The Role of Carbon Taxes and Revenue Use for China’s Unilateral CO2 Intensity

and Non-fossil Fuel Pledges for 2020

Shenglv Zhoua,b Minjun Shia,b,1 Joachim Schleichc,d,e, Na Lia,b

a Graduate University of Chinese Academy of Sciences, No.19 A, Yuquan Road, Shijingshan District, Beijing 100049, Chinab Research Center on Fictitious Economy and Date Science, Chinese Academy of Sciences, No. 80, Zhongguancun East Road, Haidian District, Beijing 100190, Chinac Fraunhofer Institute for Systems and Innovation Research, Breslauer Strasse 48, 76139 Karlsruhe, Germany d Grenoble Ecole de Management, 12, rue Pierre Sémard, BP 127, 38003 Grenoble Cedex 01, France e Department of Agricultural and Applied Economics, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA

Abstract: This paper assesses the economic and environmental effects of fulfilling

China’s unilateral CO2-intensity and non-fossil fuel pledges for 2020. A dynamic energy-

environment-economic CGE model for China, which specifically includes detailed

information on electricity technology, is employed to simulate the effects of a carbon tax

of 40 RMB/t CO2 under four different assumptions about the tax revenue use:

government budget, households' disposable income, reduction of output tax in selected

industrial sectors, and investment in non-fossil electricity generation. The results suggest

that tax revenues earmarked for additional investments in non-fossil power technologies

is an efficient policy not only for the realization of CO2-intensity target, but to support

the development of non-fossil fuel.

Keywords: dynamic energy-environment-economic CGE model, CO2-intensity target,

non-fossil fuel target, carbon tax, China.

The Role of Carbon Taxes and Revenue Use for China’s Unilateral CO2 Intensity

1 Corresponding author at: Research Center on Fictitious Economy and Date Science, Chinese Academy of Sciences, No. 80, Zhongguancun East Road, Haidian District, Beijing 100190, China. Tel: +86 10 82680911. Email: mjshi1964@gmail.com.

and Non-fossil Fuel Pledges for 2020

Abstract: This paper assesses the economic and environmental effects of fulfilling

China’s unilateral CO2-intensity and non-fossil fuel pledges for 2020. A dynamic energy-

environment-economic CGE model for China, which specifically includes detailed

information on electricity technology, is employed to simulate the effects of a carbon tax

of 40 RMB/t CO2 under four different assumptions about the tax revenue use:

government budget, households' disposable income, reduction of output tax in selected

industrial sectors, and investment in non-fossil electricity generation. The results suggest

that tax revenues earmarked for additional investments in non-fossil power technologies

is an efficient policy not only for the realization of CO2-intensity target, but to support

the development of non-fossil fuel.

Keywords: dynamic energy-environment-economic CGE model, CO2-intensity target,

non-fossil fuel target, carbon tax, China.

1Introduction

According to the Fourth assessment report (Gupta et al. 2007) to the Intergovernmental

Panel on Climate Change (IPCC), global CO2 emissions need to be reduced by at least

50-85% in 2050 compared to 2000 levels in order to reach the 2°C target. A range of

25%-40% emission reductions compared to 1990 for industrialized countries, and 15%-

30% below baseline in some developing regions in 2020 (Gupta et al. 2007; den Elzen

and Höhne 2008) are required as an intermediate GHG reduction target.

China, which has surpassed the U.S. as the world’s leading emitter of greenhouse gases,

is facing international and domestic pressure to contribute towards achieving global

climate targets. In response, the Chinese government has pledged to lower CO2 emissions

per unit of GDP by 40-45% by 2020, compared to the 2005 level, and to increase the

share of non-fossil energy (nuclear and renewables) in primary energy consumption to

15% by 20201. This is a voluntary unilateral pledge and – unlike the pledges by some

other countries – it does not depend on pledges made by other countries.

The Chinese pledge arouses a lot of questions to be answered. This paper firstly employ

the dynamic energy-environment-economic CGE model to examine to what extent

China’s CO2-intensity target for 2020 may be realized without additional CO2 mitigation

policies, and then assess the economic and CO2 mitigation effects of different strategies

considering not only the CO2-intensity, but also the non-fossil fuel share pledges.

Several recent studies using different types of models allow assessment of whether the

CO2-intensity target pledged by China for 2020 compared to 2005 is ambitious or just

business as usual (BAU) (see Figure 1). Accordingly, about half the simulations in the

studies considered in Figure 1 suggest that the 40% target will be business as usual.

While to some extent differences in baseline emissions stem from differences in the

models, these differences are mainly driven by differences in assumptions about the

policy measures included in the baseline. For example, the projections from the

International Energy Agency (IEA 2009) and Energy Information Administration (EIA

2009) that underlie various studies assume very significant policy actions, including

aggressive conservation and clean energy measures, e.g. the Renewable Energy

Development Plan, the Nuclear Programme, or high feed-in tariff rates for wind power

(IEA 2008; IEA 2009) that can hardly be treated as BAU trajectories (Stern and Jotzo

2010; Chandler and Wang 2009; McKibbin et al. 2010). Another main difference refers to

assumptions of key parameters capturing, for example, (autonomous) technological

change and energy efficiency in the baseline (Stern and Jotzo 2010). Energy efficiency is

assumed to increase by more than 3% per year in Wang et al. (2009), while Dai et al.

(2011) assume an annual rate of 2.5%. Analyzing historic and likely future trends in

energy and CO2-intensity for China, Zhang (2011, p. 6) concludes that China’s intensity

pledge does not just represent business as usual. While energy intensity improved by

5.25% per year during the period 1980-2000, it slowed down considerably at the

beginning of the 21st century in the wake of rapid industrialization and urbanization.

