Economics/Policy and planning analysis Homework

The Economic Impacts of Climate Change

Richard S. J. Tol*

Introduction

For such a fractious discipline, there has been remarkable agreement among economists

concerning the first-best climate policy. Ever since the writings of Nordhaus (1977),

d’Arge (1979), and Schelling (1992), it has been widely accepted that climate change is, on

balance, a negative externality and that greenhouse gas (GHG) emissions should be priced,

preferably taxed. Although there continues to be a vigorous debate about climate targets in

the long-term (Stern et al. 2006; Nordhaus 2013), most economists agree that a sensible

climate policy starts modestly and then accelerates (Wigley, Richels, and Edmonds 1996’

Goulder and Mathai 2000). Despite this general agreement on the need to reduce GHG

emissions, the debate among economists about climate change has been unusually bitter,

perhaps as a reflection of the wider polarization of climate research and climate policy. In

particular, estimates of the marginal impact of climate change vary so widely that the initial

carbon price is more a matter of politics than economics.

The purpose of this article is to examine the economic impacts of climate change. More

specifically, I review estimates of the total economic impact of climate change and the dis-

tribution of those impacts around the world, discuss the interactions between economic

development and climate change, and review and analyze estimates of the social cost of

carbon (i.e., the Pigou tax). For each of these topics I discuss the state of the art with an

emphasis on key recent developments. I present a summary of my findings and a proposed

research agenda in the final section. I find that the total economic impacts of climate change

are negative, but modest on average, and that the severe impacts on less developed countries

are caused primarily by poverty. The impact of climate and climate change on economic

growth is poorly understood but could imply that current estimates are too optimistic.

Estimates of the social cost of carbon are very uncertain but are typically above observed

carbon prices.

*Department of Economics, University of Sussex, Falmer BN1 9SL, UK; Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands; Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands; Tinbergen Institute, Amsterdam, The Netherlands; and CESifo, Munich, Germany; Tel: +44-1273-877282; Email: [email protected].

David Anthoff, Doug Arent, Mike Mastandrea, and Brandon Shollenberger have been a great help in the construction of Table 1. Three anonymous referees and Suzanne Leonard provided excellent comments. All errors and opinions are mine.

Review of Environmental Economics and Policy, volume 12, issue 1, Winter 2018, pp. 4–25 doi: 10.1093/reep/rex027 Advance Access Published on January 12, 2018 VC The Author(s) 2018. Published by Oxford University Press on behalf of the Association of Environmental and Resource

Economists. All rights reserved. For Permissions, please email: [email protected]

4

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

The Total Economic Impact of Climate Change

The impacts of climate change are many and diverse. Determining whether these impacts are

beneficial or detrimental, small or large, depends on the sector, location, and time being

considered. Unfortunately, a reading of the literature on the impacts of climate change (Field

and Canziani 2014) is likely to leave a lay reader confused. It is very difficult to make sense of

the many and different effects: crops hit by worsening drought, crops growing faster because

of carbon dioxide fertilization, heat stress increasing, cold stress decreasing, sea levels rising,

increasing energy demand for cooling, decreasing energy demand for heating, infectious

disease spreading, species going extinct. Thus we need aggregate indicators to assess whether

climate change is, on balance, a good thing or a bad thing and whether the climate problem is

small or large relative to the many other problems that society faces. I focus in this and the

next section on two aggregate indicators (Smith et al. 2001): the impact of climate change on

total economic welfare and the distribution of those welfare impacts.

Estimates of Impacts on Total Economic Welfare

There are currently 27 published estimates of the total economic impact of climate change

(measured in terms of welfare-equivalent income loss) contained in 22 studies (see table 1 and

figure 1). 1

To put these estimates in context, they indicate that a global mean temperature

increase of 2.5�C would make the average person feel as if she had lost 1.3 percent of her

income (1.3 percent is the average of the 11 impact estimates for warming of 2.5�C).

Pindyck (2013) argues that these estimates of the economic impact of climate change

have no foundation in economic theory. No estimate is perfect, but the existing estimates

use well-established and well-accepted methods. 2

Moreover, although the estimates in

table 1 and figure 1 are based on different methods, the results are consistent with each

other. We do not know the accuracy of these estimates, but this is true for any prediction

of the future. In a later paper, Pindyck (2017) argues for transparency and simplicity,

extrapolating into the far future, and deep tails without any detail on the processes in-

volved. Heal (2017) writes that current models are “not accurate enough to provide

quantitative insights” and, like Pindyck, calls for a more intuitive approach to climate

policy advice.

Clearly, 27 estimates are a thin basis for drawing definitive conclusions about the total

welfare impacts of climate change. Moreover, the 11 estimates for warming of 2.5�C indicate

that researchers disagree on the sign of the net impact: 3 estimates are positive and 8 are

1 A number of papers estimate the impact of weather on a range of economic indicators (Deschênes and Greenstone 2007; Barreca 2012; Hsiang and Meng 2015; Hsiang et al. 2017). The key advantage of consid- ering weather impacts is that weather is, from an economic perspective, random. The economic impact of weather is therefore properly identified. Although some of these papers may suggest otherwise, the impact of a weather shock is not the same as the impact of climate change. Climate is what you expect, while weather is what you get. Adaptation to weather shocks is therefore limited to immediate responses: put up an umbrella when it rains, close the flood doors when it pours. In contrast, adaptation to climate change extends to changes in the capital stock: buy an umbrella, invest in flood doors. In other words, weather studies estimate the short-run elasticity, but what we are interested in is the long-run elasticity. Thus, extrapolating the impact of weather shocks to the impact of climate change is unlikely to lead to credible results.

2 See appendix A in the online supplementary materials for a discussion of the methods used to estimate the total welfare impacts of climate change presented in table 1.

The Economic Impacts of Climate Change 5

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

negative. Thus it is unclear whether climate change will lead to a net welfare gain or loss. At the

same time, however, despite the variety of methods used to estimate welfare impacts,

researchers agree on the order of magnitude, with the welfare change caused by climate

change being equivalent to the welfare change caused by an income change of a few percent.

That is, these estimates suggest that a century of climate change is about as good/bad for

welfare as a year of economic growth. 3

Table 1 Estimates of the welfare impact of climate change

Study Warming (�C) Impact (% GDP)

Best SD Low High

d’Arge 1979 �1.0 �0.6 Nordhaus 1982 2.5 �3.0 �12.0 5.0 Nordhaus 1991 3.0 �1.0 Nordhaus 1994b 3.0 �1.3 Nordhaus 1994a 3.0 �3.6 �21.0 0.0

6.0 �6.7 Fankhauser 1995 2.5 �1.4 Berz undated 2.5 �1.5 Tol 1995 2.5 �1.9 Nordhaus and Yang 1996 2.5 �1.4 Plambeck and Hope 1996 2.5 �2.9 �13.1 �0.5 Mendelsohn et al. 2000 2.5 0.0

2.5 0.1

Nordhaus and Boyer 2000 2.5 �1.5 Tol 2002 1.0 2.3 1.0

Maddison 2003 2.5 0.0

Rehdanz and Maddison 2005 0.6 �0.2 1.0 �0.3

Hope 2006 2.5 �1.0 �3.0 0.0 Nordhaus 2006 3.0 �0.9 0.1

3.0 �1.1 0.1 Nordhaus 2008 3.0 �2.5 Maddison and Rehdanz 2011 3.2 �5.1 Bosello et al. 2012 1.9 �0.5 Roson and van der Mensbrugghe 2012 2.9 �2.1

5.4 �6.1 Nordhaus 2013 2.9 �2.0

Notes: Impact is measured as welfare-equivalent income loss and expressed as a percentage of income. Climate change is char-

acterized by the increase in the global annual mean surface air temperature. Estimates are best guesses (Best). Where available,

either the standard deviation (SD) of the estimate or an indication of the lower (low) and upper (high) bound of its confidence

interval is given. There are three differences between this table and the IPCC table (Arent et al. 2014). First, this table includes the

estimates by d’Arge, (1979). Second, to be consistent with the other estimates in the table, the Mendelsohn estimates are shown

against the area average temperature change rather than the population average. Third, to be consistent with the other estimates in

the table, the Maddison and Rehdanz estimate is shown in market exchange rate dollars rather than in purchasing power parity

dollars.

Source: Data are available at http://users.sussex.ac.uk/�rt220/totalimpactreep.xlsx.

3 This suggests that there are bigger problems facing humankind than climate change. For example, the people of Greece lost a third of their income in five years’ time, arguably due to poor monetary policy. The people of Syria lost even more in a shorter period. Climate change may not even be our biggest environmental problem, as many people are killed by indoor and urban air pollution (WHO 2014).

6 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Considered together, the 27 estimates suggest that the welfare impacts of initial

warming are positive on net, while further warming will lead to net damages

(d’Arge, Schulze, and Brookshire 1982). This is illustrated by the solid line in figure 1.

