Research Paper-Two Parts SECOND PART-3pages Third Part 4 pages 7 pages total

ORIGINAL PAPER

A CGE analysis of carbon market impact on CO2 emission reduction in China: a technology-led approach

Lin Yang1 • Yunfei Yao2 • Jiutian Zhang3 • Xian Zhang3 •

Karl J. McAlinden4

Received: 14 April 2015 / Accepted: 7 December 2015 / Published online: 17 December 2015 � Springer Science+Business Media Dordrecht 2015

Abstract To offer a scientific basis and reference for policy makers when developing the regulation framework for Chinese national carbon market, this research analyzes the

environmental and economic effects of the carbon market from a mid- to long-term per-

spective in China. Through a computable general equilibrium model, with technological

progress as an endogenous variable, this study provides a comprehensive investigation on

the relationships between the price of carbon, technological development, emission

reduction and economic growth in low, mid- and high emission reduction scenarios.

Furthermore, it analyzes the potential of price-setting mitigation measures to create

incentives for technological progress. The results indicate that the carbon market will have

a positive impact on the R&D investment in China and in turn would promote technology

development. Consequently, it is vital that the carbon price is high enough; otherwise, the

pressure on technological development will not be sufficiently strong; there is compati-

bility between carbon intensity reduction and mid- and long-term GDP growth, but it

seems impossible to realize the positive effects on both emission reduction and economy

development at the early stage. Hence, the carbon market alone will not be a cost-effective

instrument for emission abatement and other auxiliary policies will also be needed in the

early stages. Meanwhile, selection of reasonable emission reduction scenarios will play a

crucial role in achieving the pre-2020 and post-2020 carbon reduction commitments

efficiently in China.

& Xian Zhang [email protected]

1 School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China

2 Sinopec Research Institute of Petroleum Engineering, Sinopec Group, Beijing 100101, China

3 The Administrative Center for China’s Agenda 21, Ministry of Science and Technology, Beijing 100038, China

4 Geo-Energy Research Centre and School of Contemporary Chinese Studies, The University of Nottingham, Nottingham, UK

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Nat Hazards (2016) 81:1107–1128 DOI 10.1007/s11069-015-2122-y

Keywords Carbon market � Technological progress � CO2 emission reduction � Economic growth � CGE model

1 Introduction

In recent years, there has been an increase in attention from Chinese academics,

researchers and policy makers toward the causes and effects of global climate change. As a

major force in the global economy, China has experienced an average economic growth

rate exceeding 9 % each year since the economic reforms of 1978. Yet, along with rising

economic prosperity, there has been a corresponding increase in carbon emissions.

However, with the IEA attributing the global halt in emissions growth of 2014 to changing

patterns of energy consumption in China and OECD countries, China still produced almost

25 % of the world’s total greenhouse gas (GHG) emissions (IEA 2015). Faced with

international pressure to clarify its targets for reducing GHG emissions, China has made a

series of mitigation commitments and determined compulsory and verifiable targets within

the framework of its domestic development program. At the 15th Conference of the Parties

(COP 15) to the United Nations Framework Convention on Climate Change (UNFCCC), in

Copenhagen, China made a pledge to reduce its CO2 emission intensity per unit of GDP

(gross domestic product) by 40–45 % by 2020, compared to 2005 levels. Following this, in

2015, China again outlined its post-2020 commitments to voluntarily cut emissions. In this

document, China pledged to peak its CO2 emissions on, or even before, 2030 and to cut its

CO2 emission intensity per unit of GDP by 60–65 % from 2005 levels. How China will

efficiently achieve these stringent mitigation targets has become a huge challenge for the

country.

Generally, a reduction in carbon intensity can involve three main factors: an adjustment

of industrial structures, the optimization of the energy structure and technological progress

(Li and Zhou 2006; Ma and Stern 2008; Yao 2012). The ‘Climate Change Report’ (2001)

pointed out that technological progress is the most important factor in emission reduction.

Moreover, China is at a critical stage of industrialization and urbanization and has many

carbon-intensive industries, which counteract the impact of industrial structure adjustment

and energy structure optimization on CO2 emission reduction (Wu et al. 2005; Liao et al.

2007). Fortunately, however, technological progress accounted for 56 % of carbon

intensity reductions between 1990 and 2005 and, if calculations are right, should fulfill

China’s carbon reduction targets in 2020 through a further 43 % reduction (He 2011).

Therefore, China’s reliance on scientific and technological progress to develop low-carbon

technologies has become the main approach to reducing GHG emissions (Feng 2010; Sun

2011; Li and Qu 2012).

Previously, environmental policies in China were mainly implemented in the manner of

command and control (CAC), in which entities were obliged to strictly comply with

emissions standards. With the absence of effective incentives, the private sector typically

underinvested in research, development and demonstration, as well as the ultimate

deployment of new technologies. With such results, China is now looking toward market-

based instruments, such as an emissions trading scheme (ETS), to reduce CO2 emissions.

Under such a scheme, participants are allocated a certain quantity of emissions allowances

within a specified period. If they wish to emit more or fewer emissions than are covered by

their given allowances, they have the option to either buy allowances from others or to sell

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the allowances they have been allotted. As this market theory proposes, participants are

able to adjust their consuming behavior according to their marginal abatement costs. If

marginal abatement costs are higher than the price of allowance, participants can buy

additional allowances; if they are lower, then it is beneficial to invest in upgrading energy

technologies to reduce carbon emissions, enabling them to sell their surplus allowance

(Braun 2009; Jaehn and Letmathe 2009). Hence, the carbon market is considered a cost-

effective market-based instrument to control carbon emissions through the provision of

economic incentives to participants. The central feature is the creation of a price on

emissions, which can be used as a signal to participants which they, in turn, factor into their

decision making. This works to internalize environmental costs and helps to achieve a

much more efficient allocation of resources, when compared to other environmental

policies. However, it must be pointed out that there are concerns that the carbon prices

generated through such carbon markets may be too low to create the incentives necessary

to stimulate the level of technological development needed (Lundgren et al. 2015).