Even in 2006, the first year of the “11th five-year plan”, energy intensity only declined

by 1.79% (Zhang, 2010). In subsequent years, China made substantial efforts towards

achieving the target of reducing energy intensity by 20% during 2006-2010, and the

actual drop rate is 19.1%2. Substantial progress has been achieved, although the

economic crisis contributed to this development, since export demand for energy-

intensive commodities was substantially lower in 2008 and 2009. Thus, as Carraro and

Tavoni (2010) point out, it may not be adequate to project historic rates of energy

efficiency improvements into the future.

20%

25%

30%

35%

40%

45%

50%

55%

60%Carraro & Massetti(2011)

EIA(2009)

Wang et al.(2009)

Jiang et al.(2009)

IEA(2009)

Dai et al.(2011)[b]

Stern & Jotzo(2010)[c]

Stern & Jotzo(2010)[b]

Peterson et al.(2011)

UNDP & Renmin University of China(2010)

Dai et al.(2011)[a]

McKibbin et al.(2010)

Stern & Jotzo(2010)[a]

Figure 1 CO2-intensity reductions for China in 2020 compared to 2005 in the baselineNotes: Dai et al.(2011)[a],[b] represent the reference scenarios with the non-fossil fuel share unchanged from 2005 [a], and the non-fossil fuel share increase as development target [b], respectively; Stern & Jotzo(2010)[a],[b],[c] represent baseline scenarios with the forward growth rate for technological change converge to US in underlying energy efficiency [a], equal to regression estimate of the mean rate of growth over the 2000-2007 period [b], and 1971-2007 period [c], respectively.

Prior to making these pledges, China had already announced significant voluntary efforts

to reduce CO2 emissions. The Chinese government has also recognized that relying more

on economic instruments may be an efficient strategy to incentivize energy saving and

CO2 mitigation. Market mechanisms such as carbon taxes or CO2 emissions trading

systems could be introduced. Notably, a research team from the National Development &

Reform Commission of China and the Ministry of Finance published a report titled

"China Carbon Tax Framework Design" in 20103. Zhou (2011) estimated the contribution

of a carbon tax of 30, 60, and 90 RMB/t CO2 to CO2-intensity pledge in 2020. CO2

emissions trading are also considered in China’s Twelfth Five-Year Plan, and China put

forward Guangdong, Hubei, Beijing, Tianjin, Shanghai, Chongqing and Shenzhen as

carbon emission rights trading pilot areas. Liu (2010) have set the cross-sectoral, inter-

regional and cross-time carbon trade scenario aiming to the CO2-intensity target for 2020,

and the results illustrate that the market trade based on CO2-intensity index can bring us

economic, environmental and social benefits, and facilitate the reduction of carbon

emissions and CO2-intensity, simultaneously reduce carbon emission reduction costs and

expenses.

Regarding to the costs of meeting China’s 2020 CO2-intensity target, the results from

UNDP and Renmin University of China (2010) shows that CO2-intensity reductions by

45% in 2020 compared to 2005 requires China to invest an additional $ 30 billion per

year. Han and Liu (2010) calculate that 40% and 45% CO2-intensity cuts imply $ 10.4

and $ 31.8 billion incremental cost per year, respectively. Dai et al. (2011) estimate that

the CO2 mitigation costs of reaching the 40%-45% target result in GDP losses of 0.032%-

0.24% compared to the reference scenario with a non-fossil fuel share increase as

development target.

The previous researches which focus on China’s 2020 CO2-intensity target are mainly

discuss whether the target is ambitious or business as usual. The assessment of policies

aiming at China’s CO2-intensity target in 2020 needs further research. What’s more, the

existing researches concerning the cost of the 2020 target realization seldom compares

the differences among policies.

It should be noticed that China’s 2020 pledges are not only related to the CO2-intensity,

but also the non-fossil fuel development. China’s energy mix is dominated by coal, and

non-fossil fuel accounted for only 7.8% in total energy consumption in 2009 (China

Energy Statistical Yearbook 2010). Structural change in energy consumption plays an

important role in the decline of CO2-intensity target（Li et al. 2010; Dai 2011; Wang et

al. 2011). The Chinese government also made great efforts toward the development of

non-fossil fuel. It had announced the ‘Medium- and Long-term Development Plan for

Renewable Energy’4 and ‘Medium- and Long-term Development Plan for Nuclear

Energy5’ concerning the development of non-fossil energy in 2020. However, the

capacity target was updated with the rapid development of non-fossil energy, i.e. the goal

of installed capacity of wind is 30 million kW in 2020 in ‘Medium- and Long-term

Development Plan for Renewable Energy’, but the new target of wind installed capacity

should be reach 100 million kW at the end of 20156. The new target for nuclear in 2020 is

nearly doubled compared to the target of ‘Medium- and Long-term Development Plan for

Nuclear Energy’. What’s more, the bottom-up models are also applied to forecast the

future development of different non-fossil fuels (IEA 2010; Jiang et al. 2009).

The required accumulated investments from 2005 to 2020 are 2 trillion RMB for

renewable energy (Medium- and Long-term Development Plan for Renewable Energy),

and 0.45 trillion RMB for nuclear (Medium- and Long-term Development Plan for

Nuclear Energy). IEA (2011) also estimated the required funding for non fossil fuel to

2035. However, how to collect the funding is limited in qualitative analysis, for example,

to establish the carbon tax or carbon trading foundation.

The first contribution of this paper is to compare the economic and CO2 mitigation

effects of different carbon tax revenues use, aiming at not only the 2020 CO2-intensity

target, but also the non-fossil fuel target. The second contribution is to make a

quantitative analysis for using carbon tax revenue earmarked for funding in non-fossil

power technologies. To do so, a dynamic energy-environment-economic CGE model for

China, which specifically includes detailed information on electricity technologies, is

employed in this paper. This paper concentrates on assessing the carbon tax policy rather

than carbon emission trading scheme for the reason that carbon tax is easier to implement

and covers wider sectors. More importantly, the revenues of carbon tax are collected by

government, while the trading incomes from carbon emission trading mainly belong to

enterprises. Therefore, carbon tax policy is easier for government to collect the money,

and then invest on the development of non-fossil fuel.