Alternative specifications of the impact function are possible, but the piecewise linear

model of figure 1 is by far the best fit. 4

Some popular impact functions do not fit the data

at all. 5

The initially positive impacts do not imply that GHG emissions should be subsidized. As

shown in figure 1, the total impacts turn negative just below 1.7�C warming above prein-

dustrial levels. More importantly, the incremental impacts turn negative before that, around

1.1�C global warming. Because of the slow workings of the climate system and the long-lived

capital in the energy sector, it is likely that a warming of 2�C cannot be avoided; a warming of

1�C can certainly not be avoided. Thus the initial net benefits of climate change are sunk

benefits—that is, we will reap these benefits no matter what we do to our emissions. This

means that GHG emissions should be taxed, not subsidized.

-25.0

-22.5

-20.0

-17.5

-15.0

-12.5

-10.0

-7.5

-5.0

-2.5

0.0

2.5

5.0

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5

W el

fa re

-e qu

iv al

en t i

nc om

e ch

an ge

(i n

pe rc

en t)

Global warming (in degrees centrigrade)

Figure 1 The global total annual impact of climate change

Notes: Impact is expressed in welfare-equivalent income change as a function of the increase in the global

annual mean surface air temperature since preindustrial times. The dots represent the estimates reported in

table 1, the solid line indicates the best-fit piecewise linear function, and the dotted lines indicate the

95 percent confidence interval.

Source: Data are available at http://users.sussex.ac.uk/�rt220/totalimpactreep.xlsx.

4 Table B1 in the online supplementary materials presents alternative specifications and how they fare when fitted to the data of table 1, an exercise that should be done more often in climate economics. Note that the parabolic function of Tol (2009) provides the second-best fit.

5 For example, although Weitzman (2011) argues that the climate change impact function is very nonlinear, with a sharp turn towards large damages at higher temperatures, this is not supported by the estimates shown in table 1 and figure 1.

The Economic Impacts of Climate Change 7

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Impact of Uncertainty

The uncertainty about the estimates of the impact of climate change on total economic

welfare is rather large. The dotted lines in figure 1, which are derived from the few standard

errors reported in table 1, depict the 95 percent confidence interval. However, given that

experts tend to be overconfident (Lichtenstein and Fischhoff 1977) and the 27 estimates were

derived by a group of researchers who know each other well, this is probably an

underestimate of the true uncertainty. If we take the confidence interval at face value, the

impact of climate change does not significantly deviate from zero until 3.5 �C warming.

It is important to note, however, that the uncertainty indicated in figure 1 is right-skewed.

That is, negative surprises are more likely than positive surprises of similar magnitude. This is

true for both GHG emissions and the climate itself. For example, it is easier to imagine a

world that burns a lot of coal than a world that rapidly switches to wind and solar power

(Nakicenovic and Swart 2001; van Vuuren et al. 2011; Clarke et al. 2014). Feedbacks that

accelerate climate change are likely to be stronger than feedbacks that dampen warming (Roe

and Baker 2007; Knutti and Hegerl 2008; Lewis 2013). Furthermore, the impacts of climate

change are typically found to be more than linear. That is, if climate change doubles, its

impacts more than double (see figure 1). Many researchers have painted dismal scenarios of

climate change (Myers 1993; Chalko 2001; Stern et al. 2006; Potsdam Institute for Climate

Impact Research and Climate Analytics 2012; Oppenheimer et al. 2014). However, no one has

credibly suggested that climate change will make us all blissfully happy. In light of these

uncertainties and asymmetries, the earlier conclusion that a century of climate change is

about as good/bad for welfare as a year of economic growth needs to be rephrased: A century

of climate change is likely to be no worse than losing a decade of economic growth.

Distribution of Impacts

Thirteen of the twenty-two studies listed in table 1 include estimates of the regional impacts of

climate change and, in the case of the Maddison (2003), Rehdanz and Maddison (2005), and

Maddison and Rehdanz (2011), national impact estimates. These estimates indicate that

poorer and hotter countries are notably more vulnerable to climate change than richer ones. 6

As shown in figure 2, for global warming of 2.5�C, the expected impacts for the majority of

countries are more negative than in figure 1, where the global total impact is�1.4 percent of gross domestic product. This is because the world economy is concentrated in a few rich countries.

7

Figure 2 also shows that, by and large, the negative impacts of climate change will be borne by

developing economies. Contrary to what is assumed in some studies (Hoel and Sterner 2007;

Sterner and Persson 2008), the relative impacts of climate change decline as per capita income rises.

Developing countries are more vulnerable to the impacts of climate change for three

reasons. First, poorer countries are more exposed to the weather because of the important

role of agriculture and water resources in the economy. In contrast, richer countries have a

larger share of their economic activities in manufacturing and services, which are typically

shielded (to some extent) from the vagaries of weather and hence climate change.

6 See appendix C in the online supplementary materials for a description of the methods used.

7 Note also that the world average in figure 1 counts dollars, rather than countries, let alone people.

8 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

-20

-15

-10

-5

0

5

10

100 1,000 10,000 100,000

W el

fa re

-e qu

iv al

en t i

nc om

e ch

an ge

(i n

pe rc

en t)

Income (in 2005 dollar per person per year)

-20

-15

-10

-5

0

5

10

-10 -5 0 5 10 15 20 25 30 35

W el

fa re

-e qu

iv al

en t i

nc om

e ch

an ge

(i n

pe rc

en t)

Temperature (in degrees centrigrade)

Figure 2 The national total annual impact of climate change

Notes: Impact is expressed in welfare-equivalent income change for a 2.5 �C global warming (relative to preindustrial times) as a function of per capita income (top panel) and temperature (bottom panel). In the top

panel, countries are ranked from low to high per capita income (in 2005); in the bottom panel, the ranking is

by average annual temperature.

Source: Data are available at http://users.sussex.ac.uk/�rt220/totalimpactreep.xlsx.

The Economic Impacts of Climate Change 9

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Second, poorer countries tend to be in hotter places. This means both that ecosystems are

closer to their biophysical upper limits and that there are no analogues for human behavior

and technology. That is, if the hottest places on the planet become even hotter, there are no

existing examples to learn from; new technologies will have to be developed and behavior will

have to be adjusted by trial and error. In contrast, Great Britain’s future climate may become

like Spain’s current climate, and the people of Britain would likely adopt some of the behav-

iors and technologies of the people of Spain.

Third, poorer countries tend to have a limited adaptive capacity (Adger 2006; Yohe and Tol

2002), which depends on a range of factors, such as the availability of technology and the ability to

pay for those technologies. Poorer countries often lack access to modern technology and insti-

tutions that can help protect against the weather (e.g., air conditioning, malaria medicine, crop

insurance). They may also lack the ability, and sometimes the political will, to mobilize the

resources for large-scale infrastructure—irrigation and coastal protection, for example.

Development and Climate Policy

There are two options for mitigating the excessive impact of climate change on the poor:

reduce climate change and/or reduce poverty. However, the relationship between poverty

and vulnerability to climate change is not simple, as I illustrate with some examples below.

Coastal Protection

Bangladesh and the Netherlands are both densely populated, low-lying countries at risk from

flooding by river and sea. However, Bangladesh is much more vulnerable to climate change. The

Netherlands started its modern, large-scale dike building program in 1850 (Tol and Langen 2000).

Before that, dike building was local and primitive and the country was regularly plagued by

floods. In 1850, the Netherlands was somewhat richer than Bangladesh is now (purchasing power

parity $2400 versus $1400 per person per year), but dike-building technology has improved since

then. The main difference between the Netherlands in 1850 and Bangladesh in 2014 is political. In

1849, the Netherlands had a powerful central government broadly representative of the

population, which prioritized flood protection. In contrast, Bangladesh is one of the most corrupt

and poorly governed countries in the world. 8

Floods primarily affect the poor, who live in the river

and coastal flats where land is cheap (Brouwer et al. 2007). There is no political reason to protect

the poor because votes can be bought 9

and floods are seen as an act of Allah (Alam 1990). This

makes Bangladesh more vulnerable to climate change than the Netherlands.

Agriculture

Climate change could reduce crop yields in Africa by up to 50 percent (Porter et al. 2014).

Yields from subsistence farms are often only one-tenth of what is achieved at model farms that

have the same soil and climate (Mueller et al. 2012). This so-called yield gap is caused by

8 See https://www.transparency.org/news/feature/corruption_perceptions_index_2016.

9 See http://bdnews24.com/economy/2015/04/27/bangladesh-bank-suspends-mobile-banking-to-prevent- vote-buying-in-city-polls.

10 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

factors such as a lack of access to irrigation, high-quality seeds, and pesticides, but the un-

derlying causes include a lack of access to capital and product markets due to poor roads and

insecure land tenure (Dorward et al. 2004; Foley et al. 2011). Modernizing agriculture would

close this yield gap and make African agriculture less vulnerable to climate change

(Mendelsohn and Dinar 1999; Howden et al. 2007).