China has signaled its strong intention to establish a national carbon market. In late

2011, during the Twelfth Five-Year Plan (2010–2015), the Chinese Government estab-

lished regional carbon markets by assigning seven pilots within two provinces (Guangdong

and Hubei) and five cities (Beijing, Tianjin, Shanghai, Chongqing and Shenzhen). The

short-term goal has been to establish trans-provincial and trans-regional carbon markets, to

transition to a national scheme by 2015. Subsequently, the idea of setting up a national

carbon market had been written into the national 12th ‘Five-Year Plan for National Eco-

nomic and Social Development’ (FYP), which covers the period 2011–2015 (State Council

2011). The current carbon market covers electricity, steel, cement and other heavy

industries which have a greater potential to abate their carbon emissions, while at the same

time perhaps exerting a significant negative influence on the economy. It can be seen that

the carbon market will play an important role in reducing China’s carbon emissions in the

near future. Li (2013) noted that the objectives of the carbon market are threefold: (1) to

promote emission reduction, or at least reduce the growth rate of carbon emissions; (2) to

reduce the cost of carbon abatement for society as a whole; and (3) to promote techno-

logical progress. At the same time, the carbon market should also take into account and

ensure China’s economic development objectives, while also achieving its carbon intensity

reduction targets, as well as attempting to protect the security of the global economy.

The computable general equilibrium (CGE) model is widely used to analyze the impact

of policies, such as taxes, subsidies or quotas. It combines the abstract general equilibrium

structure, with real economic data to numerically solve the levels of supply, demand and

price that support equilibrium across a specified set of markets (Wing 2004). The CGE

model has been increasingly applied in China to assess environmental and energy-related

policies, such as those on energy investment and energy efficiency (He et al. 2010; Lu et al.

2010), as well as carbon and environment taxes (Li et al. 2009; Shi and Zhou 2010).

Several studies have specifically assessed inter-provincial carbon markets or regional

carbon intensity targets using a multi-region CGE model. Wang et al. (2015) and Cheng

et al. (2015) constructed their CGE models to simulate the impacts of carbon markets on

economic benefits and pollutant emissions of the pre-2020 mitigation pledge from

Guangdong Province and determined that the carbon price and economic impacts are

closely related to both emissions constraints and trading systems. Cui et al. (2014) explored

the cost-saving effects of the carbon market in China for achieving its 2020 intensity

reduction targets through utilizing an inter-provincial emissions trading model and con-

firmed that the emission trading scheme will play an important role in promoting a cost-

effective means for China’s CO2 emission reduction. Nevertheless, these studies are not

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amenable to the post-2020 mitigation targets and ignore the relationship between the

carbon price and technological progress. Therefore, in order to provide technically viable

and economically affordable options for China to combat climate change and for the

realization of the pre-2020 and post-2020 mitigation pledges, this paper constructs a multi-

sector recursive dynamic CGE model to explore the technological progress and economic

efficiency brought about by the carbon market and aims to provide evidence and advice for

government.

In order to allow for a better understanding of this topic, Sect. 2 provides a short

description of the relationship between carbon markets and technological progress and

Sect. 3 sets out the model used. In Sect. 4, the results are derived and discussed, while

Sect. 5 concludes the paper.

2 The relationship between carbon market and technological progress

Technological progress can enhance resource utilization, which in turn decreases energy

consumption, reduces pollution emissions and limits ecological damage at a certain level

of output (Yu and Qi 2007). Studies have shown that technical effects have lowered

China’s carbon intensity and significantly curbed carbon emissions in recent decades (Liu

et al. 2007; Guo 2010; Chen and Lin 2015). Carbon markets operates by first setting a ‘cap’

on the amount of carbon emissions that regulated enterprises are permitted to release. With

a carbon emission allowance created for each ton of capped emissions, these allowances

are distributed to firms and other entities either through free allocation or auction, or some

combination of the two. While allowances can be traded freely between market partici-

pants, at the end of each compliance period, regulated firms must surrender allowances to

the government equivalent to their carbon emissions. Trading gives these regulated firms

the flexibility to either reduce their emissions through technological innovation or to buy

allowances from other firms, which finally minimizes the overall economic cost of the

program. One of the central features of a cap-and-trade system is that it creates a price on

emissions (Abadie and Chamorro 2008; Zhang and Wei 2010). Ensuring that industry and

consumers see this price signal and factor it into their decision making is essential to create

the incentive to reduce emissions and to invest in low-carbon technologies. Prior research

has shown that the impacts on large-scale investment in R&D efforts are fairly limited

(Laurikka 2006; Hoffmann 2007; Klingelhöfer 2009), mainly because a low-carbon price

does not provide a sufficient incentive for the rapid adoption of advanced technology. It is

likely that excessive carbon emissions allocation would result in driving down the carbon

price, thus would not constitute effective incentivization and would be considered a

restrictive mechanism. Conversely, insufficient carbon emissions allocations would likely

drive the carbon price upward, which could increase the burden on enterprises and in turn

hinder the progress of science and technology, as well as raising energy prices in the long

run (Zhang and Wei 2010). Thus, the resulting price signal is crucial to inducing tech-

nological innovation and initiating the changes needed in corporate behavior necessary to

reduce emissions (Requate 2005). Any unreasonable intervention is likely to seriously

distort the market and may impede investment in low-carbon technology in the future (Don

and Cal 2011). What is noticeable is that the carbon price is primarily driven by the

balancing between supply and demand: On the supply side, the number of allowances

distributed is determined through national allocation plans, while on the demand side, the

use of carbon allowances is a function of expected carbon emissions (Requate and Unold