The remainder of the paper is organized as follows: Section 2 introduces the

methodology and data sources. The baseline and policy scenarios are described in detail

in Section 3. Section 4 includes the simulation results. Section 5 examines the sensitivity

of the findings with respect to key parameters embedded in the model. The main

conclusions are presented in Section 6.

2 Methodology and data

2.1 The dynamic energy-environment-economic CGE model for China

Over the last two decades, CGE models have become quite popular for simulating the

macroeconomic effects of climate policies. The CGE model used for the policy analyses

in this paper will be described in more detail in the remainder of the section.

2.1.1 Production structure

The dynamic energy-environment-economic CGE model for China used in this paper

covers 38 sectors, including one agricultural sector, 35 industrial sectors and two service

sectors, two sets of household and three production factors (labor, capital and energy).

The factor energy is composed of eight energy types, that is, coal, oil, natural gas, oil

refined products, coke, fuel gas, electricity and heat.

The basic model is based on the structure of Lofgren et al. (2002) and energy is

embedded into the production module. Since the elasticity of substitution varies among

different inputs, the production is described by a multi-level nested structure.

Intermediate commodities are entered into the model with the Leontief structure to reflect

the assumption of no substitution among different intermediate commodities, as well as

between the intermediate commodities bundle and factors bundle, which consists of an

energy bundle and a capital-labor bundle. Energy is entered into the model with a multi-

level nested structure for the eight types of energy. The constant elasticity of substitution

(CES) function is adopted to describe the substitution relationship among these types.

The main production structure is presented in Figure 2.

CES

Leontief

CES

capital-labor mix

capital labor CES

CES

fossil fuel

refined oilcoal oil fuel gascokenatural gas

CES

heat electricity mix

heat electricity

energy mix

output

labor-capital-energy mix

intermediate input

Leontief

non-energy intermediate commodity 1non-energy intermediate commodity n… …

Figure 2 Structure of production module in dynamic CGE model

In this dynamic model, economic development over time is realized via capital

accumulation, labor growth and technological improvement. The accumulation of capital

over time in each sector accrues by adding net investment to the capital stock in each

time period. It is assumed that the proportion of effective labor force to total population

is constant in the simulation period. With static expectations (myopic foresight), the

model can be solved recursively.

2.1.2 Other key assumptions

Economic growth is driven by endogenous investment, consumption and net export.

Investment is determined by savings which come from households, government,

enterprises and abroad. Household income is used for tax payment, consumption and

saving, while government income is expended for transfer payment, subsidies,

consumption and saving. Household and government consumption are co-determined by

income and consumption preference and described by the extended linear expenditure

system. The Armington assumption is used to distinguish identical domestic goods and

imported (exported) goods. World price is exogenous and China is treated as price-taker

in all markets. To close the model, we further assume that the tax rates levied by

government are exogenous whereas government saving is endogenous. Also, the

exchange rate is endogenous while foreign savings are exogenous and determined by

balancing out the international market. Finally, investment is driven by savings.

It is further assumed that all sectors exhibit constant returns to scale. Since CO2 accounts

for almost 80% of all greenhouse gases, and since 90% of CO2 emissions in China

originate from fossil fuel, this paper only considers energy-related CO2 emission and

reflects the 2020 CO2-intensity target in proportion. As the Chinese climate policy pledge

for 2020 is a voluntary unilateral pledge and does not depend on pledges made by other

countries, this paper does not cover the policies of the rest of the world.

2.2 Integrating electricity technologies into the CGE model

China’s energy structure is dominated by coal and is characterized by a low energy and

carbon efficiency (Table 1). More specifically, fossil fuels currently account for 92.7% of

total final energy use, 89.5% of CO2 emissions, and 83.2% of electricity generation in

2007 in China7. Hence, promoting the use of non-fossil energy and of advanced

technology is expected to have great potentials in terms of mitigating CO2 emissions.

However, a general CGE model does not include technology-specific, but rather

aggregate information on energy technologies. To better reflect technology-specific

aspects (e.g. in response to climate policy), the electricity sector is integrated into the

CGE model following the methodology developed by Sue Wing (2006) for the US

economy and by Dai et al. (2011) for China.

The electricity sector is disaggregated and divided into the following eight technologies:

coal, oil, natural gas based electricity technologies, and hydro, nuclear, wind, biomass

and solar-PV. Output in all other sectors except the electricity sector is directly

determined by a fixed coefficient aggregation of non-energy intermediate commodities,

capital, labor and energy commodities (Figure 3). In contrast, the different technologies

in the electricity sector production differ in their use of primary factors and of

intermediate commodities (Figure 4). The electricity outputs of each technology are

assumed to be perfect substitutes.

Disaggregating the electricity sector in the CGE model involves finding technology-

specific information which allows dividing the respective data column for the sector into

eight technologies in a consistent manner. In practice however, the engineering and the

economic may not match. In other words, the sum of each kind of input for all

technologies from engineering data is usually not consistent with the input for the electric

output

Non-energy intermediate commodities

capital-energy -labor

… …

capital-labor energy mix

coalcapital labor

power sector in the CGE model as a whole. To estimate the allocation of capital, labor,

energy and material inputs among different individual technologies that is consistent with

both the inputs shares implied by engineering cost data, and the conditions of zero profit

and market clearance, we calculate an input share matrix to obtain the disaggregated

values by minimizing the divergence of calculated share and statistical share. This

approach refers to the positive mathematical programming approach (Howitt 1995; Sue

Wing 2006).

Table 1: Energy efficiency and power generation by source in selected countries　 China Japan Germany Korea

TPESa/GDP in 2007 (in toe per thousand 2000 US$)

0.82 0.1 0.16 0.32

Gross coal consumption rate for fossil-fired power plant in 2006 (in gceb/kWh)

342c 299d 306 300

Power generation by source in 2006 (in %)

coal 81.33 26.03 48.03 38.01oil 1.46 10.58 1.52 5.91gas 4.51 24.1 12.09 18.06nuclear 1.92 27.76 26.98 36.98hydro 14.55 8.01 3.17 0.86

Source: China Energy Statistical Yearbook 2009.

