Japan’s trade liberalization in the SYC case became smaller than in the non-SYC case.
Furthermore, in terms of export regions, such as the United States and Brazil, the ratios of two cases in average price and highest price were larger in the SYC case than those in the non-SYC case. Specifically, in Brazil, the ratio even in the standard deviation also became larger in the SYC case than in the non-SYC, showing an expansion in the annual fluctuation range. This means that the negative effects of trade liberalization were exacerbated due to SYC. In the United States, however, the annual fluctuation range decreased slightly by SYC, indicating a mitigation of the negative effect of liberalization. Similarly, in WLD and China, the effects of liberalization shown by average price and highest price were exacerbated, but the liberalization effects shown by the annual fluctuation range were improved by SYC.
Overall, for domestic consumers, trade liberalization has a positive impact on food-importing regions, making price levels lower and price fluctuation more stable. Conversely, for food-exporting regions, trade liberalization causes a negative impact due to increasing domestic prices. Meanwhile, SYC reduces the positive effects in importing regions and exacerbate the negative effects of rising prices in exporting regions. Although liberalization affects oppositely in import and export regions, SYC has a negative impact on the effects of liberalization in both import and export regions.
3.5. Influences of SYC on Social Welfare Levels
To show the macroeconomic influence of SYC, we examined social welfare level measured by equivalent variation (EV), in accordance with Hosoe [21]. EV in each case is the change in the monetary value of utility level from the starting year level, corresponding to the calibration year of 2011. As the average of disturbances in Cases 1 and 4 was set to 1, which was the same as the calibration year, the average EV for Cases 1 and 4 became approximately 0 and can be regarded as unchanged from 2011, although variations in EV do exist throughout the years.
Figure 5 shows the average, minimum, and standard deviation of EV. Since EVs in non-SYC of Case 1 were almost 0 and show no change in the social welfare level from the present level, EVs of Cases 3, 4, 5, and 6 were subtracted from or divided by the EV of Case 1 to measure the changes from non-SYC without trade liberalization.
Case 3 minus 1 shows that social welfare levels declined in most countries due to SYC in T3 period. In the worst case shown by Figure 4b, social welfare levels declined by US$62 billion in the United States, US$37 billion in China, and more than US$160 billion in WLD. Conversely, Case 4 minus 1 shows that trade liberalization under non-SYC increased the social welfare level in many regions. In particular, there were remarkable increases in Japan, where agricultural tariff rates were high, and in the United States and Brazil, which export foods. In food-exporting countries, although domestic agricultural price increased (Figure 3) and consumers’ surplus decreased, household income could increase due to an expansion of export, and then, EV increased due to income effects.
Considering both climate change and trade liberalization (Case 5 or Case 6 minus Case 1), the average EV increased in the United States, Brazil, and Japan, similar to the difference between Cases 4 and 1. In WLD and food-importing regions such as Japan, agricultural price was decreased by trade liberalization, and liberalization could overwhelm the negative effects of SYC under climate change. However, these positive effects of trade liberalization were decreased by SYC in T3 by 12% (US$16.5 billion/US$18.8 billion) in Japan. Furthermore, at minimum EV (Figure 5b), the EVs of the United States, China, and WLD in Case 6 were worse than those in Case 1 (and also in the initial level of the simulation) because of an increase in the fluctuation ranges of EV under SYC.
The EV’s standard deviation indicates that the variations in Cases 3 and 6 were much larger than in Case 1, and those in Cases 4 and 5 were slightly larger than in Case 1. This implies that both extreme climate change and trade liberalization widened the gap between a good and a bad year of social welfare level in all regions. These results differ slightly from the results of previous
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study [21], which demonstrated a reduction in deviations in Japan’s social welfare level by trade liberalization. Since trade liberalization for food-importing region could pool the risk origins of crop-yield change occurring in different countries, the volatility of agricultural price was reduced by agricultural trade liberalization, showing the same tendency as the previous study. However, such effects were weakened in EV due to different trade liberalization schemes. Liberalization among all regions was assumed in this study and marked weaker diversification effects of importing regions than the unilateral liberalization scheme set in the previous study. This happens due to competition with other regions for imports under fully liberalization scheme. Furthermore, such effects of trade liberalization in the EV’s fluctuation range were reduced by the decrease in price effects caused by SYC under climate change. Such influences have not been evaluated by previous studies.
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liberalization, showing the same tendency as the previous study. However, such effects were weakened in EV due to different trade liberalization schemes. Liberalization among all regions was assumed in this study and marked weaker diversification effects of importing regions than the unilateral liberalization scheme set in the previous study. This happens due to competition with other regions for imports under fully liberalization scheme. Furthermore, such effects of trade liberalization in the EV’s fluctuation range were reduced by the decrease in price effects caused by SYC under climate change. Such influences have not been evaluated by previous studies.