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2003; Julien 2011). Therefore, regardless of the number and variety of market participants,

the mitigation target of the carbon market will ultimately be the most important price

driver (Peace and Juliani 2009). The market is formed through the scarcity in the right to

emit CO2, which has been created through the capping of emissions. A more stringent cap

would lead to higher allowance prices and a higher market value, which would increase the

costs of the program; however, this also creates the necessary incentive for innovation. At

the same time, the creation of a market with carbon prices that move too high too quickly

can have significant implications both for the economy and for the political viability of the

program, particularly if high energy and carbon-based feedstock prices push industry to

rebel against the new regulations.

Setting up a carbon market requires taking into account not only the effect on abatement

but also considerations toward wider macro-economic interests. Emissions mitigation

policies can easily lead to a decrease in economic output or material shortages, as well as

causing high prices and even unemployment, which would counteract any gains from

technological progress, as well as reducing any welfare benefits received for society as a

whole (Yao 2012; Julien 2011; Jaffe et al. 2002; Shi et al. 2010; Shi and Zhou 2010).

Although the carbon market is different from traditional environmental policies, which

generally have significant negative effects on economic growth, the design of the trading

mechanism to promote low-carbon technological innovation and economic growth is

critical to its success (Peace and Juliani 2009). Judging from the situation outside China,

carbon markets have different impacts on different regions, which is largely due to the

differences in CO2 emission reduction targets, quotas and carbon prices (Ahammad et al.

2001; Chevallier et al. 2009; Anger and Oberndorfer 2008; Convery et al. 2008; Demailly

and Quirion 2008; Oberndorfer and Rennings 2007; Klepper and Peterson 2006). Chinese

scholars have not yet widely studied the impact of the carbon market on economic growth,

but some studies on the impact of other mitigation policies on the economy show that these

policies have significantly improved energy efficiency, although they do have negative

impacts on economic growth (Gao and Chen 2002; Wei 2002; Zhu et al. 2010). A rea-

sonable carbon trading mechanism should reduce carbon intensity while lowering pro-

duction costs through the promotion of technological progress and minimizing the negative

impacts on industrial outputs and economic costs. Thus, choosing the best carbon reduction

program, through measuring the economic costs under different abatement scenarios, is

vital for the establishment of the national carbon market and for economic growth in

China.

Overall, environmental policies play an important role in the coordination of energy

with economic development and environmental protection, as well as addressing global

climate change. If properly introduced, such policies could promote a pattern of behavioral

change in producers and consumers, thus guiding the development of energy demand

toward strategic energy targets. In contrast, improper environmental policies could seri-

ously hinder economic development and any advancement in energy technologies. While

the foundation and implementation of any policy would be a highly complex process, there

are usually many primary elements to be set for policy certainty. Furthermore, the

uncertain effects of any policy could also be a major obstacle in its formulation and

implementation. Therefore, in order to promote an efficient and reasonable decision-

making process, it is necessary to develop the proper tools for performing precise market

policy simulations, both scientifically and conveniently.

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3 Methodology

3.1 Framework of CGE

The computable general equilibrium (CGE) model, which has emerged from the general

equilibrium theory of Walras (1964), allows for a better understanding of the role of the

market and the interactions between different agents in macro-economic systems. Holding

the potential to capture a much wider set of economic impacts, the model can be used to

evaluate the implementation of a policy reform, as well as the distributive effects on the

economy at different levels of disaggregation. However, it must be recognized that with the

collection of high-quality, up-to-date economic data and the building of complex social

accounting matrixes (SAM), as well as the programming and calibrating a CGE model,

such models can be a very time-consuming process, especially when technical details are

limited.

In this paper, based on the basic assumptions of previous studies, we assume that

producers, in all sectors, have the same objectives, to use the best technologies in the

pursuit of profit maximization. The household sector is made up of numerous consumers

with the same pursuance, and the decision goal is to realize utility maximization in con-

sumption. Meanwhile, we assume that markets are fully competitive and that the price of

commodities is exogenous for the producers and consumers. In addition, based on the

theory of endogenous economic growth of Romer’s assumed conditions (1990), our model

introduces technological progress as the accumulation of knowledge capital that is derived

from the investment in research and development. Technical change is an alteration in the

character of productive activity, allowing for more output to be produced with the same

Fig. 1 Framework of CGE

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quantities of inputs or, symmetrically, to facilitate the creation of the same level of output

while using less input. As an input factor, the incremental accumulation of knowledge

capital happens due to R&D investments and can be used to substitute other physical inputs

in the production process. So the growing trajectories of knowledge capital growth rep-

resent the technological change. Knowledge capital stock has extensively been studied in

the CGE model (Wing 2006; Fisher-Vanden and Wing 2008; Loisel 2009; Bye and

Jacobsen 2011), for its important effect on endogenous economic growth theory (Lucas

1988; Romer 1990; Aghion and Howitt 1990; Barro 1996). This study introduces the

knowledge capital account and the R&D investment account into the SAM, while using

Terleckyj’s method to estimate knowledge capital (Wing 2006; Fisher-Vanden and Wing

2008). We hope that the inclusion of these accounts can be helpful in understanding the

effects of the carbon market in China from another perspective.