Note: a, TPES stands for Total Primary Energy Supply; b, gce stands for gram coal equivalent; c, >6MW Unit; d,

Average level of 9 electricity companies.

Figure 3 Simple structure of sectors (excl. electricity sector)

… …

output

Technology 1 Technology 2… …

Non-energy intermediate commodities

capital-energy -labor

energy mix

coal

capital-labor

capital labor

Figure 4 Simple structure of electricity sector

2.3 Data

2.3.1 Basic data for the model

The basic dataset of this model is the 2007 Social Accounting Matrix (SAM) for China,

which is constructed using the 135 sectors input-output table and other data in 2007

including customs, tax, and international balance of payment, together with the flow of

funds. The 135 sectors input-output table is aggregated and disaggregated into 38 sectors.

Parameters for the substitution elasticities (Table 16) among energy, energy and capital,

energy-capital combination and labor are partly taken from Wu and Xuan (2002) and

Paltsev et al. (2005). The values for the substitution elasticities between import and

domestic commodities are taken from GTAP-68. The proportion of labor by sectors

comes from the “Fifth Population Census” in 20009 and “China Economic Census

Yearbook 2004”. The data for population and total labor, as well as fixed assets

investment are taken from the “China Statistical Yearbook 2008”.

For information on the electricity technologies, Total electricity production is 3318 TWh

(IEA 2009), and the share of each technology in the base year can be computed from IEA

(2009) and “China Energy Statistics Yearbook 2008”. Generation cost and input share

mainly refer to IEA (2005, 2007) and Paltsev et al. (2005). Table 2 shows the share of

electricity generation by technologies in physical unit and monetary unit in 2007.

Table 2: The share of electricity generation by technology in physical and monetary unit in 2007

physical unit monetary unitCoal 80.61% 70.51%Oil 1.02% 2.12%

Natural gas 1.23% 2.23%Hydro 14.95% 21.80%

Nuclear 1.92% 2.35%Wind 0.17% 0.22%

Biomass 0.05% 0.11%Solar 0.05% 0.66%

2.3.2 Data for energy consumption and CO2 emissions

Energy consumption should be treated in physical terms, but the input-output table

provides energy consumption by each agent in monetary terms. The required conversion

coefficient can be obtained by dividing the monetary terms by the physical terms (China

Energy Statistics Yearbook 2008). This conversion factor is then used in the forecast of

future physical terms of energy consumption, based on the predicted monetary terms

from the CGE model. CO2 emissions are calculated following the method recommended

by the IPCC (2006), i.e. multiplying each type of fossil fuel consumption with its CO2

emission factors and oxidation rate.

3 Scenario design

3.1 Baseline scenario

In the baseline scenario, CO2-emission reductions result from improvements in energy

efficiency, but no additional CO2 mitigation policies are assumed. Some key economic

variables are exogenous (for which actual values are used for the years 2007-2011):

The forecast of GDP from 2012 to 2020 refers to China's 12th five-year plan, as

well as the simulation by The Academic Group of Development Research Center

of State Council (2005), Wei et al. (2008) and EIA (2009). It is assumed that the

Chinese economy will maintain a moderately fast growth, but the growth rate will

decline gradually in the future because of limited resource availability and

environmental constraints (see Table 3). The assumed average annual growth rate

for the entire period 2012 to 2020 is 7.2%. The TFP is endogenous giving the

growth rate of GDP.

Table 3: GDP growth rate from 2011-2020　 2012-2013 2014-2015 2016-2017 2018-2020

GDP growth rate (%) 8.0 7.5 7.0 6.5

The commodity price index for household and government consumption refers to

historical data for the consumer price index (CPI) for China. The average annual

growth rate of the CPI for 1990 to 2011 is 2.5% (once outliers of 14.7%, 24.1%,

and 17.1% for 1993, 1994, and 1995 are eliminated). It is assumed that CPI will

not always keep this high level according to international experience and here the

growth rate for the CPI is assumed to be 3% for 2012 to 2013, and 2% for 2014 to

2020.

Energy efficiency improvements are taken from the AIM model10 provided by the

Energy Research Institute (ERI), and calibrated according to the “Medium- and

Long-term Energy Conservation Plan”11 and efficiency gap from developed

countries. The average annual rate of energy efficiency improvement is 2.1% and

hence somewhat lower than the 2.5% assumed in Dai et al. (2011). For details

refer to Shi (2010).

3.2 Policy scenarios

Using the methodology and baseline described above, economic and environmental

effects compared to baseline are analyzed in four policy scenarios with the following

features (see also Table 4).

In all policy simulations, a constant tax rate of 40 RMB/tCO212 is imposed on

fossil fuel input in domestic production for the period 2013 to 2020, coinciding

with the post-Kyoto period and the provisions of the “Bali Road Map”. This tax

rate is somewhat lower than current prices for certified emission reductions

(CERs) (around $ 14/tCO2), but deemed politically feasible, in particular

considering China’s dependence on the export market. However, this tax rate may

not necessarily lead to achieving the intensity target.

The four policy scenarios differ by the use of the carbon tax revenue.

o In the S-gov policy scenario, the tax revenue goes to the central

government and is spent for transfer payment, subsidies, consumption,

and savings according to historic shares.

o In the S-hou policy scenario, the tax revenue is transferred to private

households, increasing households’ disposable income.

o In the S-sec policy scenario, the tax revenue is used to alleviate the

negative impact of the carbon tax on the competition of energy production

and energy-intensive industries. More specifically, the government budget

is the same as in the baseline, and the output tax is reduced by the same

proportion among the following sectors when levying carbon tax:

petroleum processing, coking and nuclear fuel processing, raw chemical

materials and chemical products, non-metal mineral products, smelting

and pressing ferrous metals, smelting and pressing of non-ferrous metals,

production and supply of electric power and heat power.

o In the S-ren policy scenario, which jointly addresses the CO2-intensity

target and the target for non-fossil energy consumption, the tax revenue is

invested in non-fossil electricity generation. Revenues are spent to

promote nuclear and renewable power technologies according to the

estimated investment demands from the “Medium- and Long-term

Development Plan for Renewable Energy”13 in China.