Malaria

Malaria was endemic in large parts of Europe and North America (Hay et al. 2004). Habitat

reduction, mosquito control, and medicine turned malaria into a tropical disease. Climate

change would spread malaria since the parasite is more vigorous in hot weather and mos-

quitoes thrive in hotter and wetter places (Martens, Jetten, and Focks 1997; van Lieshout et al.

2004). However, malaria is first and foremost a disease of poverty (Tol and Dowlatabadi 2001;

Tol, Ebi, and Yohe 2007). Investments in insecticides, bed nets, and vaccine development

have the potential to ensure a malaria-free world, regardless of climate (Cotter et al. 2013;

Seder et al. 2013).

Implications for Research and Policy

These three examples—coastal protection, agriculture, and malaria—illustrate that develop-

ment and vulnerability to climate change are closely intertwined. Although this was first

discussed by Schelling (1992), many studies have ignored this relationship, assuming that

vulnerability is constant (Burke et al. 2016).

Concentrating GHG emissions reductions in rich countries will not solve the climate

problem, while slowing economic growth in poor countries to reduce climate change may

do more harm than good (Tol 2005a; Anthoff and Tol 2012). One-fifth of official develop-

ment aid is now aimed at climate policy (Michaelowa and Michaelowa 2007; Tol 2014),

although there has been some relabeling of conventional aid as climate aid (Michaelowa

and Michaelowa 2011). Cheap and abundant energy fuelled the industrial revolution (Stern

and Kander 2012), and a lack of (reliable) electricity retards growth in poor countries

(Chontanawat, Hunt, and Pierse 2008; Steinbuks and Foster 2010). Yet some donors no

longer support the use of coal, the cheapest way to generate electricity, or indeed any other

fossil fuel. Energy poverty alleviation and carbon dioxide emission reduction may be con-

tradictory goals (Chakravarty and Tavoni 2013). Climate policy could thus increase the

impacts of climate change in poor countries.

The disproportionate impacts of climate change on the most vulnerable is a good reason to

reduce GHG emissions. However, it is odd to express great concern about the plight of the

poor when it comes to climate but not in other policy domains (Schelling 1992, 2000). Levels

of charitable giving and official development aid suggest a low level of inequity aversion

between countries (Tol 2010). Barriers to international trade (Winters, McCulloch, and

McKay 2004; Hertel et al. 2009; Winters and Martuscelli 2014) and labor migration

(Lipton 1980; Adams and Page 2005) suggest a disregard for the poor in other countries.

The disconnect between climate and other policies and the implied concern for people in

poor countries may be interpreted in one of two ways: concern about the impact of climate

The Economic Impacts of Climate Change 11

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

change on the global poor is exaggerated, or our aid, trade, and migration policies need

reform. 10

Development and Climate Change

In addition to its comparative static impacts (see figure 1), climate change affects the growth

rate of the economy (Fankhauser and Tol 2005; Hallegatte 2005; Eboli, Parrado, and Roson

2010; Bretschger and Valente 2011; Lemoine and Kapnick 2016). Climate change may affect

the size and productivity of the labor force and the capital stock, which would affect invest-

ment and hence future output. Dietz and Stern (2015), Moyer et al. (2014), and Moore and

Diaz (2015) conjecture, without evidence, that climate change would also affect technological

progress, which would have a large effect on economic growth (Solow 1956). 11

If true, the

dynamic impacts of climate change would dominate the static ones.

There is empirical evidence that climate change has an impact on economic growth. Dell,

Jones, and Olken (2009) and Horowitz (2009) find that higher temperatures would reduce

income, particularly in poor countries (Dell, Jones, and Olken 2012). Barrios, Bertinelli, and

Strobl (2010) and Brown et al. (2011) find a large impact of anomalous rainfall on economic

growth in sub-Saharan Africa. Bloom, Canning, and Sevilla (2003) find that hot and wet

conditions and large variability in rainfall reduce long-term growth in poor countries (but

not in hot ones) and increase the probability of being poor. With the exception of Burke,

Hsiang, and Miguel (2015), these studies find relatively small effects.

The impact of climate (rather than climate change) on development is a subject of active

debate and research (Bhattacharyya 2009; Dell, Jones, and Olken 2014). The implications for

climate change are unclear, as the findings are mixed. Some argue that geography is the main

cause of (under)development in the past (Diamond 1999; Olsson and Hibbs 2005), and

presumably in the future. Others emphasize the links between climate and disease (Gallup,

Sachs, and Mellinger 1999) and climate and agriculture (Masters and McMillan 2001). If

differences in human institutions are accounted for, the apparent influence of climate on

development disappears in some studies (Acemoglu, Johnson, and Robinson 2001, 2002;

Easterly and Levine 2003) 12

but not in others (Alsan 2015). Some argue that the reported

impact of climate on development is actually an impact of ultraviolet radiation (Andersen,

Dalgaard, and Selaya 2016).

In sum, the literature on the impact of climate (change) on development has yet to reach

firm conclusions. Climate change could reduce the rate of economic growth and even trap

people in poverty. If this occurs, then the dynamic impacts could be larger than the static ones

reviewed earlier, which means the social cost of carbon would be (much) higher.

10 Alternatively one may argue, as one referee does, that GHG emissions are categorically different from other actions and inactions that cause harm to others and therefore the observed indifference in other policy domains does not apply to climate policy.

11 See Bell and Gersbach (2013) for an analysis of an alternative mechanism through which disease and human capital formation affect economic growth.

12 Climate affects human culture, at least in poor countries, and thus institutions (van de Vliert 2008; van de Vliert and Tol 2014).

12 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

The Social Cost of Carbon

The social cost of carbon is the incremental impact of emitting an additional ton of carbon

dioxide, or the benefit of slightly reducing emissions. When evaluated along an optimal

emissions trajectory, the social cost of carbon is the Pigou tax (Pigou 1920), that is, the

amount GHG emissions should be taxed in order to maximize welfare.

There have been a number of developments concerning the social cost of carbon since my

earlier surveys (Tol 2011, 2013b). The volume of papers and estimates has increased rapidly, 13

partly, I believe, in response to the U.S. government adopting an official social cost of carbon

(IAWGSCC 2010, 2013, 2015). After a discussion of conceptual issues, I review these new

estimates.

New Insights into the Social Cost of Carbon

Some researchers have argued that current estimates of the social cost of carbon are lower

bounds on the true social cost of carbon and that, by implication, climate policy is not

ambitious enough. Three arguments are put forward. First, estimates of the social cost of

carbon are said to underestimate the true risks of climate change (Botzen and van den Bergh

2012; van den Bergh and Botzen 2014, 2015), although both primary estimates (Anthoff, Tol,

and Yohe 2009b; Anthoff and Tol 2013, 2014) and meta-analyses (Tol 2009; Arent et al. 2014)

pay considerable attention to risks. Second, estimates of the social cost of carbon rely on

incomplete impact assessments (Revesz et al. 2014). However, incompleteness only implies

bias if the missing impacts are all negative (Tol 2009; Arent et al. 2014). Third, estimates of the

social cost of carbon are partly determined by ethical parameters such as the rates of pure time

preference, risk aversion, and inequality aversion (Guo et al. 2006; Anthoff, Hepburn, and Tol

2009; Anthoff, Tol, and Yohe 2009a; Anthoff and Tol 2010; Tol 2010, 2013a). Some have

argued in favor of particular parameter values (Stern 2008, 2010, 2013; van den Bergh and

Botzen 2014, 2015), thus putting bounds on the social cost of carbon. However, there is a wide

range of estimates of parameters that describe attitudes towards time (Frederick,

Loewenstein, and O’Donoghue 2002) and risk, and it is not obvious under what conditions

democratically elected governments could or should overrule the preferences of the elector-

ate. In any case, the relationship between these ethical parameters and the social cost of

carbon is not as simple as some might think. For instance, the impact of inequality aversion

is ambiguous, because although poorer countries are more vulnerable to climate change than

richer ones, carbon dioxide fertilization 14

disproportionately benefits poorer countries

(Anthoff, Tol, and Yohe 2009b).

The social welfare function governs trade-offs between risks, between present and future,

and between rich and poor. Models often assume that the social welfare function is identically

curved in the three dimensions of time, risk, and regard for others (Atkinson et al. 2009). As

economic growth is typically assumed to continue, this implies an ambiguous effect on the

social cost of carbon. Some recent papers separate risk and time (Crost and Traeger 2014;

13 For example, there were 7 new studies and 72 new estimates in the first few months of 2015 alone. See appendix D in the online supplementary materials for more details.

14 Carbon dioxide is a key ingredient in photosynthesis. If there is more carbon dioxide in the atmosphere, plants grow faster and become more resistant to drought as less water is lost through evapotranspiration.

The Economic Impacts of Climate Change 13

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Jensen and Traeger 2014; Lemoine and Traeger 2014) but disregard distributional issues

within and between countries.