As shown in Fig. 1, this model divides consumers into three types: households, cor-

porations and government, while showing the correlation and interaction between the

agents, taxes, transfer payments and revenue. This model also includes energy-related

Table 1 The corresponding 24 sectors

Sectors Description

Agriculture Agriculture, forestry, animal husbandry and fishery

Ferrous Mining, ores smelting and rolling of ferrous metals

Non-metal Mining, processing and manufacture of non-metal ores or products

Chemical Chemical industry

Non-ferrous Mining, smelting and rolling of non-ferrous metals

Paper Papermaking, printing and manufacture of cultural articles

Food Manufacture of foods and tobacco

Text Manufacture of textile

Cloth Manufacture of textiles, clothing, footwear, caps, leather, fur, feather (down) and its realty products

Wood Processing of timber and manufacture of furniture

Metal Manufacture of metal products

Manufacture Manufacture of machinery and equipment for transport, electrical, communication, computer and other electronics, measuring and cultural activity and office work

Other heavy industry

Manufacture of artwork, other manufacture, scrap and waste

Construction Construction

Transportation Traffic, transport and storage

Service Services

Water Production and distribution of water

Coke Coking

Gas Production and distribution of gas

Coal Mining and washing of coal

Oil Extraction of petroleum

Natural gas Extraction of natural gas

Petroleum Processing of petroleum and nuclear fuel

Electric power Production and supply of electric power and heat power

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carbon emissions so that it can simulate different energy and environmental policies.

Furthermore, in view of China’s energy-intensive and high-emission foreign trade struc-

ture, this model sets a foreign account to form an open economy model.

In order to analyze the long-term impact of the Chinese carbon market, the model

established in this paper is a recursive dynamic CGE model with 24 sectors (as shown in

Table 1) with two types of people—urban and rural households, as well as government,

corporate and foreign accounts. The main module is made up of production, household,

investment, foreign trade and environment, as well as the principles for macro-closure and

market clearing.

3.1.1 Production module

This module, made up of ordinary production sectors, production sectors with natural

resource inputs and sectors with energy resources, represents the production function by

applying nested constant elasticity of substitution (CES). The objective of each producer is

to maximize profits within the limitations of production technology. Figure 2 shows a

simple example of the nesting relation of the ordinary production sector. It can be seen that

the production sector (output) uses inputs which include knowledge and other physical

inputs (KLEM), the other physical inputs include intermediate outputs obtained from the

other production sectors (Xij) and capital-labor-energy inputs (KLE), these capital-labor-

energy inputs include energy inputs and value-added factors, which include labor and

capital. The energy input is formed by fossil energy and electricity. Domestic production

(XDj) combined with imports (Mj) results in total production (Xj) through a specific

function usually conforming to Armington’s hypothesis which assumes that the analyzed

economy is small enough to have no significant effect on foreign trade (André et al. 2010;

Armington 1969).

Fig. 2 A sample of nested production function

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3.1.2 Government module

When undertaking a study focused on policy making, hypotheses on how the government

makes such decisions should be considered (Armington 1969; Liang et al. 2014).

Government income is constitutive of various taxes and transfers from other countries/

regions, whereas government expenditure includes government consumption, transfers to

households and enterprises, as well as export rebate. While the government tax department

collects tax revenues and influences the disposable income of consumers, it also makes

transfers to the private and production sectors. In any given period, the difference between

government income and expenditure forms government savings. In this model, the gov-

ernment taxation and expenditure is implemented through economic agents as exogenous

and by the government as decision variables.

3.1.3 Household module

There are n types of commodities produced by the production sectors, with each consumer

holding a set of preferences. Household income is mainly derived from labor income and

the profit distribution from enterprises. After paying income tax and receiving transfers

from government and overseas, households are left with their disposable income. Typi-

cally, a portion is preserved for savings, while the rest is used for the purchasing various

goods. The household’s objective is to achieve utility function (expressed as U) maxi-

mization, which is limited by budget constraints formed by consumer commodities HCj and savings HS:

Max U HC1; . . .; HCn; HSð Þ s:t:

Pn

j¼1 PjHCj þ PI � HS ¼ Disposable income ð1Þ

where n is the number of available commodities, Pj represents the price of commodity j,

and PI denotes the price of investment commodities. Market demands are the result of each

household’s demands.

3.1.4 Investment module

The total investment in the model includes R&D investment, as well as physical capital

investment. As shown in Fig. 3, the physical capital investment goods form the physical

Fig. 3 A sample of the investment structure

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capital investment within the CES structure, while the R&D investment goods constitute

the R&D investment with the CES structure.

3.1.5 Foreign module

In this module, the rest of the world is incorporated by the inclusion of a single foreign

sector account. Given that we primarily focus on domestic issues, the foreign sector is not a

key concern. Hence, according to Armington’s assumption, there is imperfect substi-

tutability between domestic output sold domestically and imports (Armington 1969).

While the commodities supplied domestically consist of both domestic and imported

commodities, the domestic output too is used for both domestic demand and export. The

foreign sector balance takes the form of Eq. (2):

Foreign balance ¼ Imports � Exports � Net transfers ð2Þ

3.1.6 Market clearing

Market clearing represents the equilibrium conditions, whereas only commodity and

capital markets are cleared in this model. The clearance of commodity market means that

the supply of a commodity equals its demand. Capital market could also achieve fully

sufficient adjustment, and the capital supply is set exogenously. Labor market is normally a

difficult factor to deal with since unemployment means an excess supply of labor, which is

inconsistent with the equilibrium assumption. Thus, we assume that full employment exists

in the labor market.