Table 4: Policy scenarios

ScenarioTax rate

(RMB/tCO2)Carbon revenue use

S-gov 40 Government budgetS-hou 40 Household incomeS-sec 40 Reduce output tax of most impacted sectorsS-ren 40 Invest in non-fossil electricity generation

4 Results

4.1 Results for baseline

In the baseline, the real GDP will reach 72,525 billion RMB (in 2007 prices), which

corresponds to an increase in 104% compared to 2010 levels. The primary, secondary and

tertiary sectors will account for 8.0%, 48.4% and 43.6% of the GDP in 2020. The share

of energy-intensive sectors decreases slightly from 15.3% in 2007 to 14.5% in 2020.

As expected, domestic consumption plays a more important role in economic growth,

and it is predicted to account for 59.7% of GDP in 2020 compared to 48.5% in 2007.

However, the change in the exchange rate and in prices will lead to a substantial decrease

in the share of net export in GDP from 10.0% in 2007 to 1.2% in 2020.

The projection puts China’s total energy consumption at 5.04 Gtce (gigatons of coal

equivalent, Calorific value calculation), i.e. 3.53 Gtoe (gigatons of oil equivalent) in

2020, which means an increase of 63.1% from the 2010 level. Hence, while GDP almost

doubles, the energy use grows by only little over one half. The percentage of non-fossil

fuel in total energy consumption increases from 7.3% in 2007 to 10.5% in 2020 due to

reductions in per unit generation costs14. Thus, the 15% target will be missed by a

substantial amount in the baseline.

Total CO2 emissions are expected to grow by 4.7% per year from 2010 to 2020, and will

reach 10.8 Gt in 2020, which correspond to a 19.9% and 33.3% CO2-intensity reduction

compared to 2010 and 2005, respectively. Similarly, according to the assessment by

Zhang (2011), the proposed carbon intensity target does not just represent business as

usual. The CO2-intensity reduction in 2020 compared with 2005 in the baseline of this

paper is in line with the reference scenario in UNDP and Renmin University of China

(2010), but much lower than in most other studies such as IEA (2009), Jiang et al. (2009)

or Wang et al. (2009), where the CO2-intensity in the baseline corresponds to a reduction

of 40%, 44% and 45%, respectively. To a large extent, these differences can be traced

back to different assumptions about the development of energy efficiency. Compared

with some other studies (Wang et al. 2009) our study uses a lower energy efficiency

improvement rate for the baseline (average 2.1% per year) and the CO2-intenisty target

for 2020 is missed by a significant amount.

4.2 Results for policy scenarios

4.2.1 Effects on GDP

Table 5 displays the economic impact of mitigation policies. Accordingly, in all policy

scenarios, the change in GDP relative to baseline in 2020 is rather small. Interestingly,

the findings for S-sec suggest that the GDP of using the carbon tax to lower output tax in

the sectors most negatively affected by the carbon tax are absolutely not affected by

carbon tax policy.

Not surprisingly, when the revenue from the carbon tax is used for government

consumption, government spending will rise, in S-gov by 1.16% compared to the

baseline. For the other policy scenarios, government spending will be negatively

affected. In general, mitigation policies have negative effects on household consumption.

Exports and imports decrease in all policy scenarios except S-sec. The results suggest

that net exports increase in S-sec. This is mainly due to the fact that the average output

price level is lower in S-sec, which leads to higher international competitiveness.

Table 5: Change in GDP in policy scenarios compared to the baseline in 2020 (in %)

Scenarios

GDP change rate

Government consumption change rate

Household consumption change rate

Export change rate

Import change rate

Net export change rate

S-gov -0.2 1.16 -0.62 -0.14 -0.11 -0.80 S-hou -0.27 -0.18 -0.18 -0.17 -0.14 -0.95 S-sec -0.001 -0.01 -0.07 0.09 0.09 0.08 S-ren -0.13 -0.35 -0.34 -0.19 -0.19 -0.11

The mitigation cost is expressed as the change in GDP compared to baseline per abated

ton of CO2 (Table 6). Between 2013 and 2020, mitigation costs increase in S-gov and S-

hou, while they drop significantly in S-ren and S-sec with the passing of the years.

Table 6: Mitigation cost in different policy scenarios (in RMB/t)scenarios　 2013 2020

S-gov 137 205 S-hou 183 285 S-sec 35 2

S-ren 141 68

4.2.2 Effects on the structure of the economy

All policy scenarios will lead to structural change between 2007 and 2020 compared to

the baseline, corresponding to a drop in shares of the primary and secondary sectors of

about three and three percentage points, respectively (Table 7). In comparison, the share

of the tertiary sector increases by nearly six percentage points.

For some individual industrial sectors (i.e. within the secondary sector), however, climate

policy results in more substantial changes (Table 8 and Table 9). In general, these effects

are most pronounced in S-ren, while the effects of S-gov and S-hou on industry structures

are quite similar. Also, energy sectors will generally be most affected. In particular, coal-

related industries (mining, coking) will lose value-added shares in response to climate

policy, but – to a lesser extent - typically also natural gas extraction. Energy-intensive

industry sectors will also be negatively affected, but less dramatically than the coal-

related industries. Almost all non-energy industrial sectors and also natural gas extraction

benefits from the carbon tax compared to the baseline, if tax revenues are used to lower

output taxes in these sectors. The carbon tax leads to higher shares for electricity

generation from nuclear and renewables and from the low-carbon fuels gas and oil, but

leads, as intended, to reductions in coal-fired power generation. These effects are

amplified in the S-ren scenario, where in particular renewables exhibit the largest

increase in output shares at the expense of coal- and now also gas- and oil-based power

generation. In fact, the total electricity generation in S-ren will be higher than in the

baseline.