Some of the controversy concerning the social cost of carbon arises from the complexity of

its computation. Golosov et al. (2014) show that the social cost of carbon can be written as a

function of total economic output, the pure rate of time preference, elasticity of damage with

regard to the atmospheric concentration of carbon dioxide, and the rate of decay of carbon

dioxide in the atmosphere. This result hinges on the following assumptions: (1) that utility is

logarithmic in consumption, (2) that time discounting is exponential, (3) that the carbon

cycle follows a linear difference equation, (4) that climate change impacts are proportional to

total output, (5) that climate change impacts are proportional to the exponent of the atmo-

spheric concentration of carbon dioxide, and (6) that there are no catastrophic risks.

Unfortunately, none of these assumptions is realistic. The first two assumptions are discussed

elsewhere in this article. Maier-Reimer and Hasselmann (1987) show that the removal of

carbon dioxide from the atmosphere cannot be approximated by a linear difference equation.

As argued earlier, poverty implies vulnerability to climate change; that is, impacts are less than

proportional to output. The equilibrium temperature is logarithmic in the atmospheric

concentration, so Golosov et al. (2014) assume that impact is proportional to the exponent

of the exponent of temperature. Figure 1 suggests that the relationship between temperature

and impact is close to linear. A series of papers (Keller, Bolker, and Bradford 2004; van den

Bijgaart et al. 2013; Lemoine and Traeger 2014; van der Ploeg 2014) show that catastrophes

break the smoothness assumed by Golosov et al. (2014), and hence their simple function for

the social cost of carbon. Note that these studies offer little new insight into the optimal

carbon tax in the near term. 15

New Estimates of the Social Cost of Carbon

I next examine the probability density of the social cost of carbon for all published estimates

(see appendix figure 1). The method follows Tol (2013b), 16

with the estimates weighted by

study characteristics as in Tol (2005b). In addition, estimates in excess of $7,600 per ton of

carbon are excluded. 17

Estimates between $1,150 and $7,600 per ton of carbon are discounted

by a linear function that equals 1 for $1,150 per ton of carbon and 0 for $7,600 per ton of

15 These studies show that the optimal carbon price does not follow a Hotelling-like path. Lemoine and Rudik (2017) show the same for the cost-effective carbon price.

16 The probability density function (PDF) is a kernel density. The kernel function is a Fisher–Tippett distri- bution, a fat-tailed, right-skewed PDF defined on the real line. The mode is set equal to the estimate, the bandwidth to the sample standard deviation. Only the models developed by Hope and Tol acknowledge the possibility that the impacts of modest climate change may be positive. The kernel functions for estimates by other authors are therefore knotted at zero.

17 This assumes that the social cost of carbon is a tax that should be paid. Howarth, Gerst, and Borsuk (2014) report an estimate as high as $105,000 per ton of carbon. The social cost of carbon may be interpreted as how much we should be willing to pay to reduce carbon dioxide emissions, or as the tax that we should impose on such emissions. We should expect to pay such a tax over many years, so we cannot pay more than our annual income. In 2010, global average carbon efficiency was around $7,600 per ton of carbon; this is an upper bound for a carbon tax. Howarth’s tax would thus take fourteen times total world income. One could argue that a carbon tax should offset other taxes, but not increase the total tax burden (Tol 2012). In recent years, world average tax revenue was about 15 percent of gross domestic product, so a tax of $1,150 per ton of carbon or larger would increase the total tax take. This would be an alternative upper bound. If, on the other hand, the social cost of carbon is interpreted as a marginal welfare loss, then there is no upper bound.

14 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

carbon. I next discuss the new estimates and their policy implications, deep uncertainty, and

publication bias.

Policy implications

Appendix figure 1 presents the probability density of the social cost of carbon for the entire

sample and for those estimates based on a pure rate of time preference (PRTP) of 0, 1 or 3

percent per year. 18

The higher the discount rate, the lower the concern for the future and the

lower the social cost of carbon: The mode is $220 per ton of carbon for a 0 percent PRTP, $93

per ton of carbon for a 1 percent PRTP, and $28 per ton of carbon for a 3 percent PRTP.

Furthermore, as the uncertainty grows as we look further into the future, a lower discount rate

implies a loss of confidence, with a standard deviation of $669 per ton of carbon for a 0

percent PRTP, $468 per ton of carbon for a 1 percent PRTP, and $35 per ton of carbon for a 3

percent PRTP. The higher mode and standard deviation come together in the mean social

cost of carbon, which is $677 per ton of carbon for a 0 percent PRTP, $360 per ton of carbon

for a 1 percent PRTP, and $44 per ton of carbon for a 3 percent PRTP. To provide some

context, burning a barrel of oil emits 0.43 metric ton of carbon dioxide. A $28 per ton of

carbon tax is thus equivalent to $3 per barrel, while a $677 per ton of carbon tax is equivalent

to $79 per barrel. The former carbon tax is small relative to today’s price of oil, while the latter

tax is on the same order as price changes on the world market over the last few years. In

January 2016, the price of carbon permits in the European Union Emission Trading System

(EU ETS) was $28 per ton of carbon. 19

In November 2015, permits were auctioned by the

California Air Resources Board at $47 per ton of carbon. 20

The U.S. government uses a social

cost of carbon for regulatory benefit–cost analysis that is between $12 and $58 per ton of

carbon (Revesz et al. 2017), although the current administration may seek to lower that (EPA

2017). This suggests that current climate policy can readily be justified by benefit–cost anal-

ysis and may indeed need to be tightened.

Deep uncertainty

Weitzman (2011) argues that the uncertainty about climate change is so large that the ex-

pectation of the social cost of carbon is unbounded. However, the evidence in appendix

figure 1 challenges this assertion. Moreover, Weitzman’s result only holds in partial equilib-

rium (Horowitz and Lange 2014), for zero mitigation (Millner 2013), and for constant rel-

ative risk aversion (Arrow 2009), while alternative decision criteria do not lead to

substantially different policy advice concerning optimal climate policy (Anthoff and Tol

2014).

18 The PDFs in appendix figure 1 jump at $0 per ton of carbon. This is by construction. Figure D1 in the online supplementary materials shows the PDF for all estimates if we do not knot the kernel function at zero. In this case there is a substantial probability mass for carbon subsidies, which is at odds with the underlying literature: with knotting, there is a 9 percent chance of a negative social cost of carbon; without knotting, there is a 26 percent chance. Figure D1 also shows the implications of the decision to discount estimates that would lead to an expansion of the public sectors and to discard estimates in excess of annual income. Because there are such large estimates in the database, the bandwidth in figure D1 is large and the PDF is diffuse.

19 https://www.eex.com/en/market-data/environmental-markets/auction-market/european-emission- allowances-auction#!/2017/01/09.

20 http://calcarbondash.org/.

The Economic Impacts of Climate Change 15

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Publication bias

Havranek et al. (2015) show that low estimates of the social cost of carbon are less likely to be

published than higher estimates. However, the literature on the social cost of carbon does not

appear to suffer from confirmation bias. The received wisdom is regularly challenged. 21

Indeed, in 9 of 24 years, estimates of the social cost of carbon deviate significantly from earlier

ones; 22

the patterns are similar if the sample is split by the PRTP. 23

This indicates that the

differences in estimates of the social cost of carbon are not due to differences in discounting

and thus that a consensus on the social cost of carbon has yet to be reached.

However, the median and the 90 percent confidence interval for estimates of the social cost

of carbon published in a particular year and published in previous years do not show frequent

challenges to the received wisdom (see figure 3). 24

Figure 3 suggests a gradual decline in the

central estimate of the social cost of carbon and a modest tightening of its confidence interval.

Thus figure 3 indicates that neither upward revisions nor downward revisions (because the

trends are statistically insignificant) of the social cost of carbon are supported.

Conclusions and Research Priorities

This review of estimates in the literature indicates that the impact of climate change on the

economy and human welfare is likely to be limited, at least in the twenty-first century. In the

short to medium run, climate change may well bring gains, particularly to those who depend

on rain-fed agriculture (as carbon dioxide fertilization makes plants more drought resistant)

and those who spend substantial money on heating (as warming is faster in winter). However,

in the long run, the negative impacts of climate change are likely to outweigh the positive

ones. These negative impacts will be substantially greater in poorer, hotter, and lower-lying

countries. Because poverty causes vulnerability to climate change, development is a comple-

mentary strategy to GHG emissions reduction; any trade-off between slower economic

growth and lower emissions needs to be carefully considered. At the same time, climate

change may affect the growth rate of the economy and may trap more people in poverty,

although estimates of the size of these effects vary from negligible to substantial. Thus climate

change would appear to be an important issue primarily for those who are concerned about

the distant future, faraway lands, and remote probabilities. Moreover, although recent re-

search has substantially improved our understanding of the dynamics of the Pigou tax, our

21 See table D1 in the online supplementary materials, which lists the number of studies by year of publication, the number of estimates, and the average PRTP. There is a slight downward but insignificant trend (�3.09 6 4.91 dollars per ton of carbon per year) in the estimated social cost of carbon. There is a slight upward but insignificant trend (0.0005 6 0.0179 percent per year) in the PRTP.