3.1.7 Environmental module

To simulate the carbon market, we assume that primary allowances could be distributed to

producers and consumers, who emit carbon dioxide, either for free or through auction. By

permitting the allowances to be traded in the allowance trade market, the carbon price is

then determined by the demand for allowances and the supply of carbon permits. This is

shown as follows:

Tot CO2 ¼ X

cpg

ncpgð X

i

Xcpg;iþ X

jm

Xpcpg;jmÞ ð3Þ

where Tot CO2 is the quantity of carbon permits supplied to the carbon market; ncpg is the emission factor for primary energy cpg; Xcpg,i is the quantity of primary energy cpg

consumed in sector i; Xpcpg,jm is the quantity of primary energy cpg consumed by resident

jm.

So the price of carbon is decided endogenously under a given reduction target. The ad

valorem tax rate for production sectors and households are calculated as:

CTpcpg ¼ Cprice � ncpg �

P

i

Xcpg;i P

i

ðXcpg;i � PQcpgÞ ð4Þ

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CThcpg ¼ Cprice � ncpg �

P

jm

Xpcpg;jm

P

jm

ðXpcpg;jm � PQcpgÞ ð5Þ

where CTpcpg is ad valorem tax rate levied on primary fossil energy cpg in sector I; CThcpg is ad valorem tax rate levied on primary fossil energy cpg consumed by residents; Cprice is

the price of carbon in the market; and PQcpg is the price of goods in the market.

The primary energy demands of producers and households are modified as:

Xcpg;i ¼ bcpg;i � Pfossili

ð1 þ CTpcpgÞ � PQcpg � Fossili ð6Þ

Xpcpg;jm ¼ clescpg;jm � ð1 � mpsjmÞ � YDjm

ð1 þ CTpcpgÞ � PQcpg ð7Þ

where Pfossili is the price of fossil energy input composite in sector i; Fossili is fossil energy

input composite in sector i; bcpg,i is the share parameters for the primary energy in fossil energy composite in sector i; clesi,jm is the marginal share for consumption spending on

home commodity i by household jm; mpsjm is the marginal saving rate for household jm;

and YDjm is the income of household jm.

When solving the model, the relative prices and productive levels are set as endogenous

variables. The zero-degree homogeneity of the demand functions and the linear homo-

geneity of profits, related to the prices, make only relative prices significant. In addition, it

is evident that there is not just one general equilibrium model, which depends on how

elasticities are used in the model (Armington 1969).

3.1.8 Dynamic mechanism

The recursive dynamic mechanism is adopted in this paper. The model is pushed forward

through capital accumulation, population growth and improvement in total factor pro-

ductivity. The process of capital accumulation is shown as:

SKtþ1 ¼ SKt þ INVt � dSKt ð8Þ

where SKt is stock of capital in period t; INVt is new fixed investment in period t; and d is the depreciation rate.

3.2 Definition of scenarios

This study examines future technological progress when applying CO2 emission reduction

targets and a national carbon market in China, over the period 2015–2030. The co-benefits

on the economic growth of the country from carbon emission constraints are also analyzed.

The following scenarios are considered.

3.2.1 Baseline scenario

When applying the CGE model, a baseline scenario is first constructed, wherein no

environmental policy takes place. Then results obtained from other policy scenarios are

expressed in percentages deviating from the baseline values. When generating the baseline

scenario for this paper, we first set the business as usual (BAU) of real GDP, population

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and urbanization rates based on the forecasts of the existing literature (Liang et al. 2014).

Technological progress is assumed to be neutral. In addition, we assume that government

consumption has increased at a rate of 5 % per year and transfers among institutions were

fixed at their base year levels in the baseline scenario (Liang et al. 2014). Based on the

above assumptions, a baseline calibration model is run endogenously to generate the

carbon intensity and carbon emissions, as shown in Table 2. The calculated baseline

carbon intensity and carbon emissions are then fixed in further simulations.

3.2.2 Assumptive scenarios

This paper sets three CO2 emission reduction scenarios for the above-mentioned sectors, as

follows:

1. Low CO2 emission reduction scenario (SL) By reducing carbon emissions by 3 % per

year, compared with the baseline scenario, the low CO2 emission reduction scenario

evaluates the impact of CO2 emission reduction in the entire production sectors. It is

the ‘what if’ scenario, in which annual CO2 emission reduction is not less than 3 % of

the carbon emissions during the planning horizon in the baseline scenario and all other

variables are derived through running the scenario model.

2. Mid-CO2 emission reduction scenario (SM) By reducing carbon emissions by 10 % a

year, compared with the baseline scenario, all other variables are derived through

running the scenario model.

3. High CO2 emission reduction scenario (SH) By reducing carbon emissions by 20 % a

year, compared with the baseline scenario, all other variables are derived through

running the scenario model.

In addition, according to the realistic conditions of China’s carbon trading pilots, the

carbon allowances are distributed freely based on historical emissions.

3.3 Data sources

The CGE model uses the SAM, which is known as the snapshot of the national economy of

a country within a certain period of time and describes in detail the economic performance

of that period, as its database. As the 2007 input–output table is the latest data available, a

2007 Chinese SAM table is formulated in this paper based on the 2007 input–output data

(Department of National Account 2009). Parameters are divided into exogenous and

Table 2 Major assumptions for baseline scenario

Year GDP (%) Population (billion) Urbanization (%) Carbon intensity compared with 2005 (%)

Carbon emissions (billion)

BAU BAU BAU BAU Targets BAU

2015 7.5 1.38 55 -28.2 2.46

2020 7 1.41 58 -39.3 40–50 2.92

2025 6 1.43 61 -48.6 3.31

2030 5.5 1.44 64 -57.1 60–65 3.61

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endogenous ones. Endogenous parameters are determined through calibration: Data in the

SAM table are substituted into the functions of the model as balancing the base year

equilibrium data. The table of parameters can then be obtained through solving the model.

Exogenous parameters of the model include different kinds of substitution elasticity,

carbon emission factors, carbon oxidization rates, matrix of fixed capital formation, etc.