Table 7:Value-added share of economic sectors in 2007 and 2020 (%)　

20072020

　 Baseline S-gov S-hou S-sec S-renPrimary sector 10.6 8.0 8.0 8.0 8.0 8.0

Secondary sector 51.6 48.4 48.3 48.3 48.4 48.5

Tertiary sector 37.9 43.6 43.7 43.6 43.6 43.4

Table 8: Rate of change in output of selected industry sectors in the policy scenarios compared to baseline in 2020 (%)

　 S-gov S-hou S-sec S-renCoal Mining and Washing -11.9 -11.9 -10.8 -16.1 Coking -9.5 -9.6 -8.2 -9.6 Gas Production and Supply -2.9 -2.8 -2.3 -3.2 Ferrous Metals Mining and Dressing -0.8 -0.9 -0.1 -0.4 Smelting and Pressing of Ferrous Metals -0.8 -0.8 0.0 -0.6 Raw Chemical Materials and Chemical Products -0.6 -0.7 0.3 -0.4 Metal Products -0.6 -0.7 0.0 -0.4 Ordinary Machinery Equipment for Special Purpose -0.6 -0.7 -0.1 -0.3 Natural Gas Extraction -0.6 -0.6 0.5 -0.8 Other Nonmetal Mineral Products -0.6 -0.6 0.3 -0.3 Tap Water Production and Supply -0.5 -0.4 -0.1 -0.1 Refined petroleum -0.5 -0.5 0.4 -0.9 Nonferrous Metals Mining and Dressing -0.4 -0.5 0.4 -0.1 Other Miners Mining and Dressing -0.4 -0.5 0.1 -0.1 Glass and Glass Products Manufacturing -0.4 -0.4 0.4 -0.2 Transportation Equipment -0.4 -0.4 0.1 -0.1 Cement, Lime and Plaster Manufacturing -0.3 -0.4 0.1 0.1 Smelting and Pressing of Nonferrous Metals -0.3 -0.4 0.6 -0.1 Construction -0.3 -0.4 0.1 0.2 Transport, Storage, Post and Telecommunication Services -0.3 -0.3 -0.1 -0.4

Note: All output figures for 2020 are reported in constant 2007 base year prices.

This table covers the industry sectors which are most negatively affected by the carbon tax.

Table 9: Rate of change in output of different electricity generations in the policy scenarios compared to baseline in 2020 (%)

　 S-gov S-hou S-sec S-rencoal -3.6 -3.6 -2.6 -17.0 oil 63.5 64.7 79.2 -79.7 gas 4.3 4.3 5.1 -11.7 hydro 1.4 1.4 1.0 5.3 nuclear 1.0 1.0 0.8 167.8 wind 1.0 0.9 0.8 269.4 biomass 1.1 1.0 0.8 165.9 solar 0.5 0.4 0.5 247.5

Note: All output figures for 2020 are reported in constant 2007 base year prices.

4.2.3 Effects on electricity generation

Table 10 shows the electricity generations by technologies in 2020 in our study and

World Energy Outlook 2011. Our results show that the total electricity generation will be

more than double between 2007 and 2020, from 3318 TWh in 2007 to 8802 TWh under

baseline and 8363 under S-ren in 2020 which is higher than WEO 2011 (IEA 2011).

Empirically, the annual growth rate of total electricity generation is 12% in the recent

decade, and the total generation reaches 4693 TWh in 2011. With the rapid development

of urbanization, the demand of electricity will be possible stay in a high level. Our results

show that the annual growth rate of total electricity generation is 6.6%-7.2% in all

scenarios from 2012-2020.

Non-fossil energy generation by types will increase by a factor of more than three (for

hydro) to almost fourteen (for biomass) in the baseline. In comparison, non-fossil energy

generation will grow even more significantly under S-ren. In the S-ren scenario, non-

fossil fuel generation will account for 32.9% of total electricity generation and the share

of non-fossil fuel will be over 16.5% in total energy consumption in 2020 (Table 11).

Hence, the renewable energy target is only met in S-ren. Non-fossil energy electricity

will partly substitute for fossil energy electricity for the reason that the cost relative to

base year for non fossil energy generation will gradually decline. However, the

production cost for fossil fuel electricity will even increase with the carbon tax.

According to the ‘Medium and Long-Term Development Plan for Renewable Energy’15

and ‘Medium and Long-Term Development Plan for Nuclear Energy’, the total estimated

investments from 2006 to 2020 are 2 and 0.45 trillion RMB to achieve the renewable

energy and nuclear development target, respectively. However, the demand of investment

will be change due to the update of the renewable and nuclear target. Thus, the estimated

investment for renewable and nuclear should be more than 3.5 trillion from 2006 to 2020.

The share of non-fossil fuel in total energy consumption is 7.1% in 2005. Under S-ren,

the amount of carbon tax revenues is 370 billion RMB in 2020, which equals to 2.3% of

the total tax revenues. The results show that the total accumulated carbon tax revenues

from 2013 to 2020, which invest in nuclear and renewable electricity technologies, are

nearly 2.7 trillion RMB. It promotes the development of non-fossil fuel from the share of

10.5% in baseline to 16.5% in S-ren.