22 See table D1 in the online supplementary materials for the results of a t-test for the difference between the average of the estimates published in a year and the average of earlier years.

23 See tables D2–D7 in the online supplementary materials for estimates of the social cost of carbon based on the six most frequently used PRTP values (0, 0.1, 1, 1.5, 2, and 3 percent).

24 Besides the discounting of high estimates that exceed income, the key difference between table D1 in the online supplementary materials and figure 3 is the proper reflection of uncertainty through the probability density estimation: although the standard error of the mean in table D1 is rather low, having estimated the mean with confidence does not imply that the uncertainty is small. The bandwidths underlying appendix figure 1 and figure 3 were chosen to avoid overconfidence, a choice that seems appropriate in light of the great uncertainties and controversies concerning climate change.

16 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

best estimate of the optimal carbon tax in the near term still ranges from a few tens to a few

hundreds of dollars per ton of carbon, leaving ample room for political maneuvering.

These qualitative insights are robust, but the quantitative assessment of the impacts of

climate change is uncertain and incomplete. This uncertainty is partly irreducible. We are,

after all, estimating and valuing the impact of future climate change on future society.

Further research is needed concerning several unresolved issues. First, natural scientists

and economists tend to disagree about the seriousness of climate change, and have done so for

many years (Nordhaus 1994a). However, the reasons for this discrepancy, and indeed

whether it is real, are not well understood and thus require further study to determine

who is closer to the truth.

Second, the impact of climate change on numerous important issues—water resources,

transport, migration, violent conflict, energy supply, space cooling, labor productivity, and

tourism and recreation—has not received sufficient attention; there is either very little solid

evidence, no conclusive evidence, or no quantification of welfare impacts. This means that the

estimates of the impact of climate change are incomplete and we cannot know whether the

bias is upwards or downwards until more research is conducted. However, this also increases

the uncertainty about the impact of climate change, which strengthens the case for GHG

emissions reductions.

It is unlikely that future research will overturn the fundamental finding that it is the poor

who will suffer most from climate change and reducing poverty should be a key priority for

-500

0

500

1000

1500

2000

2500

3000

3500

1996 1999 2004 2005 2006 2009 2010 2011 2012 2013 2014 2015 2016

do lla

r p er

to nn

e of

c ar

bo n

year

Figure 3 The median and 90 percent confidence interval of estimates of the social cost of carbon published

in a particular year (dots and bars) and in previous years (lines)

Notes: The median here indicates the kernel median.

Source: Data are available at http://users.sussex.ac.uk/�rt220/results-REEP.xlsx. Code is available at http:// users.sussex.ac.uk/�rt220/MetaSCC.zip.

The Economic Impacts of Climate Change 17

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

policies aimed at alleviating the impact of climate change. That being said, the quantification

remains problematic and should be researched in greater detail.

The impact of climate and climate change on economic growth and development is not

well understood, and different studies have reached opposite conclusions. New data, pref-

erably longer time series, and the application of the latest econometric techniques should shed

new, perhaps decisive, light on these issues.

Climate policy advice is channeled through estimates of the social cost of carbon, which is

very sensitive to the discount rate. However, the social cost of carbon aggregates not only over

time, but also between impacts, across species, within and between societies, and across

alternative futures. Although recent studies have made some progress in illustrating the

importance of other parameters in the welfare function for the social cost of carbon, a com-

prehensive analysis is still some way off. There is also a disconnect between the assumptions

made in integrated assessment models and the insights from behavioral and experimental

economics. Future meta-analyses may be able to determine whether there is a systematic

relationship between model structure and the social cost of carbon.

Climate policy is one of the defining issues of our time. The research agenda I have outlined

here is rich enough to keep economists occupied for years to come. Moreover, this agenda

touches on fundamental issues in economics, such as trade-offs between risky prospects for

different people and why some are rich and others poor. Together, this proposed agenda

makes for research that is both intellectually fascinating and policy relevant.

Appendix Figure 1 The probability density function of the social cost of carbon

Notes: This is shown in 2010 dollars per metric tonne of carbon for emissions in 2015 for all estimates, and for

estimates based on a 0%, 1% or 3% pure rate of time preference.

Source: Data are at http://users.sussex.ac.uk/�rt220/results-REEP.xlsx. Code is at http://users.sussex.ac.uk/ �rt220/MetaSCC.zip.

18 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

References

Acemoglu, D., S. Johnson, and J. A. Robinson.

2001. The colonial origins of comparative devel-

opment: an empirical investigation. American

Economic Review 91(4):1369–401.

————. 2002. Reversal of fortune: geography and

institutions in the making of the modern world

income distribution. Quarterly Journal of

Economics 117(4):1231–94.

Adams, R. H., Jr., and J. Page. 2005. Do interna-

tional migration and remittances reduce poverty in

developing countries? World Development

33(10):1645–69.

Adger, W. N. 2006. Vulnerability. Global

Environmental Change 16(3):268–81.

Alam, S. M. N. 1990. Perceptions of flood

among Bangladeshi villagers. Disasters

14(4):354–57.

Alsan, M. 2015. The effect of the tsetse fly on

African development. American Economic Review

105(1):382–410.

Andersen, T. B., C.-J. Dalgaard, and P. Selaya.

2016. Climate and the emergence of global income

differences. Review of Economic Studies

83(4):1334–63.

Anthoff, D., C. J. Hepburn, and R. S. J. Tol. 2009.

Equity weighting and the marginal damage costs

of climate change. Ecological Economics

68(3):836–49.

Anthoff, D., and R. S. J. Tol. 2010. On international

equity weights and national decision making on

climate change. Journal of Environmental

Economics and Management 60(1):14–20.

————. 2012. Schelling’s conjecture on climate

and development: a test. In Climate Change and

Common Sense—Essays in Honour of Tom

Schelling, ed. R. W. Hahn and A. M. Ulph, 260–74.

Oxford: Oxford University Press.

————. 2013. The uncertainty about the social

cost of carbon: a decomposition analysis using

FUND. Climatic Change 117(3):515–30.

————. 2014. Climate policy under fat-tailed risk:

an application of FUND. Annals of Operations

Research 220(1):223–37.

Anthoff, D., R. S. J. Tol, and G. W. Yohe. 2009a.

Discounting for climate change. Economics—the

Open-Access, Open-Assessment E-Journal 3(2009-

24):1–22.

————. 2009b. Risk aversion, time preference,

and the social cost of carbon. Environmental

Research Letters 4(2):1–7.

Arent, D., R. S. J. Tol, E. Faust, J. P. Hella, S.

Kumar, K. M. Strzepek, F. L. Toth, and D. Yan.

2014. Key economic sectors and services. In

Climate Change 2014: Impacts, Adaptation, and

Vulnerability. Part A: Global and Sectoral Aspects.

Contribution of Working Group II to the Fifth

Assessment Report of the Intergovernmental Panel

on Climate Change, ed. C. B. Field, V. R. Barros, D.

J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E.

Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C.

Genova, B. Girma, E. S. Kissel, A. N. Levy, S.

MacCracken, P. R. Mastrandrea, and L. L.

White, 659–708. Cambridge: Cambridge

University Press.

Arrow, K. J. 2009. A note on uncertainty and dis-

counting in models of economic growth. Journal of

Risk and Uncertainty 38(2):87–94.

Atkinson, G. D., S. Dietz, J. Helgeson, C. J.

Hepburn, and H. Saelen. 2009. Siblings, not trip-

lets: social preferences for risk, inequality and time

in discounting climate change. Economics—the

Open-Access, Open-Assessment E-Journal 3(2009-

26):1–30.

Barreca, A. I. 2012. Climate change, humidity, and

mortality in the United States. Journal of

Environmental Economics and Management

63(1):19–34.

Barrios, S., L. Bertinelli, and E. Strobl. 2010.

Trends in rainfall and economic growth in

Africa: a neglected cause of the African growth

tragedy. Review of Economics and Statistics

92(2):350–66.

Berz, G. Undated. Insuring against catastrophe.

Disasters.

Bell, C., and H. Gersbach. 2013. Growth and en-

during epidemic diseases. Journal of Economic

Dynamics and Control 37(10):2083–103.

Bhattacharyya, S. 2009. Institutions, diseases and

economic progress: a unified framework. Journal of

Institutional Economics 5(1):65–87.

The Economic Impacts of Climate Change 19

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Bloom, D. E., D. Canning, and J. Sevilla. 2003.

Geography and poverty traps. Journal of Economic

Growth 8(4):355–78.

Bosello, F., F. Eboli, and R. Pierfederici. 2012.