(Glomsrod and Wei 2005; IPCC 2006; Wang 2003; Xue 1998).

4 Results and discussion

4.1 The impact on technological progress and carbon price

Figure 4 and Table 3 illustrate the impact on technological progress and carbon price

under the low CO2 emission reduction (scenario SL), mid-CO2 emission reduction (sce-

nario SM) and high CO2 emission reduction (scenario SH) scenarios. The results show that:

(1) the rates of change of technological progress, relative to the baseline scenario, continue

to increase and reach 0.47, 1.60 and 3.35 % in 2030, respectively; (2) carbon prices

fluctuate in the narrow ranges with the average values of 57.86 Chinese yuan (CYN) per

ton, 203.17 CYN per ton and 452.87 CYN per ton, respectively. In this study, the carbon

trading is assumed to be implemented in a perfectly competitive market. Hence, the carbon

price is decided by the marginal reduction cost of the industry. Results show that all CO2 emission reduction policy scenarios are able to effectively promote technological progress.

However, scenario SH performs best in improving technology over the entire period

analyzed. This is mainly because the carbon market influences various economic agents,

primarily through high carbon prices, which can lead to a greater increase in investment in

low-carbon technologies and therefore a greater decrease in energy consumption.

The carbon market operates by first setting a ‘cap,’ or limit, on the amount of carbon

emissions that regulated enterprises are allowed to emit. The most important factor

affecting technological progress is likely to be the stringency of the cap over time. The

‘carbon allowance’ is created for each ton of capped emissions, and these allowances are

distributed to enterprises. Trading gives enterprises covered by the regulation the flexibility

to either reduce their own emissions or buy allowances from another enterprise, which

Fig. 4 Changes in technological progress in different policy scenarios

Nat Hazards (2016) 81:1107–1128 1119

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provides an incentive for enterprises with the lowest marginal cost of abatement to make

the cheapest reductions first. Industry and consumers then consider the carbon price as a

signal and factor it into their decision making. Drastic reduction targets, which would

probably lead to a high carbon price, therefore create the incentive to reduce emissions and

to invest in low-carbon technologies, which is much more effective than less stringent

targets. In addition, in the long term, relying on the purchase of carbon emission allow-

ances will restrict the company’s long-term development. Therefore, after weighing the

advantages and disadvantages between investing in low-carbon technologies and buying

carbon emissions allowances, enterprises tend to choose the former. Technological pro-

gress enables companies to reduce carbon emissions, then allowing the excess quotas to be

sold on the market which in turn alleviates the cost pressures from the investment in low-

carbon technologies.

Figure 4 also shows that the impact of the carbon market on technological progress will

gradually lessen rather than growing with time in all three abatement scenarios. Moreover,

the rate of change of technological progress is greater in scenario SH than that in the other

two scenarios and slows down after 2025. This is due to enterprises taking measures to

reduce carbon emissions within the confines of all three abatement targets, but as time goes

on, all economic agents gradually adjust their production behavior and consumer behavior

to adapt to the CO2 emission reduction policies, ultimately leading to a fading policy

stimulus. However, compared with scenario SL and scenario SM, the mitigation efforts

under SH take place over a longer period due to the formidable CO2 emission reduction

task and target pressure. In addition, from Table 3, it can also be seen that carbon prices

remain at relatively stable levels in the three CO2 emission reduction scenarios, which are

mainly determined by the special social development phase and the special economic

situation of China. Although China has invested in a lot of new energy technologies, it still

remains very dependent on coal and is likely to remain so for quite some time to come.

Hence, the carbon-intensive development model cannot be changed in the short term, nor

Table 3 Changes in carbon price under different policy sce- narios (CYN per ton)

SL SM SH

2015 66.3 215.6 448.5

2016 64.6 212.5 447.8

2017 62.3 208.3 445.2

2018 60.4 204.7 443.3

2019 58.4 201.4 441.9

2020 57.1 199.0 441.7

2021 55.9 197.2 441.8

2022 55.2 196.3 443.2

2023 54.8 196.4 445.9

2024 54.7 197.2 449.5

2025 54.9 198.6 453.9

2026 55.1 200.2 458.4

2027 55.6 202.3 463.4

2028 56.2 204.6 468.6

2029 56.8 207.0 473.8

2030 57.5 209.4 479.0

1120 Nat Hazards (2016) 81:1107–1128

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should it be changed in haste, which means that China has great potential to exploit the

carbon market and its prospects are large.

4.2 The impact on carbon intensity and carbon emissions

Figures 5 and 6 show the impact on carbon emissions and carbon intensity under the low

CO2 emission reduction (scenario SL), mid-CO2 emission reduction (scenario SM) and

high CO2 emission reduction (scenario SH) scenarios. Results show that: (1) carbon

emissions reach 3.51 billion, 3.25 billion and 2.89 billion, respectively; and (2) carbon

intensity drops by 41.11, 45.36 and 51.37 %, respectively, in 2020, as well as 58.37, 61.38

and 65.66 %, respectively, in 2030, when compared to 2005 levels. It can be seen that all

CO2 emission reduction policy scenarios are able to effectively reduce carbon intensity and

limit the growth rate of carbon emissions over time. However, scenario SH performs best

in mitigation efforts over the whole period analyzed. This is mainly because the carbon

market influences technological progress greatly in scenario SH (Fig. 4) and this can lead

to a sharp decline in energy consumption and advances in energy efficiency. In addition,

the impact of CO2 emission reduction in scenario SL is almost similar to that of the BAU

scenario, which reduces carbon emissions without the carbon market. Hence, the high

performance of the carbon market can only be achieved through reasonable CO2 emission

reduction targets; otherwise, it is essentially null and void when compared to the other

carbon reduction policies.