Table 10: Comparison of electricity generation by different technologies between our study and WEO 2011 in 2020

　 Electricity generation (TWh)This study World Energy Outlook 2011

　 Baseline S-gov S-hou S-sec S-rennew

policiesCurrent policies

450ppm

Coal 6621 6382 6383 6445 5495 4704 5194 4079Oil 72 118 119 130 15 15 16 14Natural gas 116 121 121 122 102 355 286 379Hydro 1631 1654 1653 1648 1718 1112 1079 1146Nuclear 256 258 258 258 685 544 520 611Wind 52 52 52 52 192 388 318 441Biomass 42 42 42 42 111 109 95 138Solar 13 13 13 13 46 29 23 35total 8802 8642 8642 8710 8363 7264 7537 6858

Table 11 Share of non-fossil fuel in 2020 (%)　

20072020

　 Baseline S-gov S-hou S-sec S-renShare of non-fossil fuel electricity generation 

16.7 22.7 23.4 23.4 23.1 32.9

Renewables 14.8 19.7 20.4 20.4 20.2 24.7 Nuclear 1.9 2.9 3.0 3.0 3.0 8.2 Share of non-fossil fuel energy in primary energy consumption

7.3 10.5 11.4 11.4 11.2 16.5

4.2.4 Effects on CO2 emissions

The increase in the price for CO2 emission in the policy scenarios helps reduce emission

intensity and energy consumption by increasing the price of fossil fuels and fuel-based

outputs, in particular of electricity. Because of the high carbon content of coal, its price is

particularly sensitive to a carbon tax. Hence, the 40RMB/tCO2 tax rate leads to an

increase in the coal price of 10%-12% in 2020 compared to baseline. In comparison, the

prices for oil and gas, which have substantially lower carbon content, only increase by no

more than 1%. In 2020, the CO2 reduction rate, i.e. the CO2 emission reductions in policy

scenarios (compared with baseline) divided by the baseline CO2 emission level, range

from 6% to 13% (Figure 5). Energy saving rate, i.e. the energy consumption reductions in

policy scenarios (compared with baseline) divided by the baseline energy consumption

level, is from 5% to 9% (Figure 6). CO2 emissions and energy consumption in different

scenarios are displayed in Table 12. Scenarios S-gov and S-hou hardly differ with respect

to CO2 mitigation and energy saving. The effects on CO2 emissions and energy

consumption are less pronounced under S-sec compared to S-gov and S-hou. S-ren is the

only policy scenario meeting the CO2-intensity target for 2020.

2007 2009 2011 2013 2015 2017 20190

2

4

6

8

10

12

14

S-gov S-hou S-sec S-ren

year

%

Figure 5 CO2 reduction rate compared to baseline

2007 2009 2011 2013 2015 2017 20190

1

2

3

4

5

6

7

8

9

10

S-gov S-hou S-sec S-ren

year

%

Figure 6 Energy saving rate compared to baselineTable 12: CO2 emissions and energy consumption in different scenarios in 2020

Scenarios　CO2 (Gt)CO2-intensity

decreasing rate compared to 2005(%)

Energy consumptio

n (Gtce)

Energy consumption intensity decreasing rate compared to 2005 (%)

Baseline 10.76 33.3 5.04 30.2S-gov 10.06 37.6 4.76 34.0 S-hou 10.06 37.5 4.76 33.9 S-sec 10.17 37.0 4.81 33.5 S-ren 9.41 41.6 4.59 36.3

Clearly, our results illustrate that the use of the carbon tax revenue significantly

influences the economic and mitigation effects. The policy of using the carbon tax

revenue as a non-fossil electricity investment can not only have significant CO2

mitigation effects, but can also finance the development of non-fossil fuel to stimulate

realization of the non-fossil fuel consumption goal. Our findings suggest that the

negative impact on economy is the smallest when the carbon tax revenues are used to

lower output taxes in the sectors which are most affected by the carbon tax, while CO 2

abatement effects are less obvious. Using the carbon tax revenue to increase household

income produces similar CO2 abatement effects as transfer to the government budget.

The former policy leads to a higher mitigation cost compared to the latter.

4.2.5 Economic effects under the constraint of the CO2-intensity target

Rather than analyzing the economic and environmental effects of imposing a carbon tax,

this section explores the effects of reaching the 40% intensity target. As before, four

policy scenarios are analyzed: S-gov(40%), S-hou(40%), S-sec(40%), and S-ren(40%). In

these scenarios, a CO2-intensity target is given exogenously for all years from 2013 to

2020. It is assumed to increase at constant speed from 22% to reach the target level

corresponding to the reduction of 40% in 2020 (compared to 2005). Consequently, unlike

in the previous analyses the carbon tax will change over time. Also note that while the

intensity targets are identical across scenarios, CO2-emissions will differ, because GDP

differs across scenarios.

The results in Table 13 show that carbon price per ton of CO2 will grow as the years roll

on. Carbon price is lowest in S-ren(40%), and highest in S-sec(40%). Scenarios S-

gov(40%) and S-hou(40%) hardly differ with regard to carbon price, while the impact on

GDP is more significant in S-hou(40%) than S-gov(40%). It is found that mitigation cost

is highest in S-hou(40%), and lowest in S-sec(40%).

Table 13: Economic effects under the constraints of the CO2-intensity target

　 　 S-gov(40%)

S-hou(40%)

S-sec(40%)

S-ren(40%)

2013GDP change rate (%) -0.04 -0.05 -0.01 -0.04 Carbon price (RMB/tCO2) 10 10 12 10 Mitigation cost (RMB/tCO2) 122 169 19 126

2020

GDP change rate (%) -0.34 -0.47 -0.06 -0.15

Carbon price (RMB/tCO2) 78 79 90 45

Mitigation cost (RMB/tCO2) 193 262 34 86

5 Sensitivity analyses

In order to test the sensitivity of the simulation results, four key parameters/assumptions

are altered. First, a higher and a lower economic growth scenario compared to baseline

are considered, since GDP growth is the dominant driver for CO2-emissions and energy

demand. Second, since energy efficiency improvements contribute substantially to

reducing CO2-intensity, a higher and a lower change in energy efficiency compared to

baseline is also considered. Finally, since empirical estimates of substitution elasticities

vary substantially, the key substitution elasticities between the capital-labor bundle and

energy and between capital and labor are subject to sensitivity analysis. Table 14

provides a detailed description of the sensitivity analyses.