Assessing the Economic Impacts of Climate

Change. Review of Environment Energy and

Economics:1–9.

Botzen, W. J. W., and J. C. J. M. van den Bergh.

2012. How sensitive is Nordhaus to Weitzman?

Climate policy in DICE with an alternative damage

function. Economics Letters 117(1):372–74.

Bretschger, L., and S. Valente. 2011. Climate

change and uneven development. Scandinavian

Journal of Economics 113(4):825–45.

Brouwer, R., S. Akter, L. Brander, and E. Haque.

2007. Socioeconomic vulnerability and adaptation

to environmental risk: a case study of climate

change and flooding in Bangladesh. Risk Analysis

27(2):313–26.

Brown, C., R. Meeks, K. Hunu, and W. Yu. 2011.

Hydroclimate risk to economic growth in sub-

Saharan Africa. Climatic Change 106(4):621–47.

Burke, M., M. Craxton, C. D. Kolstad, C. Onda, H.

Allcott, E. Baker, L. Barrage, R. Carson, K.

Gillingham, J. Graf-Zivin, M. Greenstone, S.

Hallegatte, W. M. Hanemann, G. Heal, S. Hsiang, B.

Jones, D. L. Kelly, R. Kopp, M. Kotchen, R.

Mendelsohn, K. Meng, G. Metcalf, J. Moreno-Cruz,

R. Pindyck, S. Rose, I. Rudik, J. Stock, and

R. S. J. Tol. 2016. Opportunities for advances in cli-

mate change economics. Science 352(6283):292–93.

Burke, M., S. M. Hsiang, and E. Miguel. 2015.

Global non-linear effect of temperature on eco-

nomic production. Nature 527(7577):235–39.

Chakravarty, S., and M. Tavoni. 2013. Energy

poverty alleviation and climate change

mitigation: Is there a trade off? Energy Economics

40(Suppl 1):S67–73.

Chalko, T. J. 2001. No second chance? Can Earth

explode as a result of global warming? NU Journal

of Discovery 3:1–9.

Chontanawat, J., L. C. Hunt, and R. Pierse. 2008.

Does energy consumption cause economic

growth? Evidence from a systematic study of over

100 countries. Journal of Policy Modeling

30(2):209–20.

Clarke, L., K. Jiang, K. Akimoto, M. H. Babiker,

Geoffrey J. Blanford, K. A. Fisher-Vanden, J. C.

Hourcade, Volker Krey, E. Kriegler, A. Loeschel,

D. W. McCollum, S. Paltsev, S. Rose, P. R. Shukla,

M. Tavoni, B. C. C. van der Zwaan, and D. P. van

Vuuren 2014. Assessing transformation pathways.

In Climate Change 2014: Mitigation of Climate

Change. Contribution of Working Group III to the

Fifth Assessment Report of the Intergovernmental

Panel on Climate Change, ed. O. Edenhofer, R.

Pichs-Madruga, and Y. Sokona. Cambridge:

Cambridge University Press.

Cotter, C., H. J. W. Sturrock, M. S. Hsiang, J. Liu,

A. A. Phillips, J. Hwang, C. S. Gueye, N. Fullman,

R. D. Gosling, and R. G. A. Feachem. 2013. The

changing epidemiology of malaria elimination:

new strategies for new challenges. Lancet

382(9895):900–911.

Crost, B., and C. P. Traeger. 2014. Optimal CO2 mitigation under damage risk valuation. Nature

Climate Change 4(7):631–36.

d’Arge, R. C. 1979. Climate and economic activity.

In Proceedings of the World Climate Conference, a

conference of experts on climate and mankind,

Geneva, 12–13 February 1979, 652–81. Geneva:

World Meteorological Organization.

d’Arge, R. C., W. D. Schulze, and D. S. Brookshire.

1982. Carbon dioxide and intergenerational

choice. American Economic Review 72(2):251–56.

Dell, M., B. F. Jones, and B. A. Olken. 2009.

Temperature and income: reconciling new cross-

sectional and panel estimates. American Economic

Review 99(2):198–204.

————. 2012. Temperature shocks and economic

growth: evidence from the last half century.

American Economic Journal: Macroeconomics

4(3):66–95.

————. 2014. What do we learn from the weather?

The new climate-economy literature. Journal of

Economic Literature 52(3):740–98.

Deschênes, O., and M. Greenstone. 2007. The

economic impacts of climate change: Evidence

from agricultural output and random fluctuations

in weather. American Economic Review

97(1):354–85.

Diamond, J. 1999. Guns, Germs, and Steel: The

Fates of Human Societies. New York: W.W.

Norton.

Dietz, S., and N. H. Stern. 2015. Endogenous

growth, convexity of damages and climate risk:

20 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

how Nordhaus’ framework supports deep cuts in

carbon emissions. Economic Journal

125(583):574–620.

Dorward, A., J. Kydd, J. Morrison, and I. Urey.

2004. A policy agenda for pro-poor agricultural

growth. World Development 32(1):73–89.

Easterly, W., and R. Levine. 2003. Tropics, germs,

and crops: how endowments influence economic

development. Journal of Monetary Economics

50(1):3–39.

Eboli, F., R. Parrado, and R. Roson. 2010. Climate-

change feedback on economic growth: explora-

tions with a dynamic general equilibrium model.

Environment and Development Economics

15(5):515–33.

EPA. 2017. Regulatory Impact Analysis for the

Review of the Clean Power Plan: Proposal.

Washington, DC: U.S. Environmental Protection

Agency.

Fankhauser, S. 1995. Valuing Climate Change - The

Economics of the Greenhouse. London: EarthScan.

Fankhauser, S., and R. S. J. Tol. 2005. On climate

change and economic growth. Resource and Energy

Economics 27(1):1–17.

Field, C. B., and O. F. Canziani, eds. 2014. Climate

Change 2014: Impacts, Adaptation, and

Vulnerability. Contribution of Working Group II to

the Fifth Assessment Report of the Intergovernmental

Panel on Climate Change. Cambridge: Cambridge

University Press.

Foley, J. A., N. Ramankutty, K. A. Brauman, E. S.

Cassidy, J. S. Gerber, M. Johnston, N. D. Mueller,

C. O’Connell, D. K. Ray, P. C. West, C. Balzer, E.

M. Bennett, S. R. Carpenter, J. Hill, C. Monfreda,

S. Polasky, J. Rockström, J. Sheehan, S. Siebert, D.

Tilman, and D. P. M. Zaks. 2011. Solutions

for a cultivated planet. Nature 478(7369):

337–42.

Frederick, S., G. Loewenstein, and T. O’Donoghue.

2002. Time discounting and time preference: a

critical review. Journal of Economic Literature

40(2):351–401.

Gallup, J. L., J. D. Sachs, and A. D. Mellinger. 1999.

Geography and economic development.

International Regional Science Review

22(2):179–232.

Golosov, M., J. Hassler, P. Krusell, and A.

Tsyvinski. 2014. Optimal taxes on fossil fuel in

general equilibrium. Econometrica 82(1):41–88.

Goulder, L. H., and K. Mathai. 2000. Optimal CO2 abatement in the presence of induced technologi-

cal change. Journal of Environmental Economics

and Management 39(1):1–38.

Guo, J., C. J. Hepburn, R. S. J. Tol, and D. Anthoff.

2006. Discounting and the social cost of climate

change: a closer look at uncertainty. Environmental

Science & Policy 9(3):205–16.

Hallegatte, S. 2005. The long time scales of the

climate-economy feedback and the climatic cost of

growth. Environmental Modeling and Assessment

10(4):227–89.

Havranek, T., Z. Irsova, K. Janda, and D.

Zilberman. 2015. Selective reporting and the social

cost of carbon. Energy Economics 51:394–406.

Hay, S. I., C. A. Guerra, A. J. Tatem, A. M. Noor,

and R. W. Snow. 2004. The global distribution and

population at risk of malaria: past, present, and

future. Lancet Infectious Diseases 4(6):327–36.

Heal, G. 2017. The economics of the climate.

Journal of Economic Literature 55(3):1046–63.

Hertel, T. W., R. Keeney, M. Ivanic, and L. A.

Winters. 2009. Why isn’t the Doha Development

Agenda more poverty friendly? Review of

Development Economics 13(4):543–59.

Hoel, M., and T. Sterner. 2007. Discounting and

relative prices. Climatic Change 84(3–4):265–80.

Hope, C. W. 2006. The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model

Incorporating the IPCC’s Five Reasons for

Concern. Integrated Assessment Journal

6(1):19–56.

Horowitz, J. K. 2009. The income-temperature

relationship in a cross-section of countries and its

implications for predicting the effects of global

warming. Environmental and Resource Economics

44(4):475–93.

Horowitz, J. K., and A. Lange. 2014. Cost-benefit

analysis under uncertainty — a note on

Weitzman’s dismal theorem. Energy Economics

42:201–3.

Howarth, R. B., M. D. Gerst, and M. E. Borsuk.