As the indicator for carbon dioxide emissions and GDP, carbon intensity has become a

restrictive indicator in China’s CO2 emission reduction campaign. This is mainly because,

first, China is at a key stage of rapid industrialization and urbanization with considerable

energy consumption and environmental impacts, with industry playing the central role in

industrial development; and second, in China, the largest developing country, per capita

emissions are much lower than in developed countries. Therefore, it is impossible for

China to set limits on the absolute amount of carbon dioxide emissions. From Fig. 6, it can

be seen that the carbon market will help to implement the commitment of CO2 emission

reduction targets of pre-2020 and post-2020, except for the SL scenario in 2030. Hence, it

can be assumed that the effectiveness of the carbon market is based on reasonable policy

scenarios. In the era of the knowledge-based economy, energy-saving emission reduction

Fig. 5 Changes in carbon emissions in different policy scenarios

Nat Hazards (2016) 81:1107–1128 1121

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efforts, in the final analysis, depend on the progress of science and technology. Figure 4

shows the strong positive effects on the carbon market for technological progress in

scenario SH, and therefore carbon intensity and emissions are considerably lower than

those in scenarios SL and SM. Accordingly, we can come to the conclusion that the carbon

market is conducive to cutting carbon emissions and achieving carbon reduction pledges,

while unreasonable policy scenarios would make it irrelevant.

4.3 The impact on economic growth

The carbon market can make both technological progress and CO2 emission reduction

more efficient, but various CO2 emission reduction targets would have different influences

on economic growth. Figure 7 shows that, in the early stages, GDP drops by 0.02, 0.12 and

0.37 %, respectively; however, these negative effects gradually weaken during that period.

It is noticeable that GDP is ultimately higher than the baseline scenario by 0.02, 0.05 and

0.015 %, respectively. The ‘Annual Report on Actions to Address Climate Change’ (Wang

and Zheng 2010), which is issued by the Chinese Academy of Social Sciences, evaluated

Fig. 6 Changes in carbon intensity in different policy scenarios

Fig. 7 Changes in GDP in different policy scenarios

1122 Nat Hazards (2016) 81:1107–1128

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the influence of reducing carbon intensity on economic growth. The results show that the

decline in GDP is less than 1 %, based on the CO2 emission reduction targets of cutting

carbon intensity in 2020 by 40–45 % compared with that of 2005 and the premise of

economic growth of 8–9 %. This paper readjusts the economic growth target, which is less

than 8 %, on the basis of the actual situation in China, and the results are basically

consistent with that of the ‘Report on Actions to Address Climate Change’ (Wang and

Zheng 2010).

Though the more stringent the mitigation scenario, the greater loss the GDP will suffer

in the early stages of the carbon market, it is particularly noticeable that the GDP in

scenario SM will exceed that in scenario SL by 2022. This is mainly because those

stringent CO2 emission reduction targets would unfairly compromise the country’s eco-

nomic growth in the early stages, whereas they would play a highly positive role in

promoting technological progress, which, in turn, accelerates economic growth in the later

period. While these are indeed considerable sums, such expenditures are not simply a drag

on the overall economy. In fact, investment and private consumption are the major

components of GDP; therefore, while some of the spending required to transition toward a

low-carbon economy may have to be redirected from other uses, such investments would

still contribute to overall economic activity. Arthur (1998) attributes this to the tech-

nologies taking some time to stimulate economic growth, with the technological effects

demanding an effort in four aspects: (1) huge capital investment: with the increase in

production, there needs to be increased efficiencies and economies of scale, resulting in

lower unit production costs, (2) learning effect: as a result of the impact of learning

efficiency and the popularity of technologies, product costs will reduce gradually, (3)

coordination effects: major participants are able to share profits through collaboration, (4)

adaptive expectations: technology diffusion relies on rules that are widely followed by as

many people as possible. Hence, the GDP of scenario SM will exceed that of scenario SL

due to the front-period incentive on technological progress, technology diffusion and

technology accumulation.

Fig. 8 Changes of major indicators in energy-intensive sectors (2030)

Nat Hazards (2016) 81:1107–1128 1123

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It can be seen from Fig. 8 that the implementation of schemes SL, SM and SH will all

incur the reduction in sectoral output and export, while increasing the sectoral production

price in several energy-intensive sectors, i.e., the coal, electricity, gas production, petrol

and oil industries, which are all high CO2 emission industries. The negative impact of

scheme SH on these sectors is clearly greater than those of schemes SL and SM. Due to the

diversity of industrial fuel structures, the CO2 emission reduction results and the induced

output by the carbon emissions trading in various industrial sectors are fundamentally

different. It is noticeable that the coal and electricity industrial sectors suffer from a greater

loss in output and export, more than other sectors, which is mainly because oil and natural

gas are less carbon intensive. Hence, carbon trading leads to a greater increase in the usage

cost for the coal and electricity industries and in turn leads to a greater decrease in their

consumption, thereby impacting their production and export. As energy is a strategic

resource for national economic development, occupying an important position in both

regional and international trade, any fall in the production of energy-intensive industries

would ultimately result in a rise in various energy prices; this is particularly likely in the

coke industries, due to an output cut in coal industry.