Figure 5. Changes in social welfare levels measured by equivalent variation (EV) due to SYC under future climate change and trade liberalization. Similar comparison on EV by regions and two crop models are shown in the Supplementary Materials (Figures S14 and S15).
4. Discussion and Policy Implications
Our simulation results show that SYCs among regions and crops were significant and robust and expanded the volatility of agricultural price and EV under future climate change. Hence, SYC becomes a cause of systemic risk to the global economy. Considering this risk under climate change, several policy implications can be noted.
When SYC becomes stronger due to future climate change, many countries will suffer from fluctuations in agricultural prices. Even food-exporting countries such as the United States will suffer
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Figure 5. Changes in social welfare levels measured by equivalent variation (EV) due to SYC under future climate change and trade liberalization. Similar comparison on EV by regions and two crop models are shown in the Supplementary Materials (Figures S14 and S15).
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4. Discussion and Policy Implications
Our simulation results show that SYCs among regions and crops were significant and robust and expanded the volatility of agricultural price and EV under future climate change. Hence, SYC becomes a cause of systemic risk to the global economy. Considering this risk under climate change, several policy implications can be noted.
When SYC becomes stronger due to future climate change, many countries will suffer from fluctuations in agricultural prices. Even food-exporting countries such as the United States will suffer from rising agricultural prices beyond the chronological trend and experience a decrease in the social welfare level in the event of an extreme year. An increase in the US agricultural prices would be led by an increase in agricultural exports, which are motivated by an increase in world agricultural prices during simultaneous global crop failure under future climate change. Hence, increasing global food stocks and developing high-temperature-tolerance varieties of food, noted in existing research, are evidently important, but realistically uncertain to achieve. In addition to these efforts, to reduce systemic risk, it is useful to secure a variety of agricultural production areas, both domestically and internationally. Countries such as Japan, where the food self-sufficiency rate is low, could benefit by maintaining a certain level of domestic food production capacity as well as keeping tight relations with many food-exporting countries.
Trade liberalization, as economic theory suggests, will generate profits and mitigate the negative effects of future climate change by increasing consumer surplus through agricultural price reductions and by increasing the income of exporting countries through trade. However, as measured in this study, the effects of trade liberalization could be reduced by SYC. Therefore, policy makers should consider that effects of trade liberalization would be overestimated if SYC is ignored.
Another policy that deserves consideration is enhancing the insurance system to compensate for global crop failures. When insurance plans are designed with consideration of systemic risk under climate change, premiums rise and become new costs for the global economy. For the insurance sector, a holistic framework to assess and mitigate systemic risk was proposed [35]. Similar considerations are required for systemic risk from climate change in the global food market. If the insurance system operates without considering systemic risk and its costs, the system itself is likely to fail when the risk is realized. Thus, private firms and policymakers must understand the risk and accordingly prepare in advance to manage synchronicity in the food system through a better understanding of SYC.
When policies consider systemic risk, the accuracy of climate, crop, and economic models that can predict the degree of risk is key. From an academic viewpoint, by modeling local extreme meteorological phenomena and local crop growth conditions, the causes of the SYC can be elucidated, which would help measures to avoid systemic risk. Increasing the accuracy of these models requires enormous costs. To improve the accuracy of crop models, e.g., we must develop crop-yield statistics with more detailed and localized units than statistics at the national level. Such statistics and models can be classified as the world’s public goods, improving the social welfare level throughout the world. Therefore, it is important to promote research on model building in the field of crop yield and price prediction through international cooperation.
5. Summary and Conclusions
This study quantified the influence of systemic risk caused by SYC in the global food market under future climate change; it also evaluated the effects of trade liberalization when systemic risk exists, using a CGE model based on harvest predictions from crop models and global climate models. Simulation results demonstrate the following points.
First, the SYC structure was statistically robust among countries and four crops, and will be enhanced by climate change. Such global SYC is probably created by two common influences in regions that produce the same crop. The first is the rising or falling trend of crop yields due to the increase in the global temperature and CO2 concentration, and the second is the widening of yield fluctuations as climatic conditions approach biological thresholds.
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Second, where there was SYC under future climate change, agricultural price volatility increased more than would be expected in the random disturbance (non-SYC) case. In the United States, e.g., the highest price and the fluctuation range of agricultural price would be, respectively, 2.5 times higher and 10 times larger than in the non-SYC case. Hence, social welfare levels in most regions of the world are reduced by SYC.
Third, trade liberalization benefited both food-importing and exporting regions, because food-exporting regions increase domestic income due to an expansion of trade, whereas food-importing regions increase consumers’ surplus due to a decrease in food price. However, such benefits of trade liberalization were degraded by SYC, causing unstable price fluctuations. For example, Japan, as one of the major importers, could decrease agricultural price by 10% after trade liberalization without SYC. However, these effects of liberalization would decrease to 8% by SYC in 2050–2099. Therefore, the effects of trade liberalization would be overestimated, if SYC is ignored.