2014. Risk mitigation and the social cost of carbon.

Global Environmental Change 24(1):123–31.

Howden, S. M., J. F. Soussana, F. N. Tubiello, N.

Chhetri, M. Dunlop, and H. Meinke. 2007.

Adapting agriculture to climate change.

Proceedings of the National Academy of

The Economic Impacts of Climate Change 21

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Sciences of the United States of America

104(50):19691–96.

Hsiang, S. M., and K. C. Meng. 2015. Tropical

economics. American Economic Review

105(5):257–61.

Hsiang, S., R. Kopp, A. Jina, J. Rising, M. Delgado,

S. Mohan, D. J. Rasmussen, R. Muir-Wood, P.

Wilson, M. Oppenheimer, K. Larsen, and T.

Houser. 2017. Estimating economic damage from

climate change in the United States. Science

356(6345):1362–69.

IAWGSCC. 2010. Technical Support Document:

Social Cost of Carbon for Regulatory Impact Analysis

Under Executive Order 12866. Washington, DC:

Interagency Working Group on the Social Cost of

Carbon.

————. 2013. Technical Support Document:

Technical Update of the Social Cost of Carbon for

Regulatory Impact Analysis Under Executive Order

12866. Washington, DC: Interagency Working

Group on the Social Cost of Carbon.

————. 2015. Technical support document:

Social cost of carbon for regulatory impact analysis

under Executive Order 12866. In Social Cost of

Carbon Estimates for Regulatory Impact Analysis:

Development and Technical Assessment, ed. J. Keck.

New York: Nova.

Jensen, S., and C. P. Traeger. 2014. Optimal cli-

mate change mitigation under long-term growth

uncertainty: stochastic integrated assessment and

analytic findings. European Economic Review

69:104–125.

Keller, K., B. M. Bolker, and D. F. Bradford. 2004.

Uncertain climate thresholds and optimal eco-

nomic growth. Journal of Environmental Economics

and Management 48(1):723–41.

Knutti, R., and G. C. Hegerl. 2008. The equilibrium

sensitivity of the Earth’s temperature to radiation

changes. Nature Geoscience 1(11):735–43.

Lemoine, D., and S. Kapnick. 2016. A top-down

approach to projecting market impacts of climate

change. Nature Climate Change 6(1):51–55.

Lemoine, D., and I. Rudik. 2017. Steering the cli-

mate system: using inertia to lower the cost of

policy. American Economic Review

107(10):2947–57.

Lemoine, D., and C. Traeger. 2014. Watch your

step: optimal policy in a tipping climate.

American Economic Journal: Economic Policy

6(1):137–66.

Lewis, N. 2013. An objective Bayesian improved

approach for applying optimal fingerprint techni-

ques to estimate climate sensitivity. Journal of

Climate 26(19):7414–29.

Lichtenstein, S., and B. Fischhoff. 1977. Do those

who know more also know more about how much

they know? Organizational Behavior and Human

Performance 20(2):159–83.

Lipton, M. 1980. Migration from rural areas of

poor countries: the impact on rural productivity

and income distribution. World Development

8(1):1–24.

Maddison, D. J. 2003. The amenity value of the

climate: the household production function ap-

proach. Resource and Energy Economics

25(2):155–75.

Maddison, D., and K. Rehdanz. 2011. The impact

of climate on life satisfaction. Ecological Economics

70(12):2437–45.

Maier-Reimer, E., and K. Hasselmann. 1987.

Transport and storage of carbon dioxide in the

ocean: an inorganic ocean circulation carbon cycle

model. Climate Dynamics 2(2):63–90.

Martens, W. J. M., T. H. Jetten, and D. A. Focks.

1997. Sensitivity of malaria, schistosomiasis and

dengue to global warming. Climatic Change

35(2):145–56.

Masters, W. A., and M. S. McMillan. 2001. Climate

and scale in economic growth. Journal of Economic

Growth 6(3):167–86.

Mendelsohn, R., and A. Dinar. 1999. Climate

change, agriculture, and developing countries:

Does adaptation matter? World Bank Research

Observer 14(2):277–93.

Mendelsohn, R. O., M. E. Schlesinger, and L. J.

Williams. 2000. Comparing Impacts across

Climate Models. Integrated Assessment 1(1):37–48.

Michaelowa, A., and K. Michaelowa. 2007. Climate

or development: Is ODA diverted from its original

purpose? Climatic Change 84(1):5–21.

————. 2011. Coding error or statistical embel-

lishment? The political economy of reporting cli-

mate aid. World Development 39(11):2010–20.

Millner, A. 2013. On welfare frameworks and cat-

astrophic climate risks. Journal of Environmental

Economics and Management 65(2):310–25.

22 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Moore, F. C., and D. B. Diaz. 2015. Temperature

impacts on economic growth warrant stringent

mitigation policy. Nature Climate Change

5(2):127–131.

Moyer, E. J., M. D. Woolley, N. J. Matteson, M. J.

Glotter, and D. A. Weisbach. 2014. Climate

impacts on economic growth as drivers of uncer-

tainty in the social cost of carbon. Journal of Legal

Studies 43(2):401–25.

Mueller, N. D., J. S. Gerber, M. Johnston, D. K.

Ray, N. Ramankutty, and J. A. Foley. 2012. Closing

yield gaps through nutrient and water manage-

ment. Nature 490(7419):254–57.

Myers, N. 1993. Environmental refugees in a

globally warmed world. BioScience 43(11):752–61.

Nakicenovic, N., and R. J. Swart, eds. 2001. IPCC

Special Report on Emissions Scenarios. Cambridge:

Cambridge University Press.

Nordhaus, W. D. 1977. Economic growth and

climate: the case of carbon dioxide. American

Economic Review 67(1):341–46.

———— 1982. How Fast Should We Graze the

Global Commons? American Economic Review

72(2):242–6.

———— 1991. To Slow or Not to Slow: The

Economics of the Greenhouse Effect. Economic

Journal 101(444):920–37.

———— 1994a. Expert opinion on climate change.

American Scientist 82(1):45–51.

———— 1994b. Managing the Global Commons:

The Economics of Climate Change. Cambridge: The

MIT Press.

———— 2006. Geography and Macroeconomics:

New Data and New Findings. Proceedings of the

National Academy of Science 103(10):3510–7.

———— 2008. A Question of Balance – Weighing the

Options on Global Warming Policies. New Haven:

Yale University Press.

———— 2013. The Climate Casino: Risk,

Uncertainty and Economics for a Warming World.

New Haven: Yale University Press.

Nordhaus, W. D., and J. G. Boyer. 2000. Warming

the World: Economic Models of Global Warming.

Cambridge, Massachusetts - London, England:

The MIT Press.

Nordhaus, W. D., and Z. Yang. 1996. RICE: A

Regional Dynamic General Equilibrium Model of

Optimal Climate-Change Policy. American

Economic Review 86(4):741–65.

Olsson, O., and D. A. Hibbs, Jr. 2005.

Biogeography and long-run economic develop-

ment. European Economic Review 49(4):909–38.

Oppenheimer, M., M. Campos, R. F. Warren, J.

Birkmann, G. Luber, B. O’Neill, and K. Takahashi.

2014. Emergent risks and key vulnerabilities. In

Climate Change 2014: Impacts, Adaptation and

Vulnerability. Contribution of Working Group II to

the Fifth Assessment Report of the Intergovernmental

Panel on Climate Change, ed. C. B. Field and O. F.

Canziani Cambridge: Cambridge University Press.

Pigou, A. C. 1920. The Economics of Welfare.

London: MacMillan and Co.

Pindyck, R. S. 2013. Climate change policy: what

do the models tell us? Journal of Economic

Literature 51(3):860–72.

———— 2017. The use and misuse of models for

climate policy. Review of Environmental Economics

and Policy 11(1):110–14.

Plambeck, E. L., and C. W. Hope. 1996. PAGE95 -

An Updated Valuation of the Impacts of Global

Warming. Energy Policy 24(9):783–93.

Porter, J. R., L. Xie, A. J. Challinor, K. Cochrane, S.

M. Howden, M. M. Iqbal, D. B. Lobell, and M. I.

Travasso. 2014. Food security and food production

systems. In Climate Change 2014: Impacts,

Adaptation, and Vulnerability. Part A: Global and

Sectoral Aspects. Contribution of Working Group II

to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change, ed. C.

B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M.

D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L.

Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S.

Kissel, A. N. Levy, S. MacCracken, P. R.

Mastrandrea, and L. L. White, 659–708.

Cambridge: Cambridge University Press.

Potsdam Institute for Climate Impact Research

and Climate Analytics. 2012. Turn down the heat:

why a 4 �C warmer world must be avoided.

Washington, DC: World Bank.

Rehdanz, K., and D. J. Maddison. 2005. Climate

and happiness. Ecological Economics 52(1):111–25.

Revesz, R., M. Greenstone, M. Hanemann, M.