In addition, it is evident that R&D investments, relative to the baseline scenario,

increases in all scenarios SL, SM and SH, respectively (Table 4), indicating that the carbon

market could effectively encourage enterprises to innovate. By moving away from China’s

long held mode of authoritarian environmentalism, industries have the potential to improve

their ability to respond to environmental regulations. However, it is also noticeable that the

growth rates in scenarios SL and SM present a downward trend, and this implies that firms

need not only stable long-term incentives in order to invest but stringent but achievable

emission reduction targets. Previously, environmental policies related to emission reduc-

tion were mainly implemented through the CAC manner, in which regulated enterprises

were required to strictly comply with emissions standards or implement specified tech-

nologies. These administrative measures have produced significant impacts; however, they

also act against healthy economic development, as well as against the mid-term CO2

Table 4 Changes in technical investment in different policy scenarios (%)

SL SM SH

2015 0.64 1.93 3.60

2016 0.63 1.91 3.62

2017 0.60 1.87 3.60

2018 0.58 1.83 3.58

2019 0.56 1.79 3.55

2020 0.54 1.75 3.53

2021 0.52 1.72 3.52

2022 0.51 1.70 3.51

2023 0.50 1.68 3.51

2024 0.49 1.67 3.52

2025 0.49 1.67 3.53

2026 0.49 1.67 3.55

2027 0.48 1.67 3.57

2028 0.48 1.68 3.59

2029 0.48 1.68 3.61

2030 0.49 1.69 3.63

1124 Nat Hazards (2016) 81:1107–1128

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emission reduction targets in the long term. This is mainly because social resources are far

from being fully utilized, particularly private capital and finance capital. However, under

the emission cap-and-trade system, market fundamentals have been markedly changed and

CO2 has become a tradable good. Enterprises see this price signal and factor it into their

decision making, which creates the incentive to reduce emissions and to invest in low-

carbon technologies. Thus, the carbon market provides new commercial opportunities for

those enterprises with surplus emission allowances.

The goal of a market-based climate policy is to spur the ingenuity of the industrial

sector to develop and deploy emission-reducing technologies. It is not unreasonable to

expect unanticipated cost savings associated with technology innovation from climate

policies. The results have revealed that the carbon market enables CO2 emission reduction

achievements and plays a positive role in economic growth through promoting techno-

logical progress in the long term. However, CO2 emission reduction targets require careful

consideration in the early stages, which should take into account both the current economic

slowdown and the long-term economic growth brought on by advanced technologies. As

China is now in a crucial stage of rapid industrialization and urbanization, the carbon

market must realize this double dividend from both the short term and long term combined.

Hence, while cap-and-trade system would likely be the centerpiece of climate legislation,

complementary policies are expected to be enacted to alleviate any negative effects.

5 Conclusion and policy implications

The likely centerpiece of China’s legislation to address emission reduction will be a cap-

and-trade program that creates a market for CO2 emissions. Whether the onset of a national

carbon market will achieve the pre-2020 and post-2020 mitigation targets, or indeed

provide additional benefits or costs to the Chinese economy, has been the subject of many

modeling studies and debates. Using a multi-sector recursive dynamic CGE model, con-

taining endogenous technological progress, this paper explores the impact of the carbon

market, which sets certain CO2 emission reduction targets, on technological progress,

emission reduction and economic growth. It can be seen that the carbon market enables

achievements in CO2 emission reduction targets through technological progress; however,

it is based on the reasonable mitigation scenario and the resulting price signal is critical to

induce the technological innovation and changes in corporate behavior necessary to reduce

emissions. A more stringent cap will lead to higher allowance prices and a higher market

value, which increases the costs of the program, while also creating the necessary incentive

for innovation. Over the course of time, the effects of technological advancements tend to

eventually reach a stable level. In addition, there is compatibility between carbon intensity

reduction and mid- and long-term GDP growth; however, it seems impossible to realize the

positive effects on both emission reduction and economic development in the early stages.

This is mainly because that the negative functions will gradually diminish over time and

will become increasingly positive at the later stages, due to the front-period incentives on

technological progress.

Hence, the costs of environmental policy can be mitigated by appropriate program

design and other auxiliary policies, which at most create a small drag on the economy. The

negative effects and associated costs of climate change on China, as a whole, must also be

acknowledged; the financial impacts might very well exceed any reductions in economic

growth. This means that China will not be able to effectively minimize carbon emissions or

Nat Hazards (2016) 81:1107–1128 1125

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sufficiently adapt to the ever-increasing damages associated with climate change if action

is not taken immediately. Therefore, the necessary costs associated with climate policies

are not only reasonable but highly affordable compared with the future highly unpre-

dictable economic losses which may result from climate change. In China, the future

centerpiece of mitigation policy would likely be a cap-and-trade program that places a

price on carbon emissions and creates a market that provides a large incentive for inno-

vation in low-carbon technologies. Considering China is in a high-speed transitional period

and the economic and social development depending on long-term stability, this mar-

ket alone will not be a cost-effective instrument for carbon emission abatement, and

complementary policies will also be needed in the early stages. Incentives in the form of

targeted allocations to mitigate cost impacts, carbon taxes and funding for research and

development are also likely to be included in a comprehensive climate policy.

Our study also has some limitations. As the economic models are not able to capture the

full resilience and innovation potential of the economy, the actual cost in the early years of

the market may be just a fraction of the predicted amount. While climate change is a larger

problem that encompasses virtually the entire economy, market-based instrument can seek

out low-cost reduction opportunities and incentivize technological change together with

complementary policies. Economic models are better used to provide insights rather than

absolute numbers, and these insights are generally considered to be more robust when they

are seen consistently from model to model. Perhaps the impact of combining carbon taxes

and emission trading on different industrial sectors could be discussed in more detail in

future research and development.

Acknowledgments This work was supported by a Grant from the National Natural Science Foundation of China (Nos. 71203008 and 70973011) and Funding Project of Education Ministry of China for the Development of Liberal Arts and Social Sciences (No. 11YJA630119). The authors would like to extend special thanks to the editor and the anonymous reviewers for their constructive comments and suggestions for improving the quality of this article.

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