These results demonstrate that SYC under climate change becomes a systemic risk for the global economy. Typically, influences of SYC are too small to be recognized in the market, but when significant change occurs, it leads to serious social welfare loss worldwide. Considering this risk, it is both prudent and important to review adaptation measures for climate change based on the quantitative results from economic and crop models as applied here.
There were limitations to this research. First, the GTAP data used were a 2011 version; an analysis with new data would be useful. Second, the analysis did not consider changes in capital stock or labor supply. When populations will increase in the future, the surge in agricultural prices should be further exacerbated due to global crop failure. An analysis considering population changes and dynamic analysis, where investment endogenously moves, would also be of interest. Finally, improving the accuracy of economic and crop models is important in and for the academic field.
Supplementary Materials: The following are available online at http://www.mdpi.com/2071-1050/12/24/10680/s1, Table S1: Frequency in magnitude of correlation coefficient (r) between regions by crop models. Figure S1: The average effective farmland productivity (EFP) of rice by regions and crop models. Figure S2: The average EFP of wheat by regions and crop models. Figure S3: The average EFP of other cereal grains including maize (GRO) by regions and crop models. Figure S4: The average EFP of oil seeds including soybean (OSD) by regions and crop models. Figure S5: The standard deviation of rice EFP by regions and crop models. Figure S6: The standard deviation of wheat EFP by regions and crop models. Figure S7: The standard deviation of GRO’s EFP including maize by regions and crop models. Figure S8: The standard deviation of OSD’s EFP including soybean by regions and crop models. Figure S9: Average agricultural price (P_agr) without trade liberalization comparing by regions and crop models. Figure S10: The standard deviation of agricultural price (P_agr) without trade liberalization comparing by regions and crop models. Figure S11: Average food net exports (exports–imports) by region (average of two crop models). Figure S12: Net effects of trade liberalization in average agricultural price (P_agr) comparing by regions and crop models. Figure S13: Net effects of trade liberalization in the standard deviation of agricultural price (P_agr) comparing by regions and crop models. Figure S14: Changes in average equivalent variation (EV) with and without synchronized yield change (SYC) or trade liberalization comparing by regions and two crop models. Figure S15: Changes in the standard deviation of EV with and without SYC or trade liberalization comparing by regions and two crop models.
Author Contributions: Conceptualization, Y.K.; methodology, Y.K. and G.S.; validation, Y.K.; formal analysis, Y.K., G.S. and T.I.; investigation, Y.K.; data curation, G.S. and T.I.; writing—original draft preparation, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the Grant-in-Aid for Scientific Research 16H04991, 16KT0036, and 20K06269 (Ministry of Education, Science, Sports and Culture).
Acknowledgments: The climate data for analysis were supplied by Motoki Nishimori (NARO), and English language editing was performed by Editage (www.editage.jp). The authors greatly appreciate their support.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
To eliminate the influences of technological progress, such as fertilizer effects and new variety creation, detrended yields (ỸAi,r,t) are calculated from the actual yield (YAi,r,t) by ỸAi,r,t = YAi,r,t/(ai,r + bi,r · t), where i, r, and t represent the four crop categories, countries, and year, respectively. Here, “a”
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and “b” are the intercept and slope, respectively, estimated from the regression between actual crop yields and the year trend, t.
Appendix B
EFP is assumed to change the farmland-input in the cost function related to the production function. The unit cost for input factors derived from cost minimization behavior of producers in Lanz
and Rutherford [31] is modified as c f j,r =
∑ f θ f · (p
p f f , j,r
/γ f , j,r) (1−σ)
1
(1−σ)
, where c f j,r is the unit cost of
factor, f, in sector j and region r; the suffix f shows input factors, i.e., labor (lab), capital (cap), land (lnd), and other resources (res); θ f is the cost share of each input factor calibrated from the base year data;
and σ represents the substitution elasticity between input factors. Here, p p f f , j,r
is the factor price with
taxes, and γ f , j,r is the input factor productivity in year t, as γ f , j,r = {
1, f ∈ lab, cap, res EFP(j, r, t), f ∈ lnd
.
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- Introduction
- Materials and Methods
- Crop Model and Data
- CGE Model for Estimation of Agricultural Price Volatility
- Simulation Method
- Results
- Robustness of the SYC Structure
- Initial Shocks in EFP Given to the CGE Model
- Influence of SYC on Agricultural Price
- Effects of Agricultural Trade Liberalization on Agricultural Price Volatility
- Influences of SYC on Social Welfare Levels
- Discussion and Policy Implications
- Summary and Conclusions
- References