Livermore, T. Sterner, D. Grab, P. Howard, and J.

Schwartz. 2017. Best cost estimate of greenhouse

gases. Science 357(6352):655.

The Economic Impacts of Climate Change 23

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

Revesz, R. L., P. H. Howard, K. Arrow, L. H.

Goulder, R. E. Kopp, M. A. Livermore, M.

Oppenheimer, and T. Sterner. 2014. Global

warming: improve economic models of climate

change. Nature 508(7495):173–75.

Roe, G. H., and M. B. Baker. 2007. Why is climate

sensitivity so unpredictable? Science

318(5850):629–32.

Roson, R., and D. van der Mensbrugghe. 2012.

Climate change and economic growth: Impacts

and interactions. International Journal of

Sustainable Economy 4(3):270–285.

Schelling, T. C. 1992. Some economics of global

warming. American Economic Review

82(1):1–14.

Schelling, T. C. 2000. Intergenerational and inter-

national discounting. Risk Analysis 20(6):833–37.

Seder, R. A., L. J. Chang, M. E. Enama, K. L.

Zephir, U. N. Sarwar, I. J. Gordon, L. A. Holman,

E. R. James, P. F. Billingsley, A. Gunasekera, A.

Richman, S. Chakravarty, A. Manoj, S.

Velmurugan, M. Li, A. J. Ruben, T. Li, A. G.

Eappen, R. E. Stafford, S. H. Plummer, C. S.

Hendel, L. Novik, P. J. M. Costner, F. H. Mendoza,

J. G. Saunders, M. C. Nason, J. H. Richardson, J.

Murphy, S. A. Davidson, T. L. Richie, M. Sedegah,

A. Sutamihardja, G. A. Fahle, K. E. Lyke, M. B.

Laurens, M. Roederer, K. Tewari, J. E. Epstein, B.

K. L. Sim, J. E. Ledgerwood, B. S. Graham, and S. L.

Hoffman. 2013. Protection against malaria by in-

travenous immunization with a nonreplicating

sporozoite vaccine. Science 341(6152):1359–65.

Smith, J. B., H.-J. Schellnhuber, M. Q. Mirza, S.

Fankhauser, R. Leemans, L. Erda, L. Ogallo, A. B.

Pittock, R. G. Richels, C. Rosenzweig, U. Safriel, R.

S. J. Tol, J. P. Weyant, and G. W. Yohe. 2001.

Vulnerability to climate change and reasons for

concern: a synthesis. In Climate Change 2001:

Impacts, Adaptation, and Vulnerability, ed. J. J.

McCarthy, O. F. Canziani, N. A. Leary, D. J.

Dokken, and K. S. White, 913–67. Cambridge:

Cambridge University Press.

Solow, R. M. 1956. A contribution to the theory of

economic growth. Quarterly Journal of Economics

70(1):65–94.

Steinbuks, J., and V. Foster. 2010. When do firms

generate? Evidence on in-house electricity supply

in Africa. Energy Economics 32(3):505–14.

Stern, D. I., and A. Kander. 2012. The role of

energy in the industrial revolution and modern

economic growth. Energy Journal 33(3):125–52.

————. 2008. The economics of climate change.

American Economic Review 98(2):1–37.

Stern, N. 2010. Presidential address: imperfections

in the economics of public policy, imperfections in

markets, and climate change. Journal of the

European Economic Association 8(2–3):253–88.

———— 2013. The structure of economic modeling

of the potential impacts of climate change: grafting

gross underestimation of risk onto already narrow

science models. Journal of Economic Literature

51(3):838–59.

Stern, N. H., S. Peters, V. Bakhski, A. Bowen, C.

Cameron, S. Catovsky, D. Crane, S. Cruickshank,

S. Dietz, N. Edmondson, S.-L. Garbett, L. Hamid,

G. Hoffman, D. Ingram, B. Jones, N. Patmore, H.

Radcliffe, R. Sathiyarajah, M. Stock, C. Taylor, T.

Vernon, H. Wanjie, and D. Zenghelis. 2006. Stern

Review: The Economics of Climate Change. London:

Her Majesty’s Treasury.

Sterner, T., and U. M. Persson. 2008. An even

Sterner Review: introducing relative prices into the

discounting debate. Review of Environmental

Economics and Policy 2(1):61–76.

Tol, R. S. J. 1995. The Damage Costs of Climate

Change Toward More Comprehensive

Calculations. Environmental and Resource

Economics 5(4):353–74.

———— 2002. Estimates of the Damage Costs of

Climate Change - Part I: Benchmark Estimates.

Environmental and Resource Economics

21(1):47–73.

———— 2005a. Emission abatement versus devel-

opment as strategies to reduce vulnerability to

climate change: an application of FUND.

Environment and Development Economics

10(5):615–29.

———— 2005b. The marginal damage costs of

carbon dioxide emissions: an assessment of the

uncertainties. Energy Policy 33(16):2064–74.

———— 2009. The economic effects of climate

change. Journal of Economic Perspectives

23(2):29–51.

———— 2010. International inequity aversion and

the social cost of carbon. Climate Change

Economics 1(1):21–32.

24 R. S. J. Tol

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019

———— 2011. The social cost of carbon. Annual

Review of Resource Economics 3:419–43.

———— 2012. Leviathan taxes in the short run.

Climatic Change Letters 113(3–4):1049–63.

———— 2013a. Climate policy under the

Bentham–Rawls criterion. Economics Letters

118(3):424–28.

———— 2013b. Targets for global climate policy:

an overview. Journal of Economic Dynamics &

Control 37(5):911–28.

———— 2014. Climate Economics: Economic

Analyses of Climate, Climate Change, and Climate

Policy. Cheltenham: Edward Elgar.

Tol, R. S. J., and H. Dowlatabadi. 2001. Vector-

borne diseases, development & climate change.

Integrated Assessment 2(4):173–81.

Tol, R. S. J., K. L. Ebi, and Gary W. Yohe. 2007.

Infectious disease, development, and climate

change: a scenario analysis. Environment and

Development Economics 12:687–706.

Tol, R. S. J., and A. Langen. 2000. A concise history

of Dutch river floods. Climatic Change

46(3):357–69.

van de Vliert, E. 2008. Climate, Affluence and

Culture. Cambridge: Cambridge University Press.

van de Vliert, E., and R. S. J. Tol. 2014. Harsh

climate promotes harsh governance (except in

cold-dry-wealthy environments). Climate Research

61(1):19–28.

van den Bergh, J. C. J. M., and W. J. W. Botzen.

2014. A lower bound to the social cost of CO2 emissions. Nature Climate Change 4(4):253–58.

————. 2015. Monetary valuation of the social

cost of CO2 emissions: a critical survey. Ecological

Economics 114:33–46.

van den Bijgaart, I., R. Gerlagh, L. Korsten, and M.

Liski. 2013. A simple formula for the social costs of

carbon. In Nota di Lavoro. Milan: Fondazione Eni

Enrico Mattei.

van der Ploeg, F. 2014. Abrupt positive feedback

and the social cost of carbon. European Economic

Review 67:28–41.

van Lieshout, M., R. S. Kovats, M. T. J. Livermore,

and W. J. M. Martens. 2004. Climate change and

malaria: analysis of the SRES climate and socio-

economic scenarios. Global Environmental Change

14(1):87–99.

van Vuuren, D. P., J. Edmonds, M. Kainuma, K.

Riahi, A. Thomson, K. Hibbard, G. C. Hurtt, T.

Kram, V. Krey, J. F. Lamarque, T. Masui, M.

Meinshausen, N. Nakicenovic, S. J. Smith, and S. K.

Rose. 2011. The representative concentration path-

ways: an overview. Climatic Change 109(1):5–31.

Weitzman, M. L. 2011. Fat-tailed uncertainty in

the economics of catastrophic climate change.

Review of Environmental Economics and Policy

5(2):275–92.

WHO. 2014. 7 million premature deaths annually

linked to air pollution. News release. World Health

Organization, Geneva.

Wigley, T. M. L., R. G. Richels, and J. A. Edmonds.

1996. Economic and environmental choices in the

stabilization of atmospheric CO2 concentrations.

Nature 379(6562):240–43.

Winters, L. A., and A. Martuscelli. 2014. Trade

liberalization and poverty: What have we learned

in a decade? Annual Review of Resource Economics

6(1):493–512.

Winters, L. A., N. McCulloch, and A. McKay. 2004.

Trade liberalization and poverty: the evidence so

far. Journal of Economic Literature 42(1):72–115.

Yohe, G. W., and R. S. J. Tol. 2002. Indicators for

social and economic coping capacity—moving

towards a working definition of adaptive capacity.

Global Environmental Change 12(1):25–40.

The Economic Impacts of Climate Change 25

D ow

nloaded from https://academ

ic.oup.com /reep/article-abstract/12/1/4/4804315 by U

niversity of O regon Libraries user on 01 O

ctober 2019