Table 14: Assumptions under alternative scenarios (baseline values are in parentheses)　 (All changes are compared with baseline scenario)GDP growth +0.5/-0.5

Annual GDP growth rate from 2012-2020 is 0.5 percentage points higher/lower than in baseline (7.2%)

Aeei +20%/-20%Annual energy efficiency improvement is 20% higher/lower than in baseline (2.1%)

Ela. cap-lab and ene.-20%/+20%

Elasticity of substitution between capital-labor bundle and energy is 20% lower/higher than in baseline (0.25)

Ela. cap and lab -20%/+20%

Elasticity of substitution between capital and labor is 20 % lower/higher than in baseline (0.63)

Table 15 displays the individual effect of changes in key assumptions on the CO2-

intensity decreasing rate in 2020 compared to 2005 and on the share of non-fossil fuels in

2020. Not surprisingly, the CO2-intensity reduction rate is particularly sensitive to energy

efficiency improvements. Although the GDP growth rate change has substantial effects

on total CO2 emission, it will keep CO2-intensity almost constant. The non-fossil fuel

consumption share is not very sensitive to either the GDP growth rate or the energy

efficiency improvement rate. The results further suggest that for equal percentage

changes, the elasticity of substitution between capital and labor has a larger impact on the

CO2-intensity and non-fossil fuel consumption shares than the elasticity of substitution

between capital-labor bundle and energy.

Table 15: CO2-intensity decreasing rate and non-fossil fuel consumption share in baseline and alternative scenarios

ScenariosCO2-intensity decreasing rate in

2020 compared to 2005 (%)Non-fossil fuel consumption

share in 2020 (%)Baseline 33.3 10.5

GDP growth +0.5 33.3 10.6 GDP growth -0.5 33.3 10.5

Aeei +20% 37.2 11.0 Aeei -20% 29.0 10.1

Ela. cap-lab and ene.-20% 35.3 10.7 Ela. cap-lab and ene.+20% 31.4 10.4

Ela. cap and lab -20% 34.2 11.1 Ela. cap and lab +20% 32.9 10.2

6 Conclusions

The brief review of the literature on China’s climate targets indicates that projections

about emissions and CO2-intensity vary widely across studies. Since the costs of

achieving these targets as calculated by economic models depend on the baseline, cost

estimates also vary. While many studies – including IEA (2009) or EIA (2009) –

conclude that China will reach its unilateral CO2-intensity pledge of a 40-45% reduction

by 2020 compared to 2005, results for our own baseline development indicate a reduction

in CO2-intensity of just about 33.3%. Accordingly, China will not achieve its target,

unless additional policy measures are implemented. In particular, assumption about

future improvements in energy efficiency in this paper is more conservative than in most

other studies, implying that historically observed trends in energy efficiency

improvements in China are unlikely to be sustained in the future, given China’s needs to

further invest in infrastructure, and given the relatively short time horizon remaining

until 2020 for energy-efficient technologies to diffuse.

This analysis based on a dynamic energy-environment-economic CGE model for China,

which specifically includes detailed information on electricity technology, suggests that,

in general, imposing a carbon tax of 40 RMB/t involves only modest changes in GDP

compared to a baseline of less than 0.3%. The negative impacts on GDP from carbon tax

are not obvious for the reason that the tax revenue of around 400 billion RMB is put into

economic system that can offset GDP loss due to the carbon tax policy to some extent. In

general, the carbon tax policies will have only small effects on the structure of the

economy, but substantial effects within the secondary sector, especially on coal-related

industries.

The comparisons of the carbon tax under four different assumptions about the tax

revenue use suggest that we should be inclined to choose the mitigation strategies

considering the relationship between the CO2-intensity target and the non-fossil fuel

target, i.e. the development of non-fossil fuel can promote the decline of CO2-intensity.

Our results show that the tax rate of 40 RMB/t beyond our baseline, however, will not be

sufficient for China to reach its CO2-intensity target or its non-fossil fuel target for 2020,

unless tax revenues are earmarked for additional investments in nuclear and renewable

power technologies.

The development for non-fossil fuel need strong investment support, carbon tax revenues

are earmarked for additional investments in nuclear and renewable power technologies

increase the share of non-fossil fuel in primary energy consumption from 10.5% in

baseline to 16.5%. It illustrates that the market mechanism, e.g. carbon tax is one of an

efficient policy to finance the development of non-fossil fuel.

The sensitivity analyses in this paper imply that the future technology improvement

should be estimated with caution. Moreover, although the CO2 intensity seems unaffected

by GDP growth rate, both the total CO2 emission and CO2 reduction will become larger

under a faster GDP growth. On the one hand, the larger total CO2 reduction means a

greater pressure of mitigation for China under the 2020’s unilateral pledge. One the other

hand, China will face a higher stress from the international and the possible future

obligation due to the higher total CO2 emission. Therefore, a moderate GDP growth rate

appears more sustainable considering the tradeoff between economic development and

CO2 mitigation.

References

Carraro, C. and Massetti, E. (2011) Energy and Climate Change in China, FEEM Note di

Lavoro 016. 2011.

Carraro, C. and Tavoni, M. (2010) Looking ahead from Copenhagen: How challenging is

the Chinese Carbon Intensity Target? VOX: research-based policy analysis and

commentary from leading economists [Available at:

http://www.voxeu.org/index.php?q=node/4449].

Chandler, W., and Wang, Y. (2009) Memo to Copenhagen: Commentary is

Misinformed- China’s Commitment is Significant, Carnegie Endowment for

International Peace [Available at

http://www.carnegieendowment.org/publications/index.cfm?fa=viewandid=24275].

Dai, H.C., Masui, T., Matsuoka, Y. and Fujimori, S. (2011) Assessment of China’s

Climate Commitment and Non-Fossil Energy Plan towards 2020 Using Hybrid

AIM/CGE Model. Energy Policy 39(5): 2875-2887.

den Elzen, M.G.J. and Höhne, N. (2008) Reductions of Greenhouse Gas Emissions in

Annex-Ⅰand Non-Annex-ⅠCountries for Meeting Concentration Stabilization

Targets: An Editorial Comment. Climate change 91: 249-274.

Department of Energy Statistics of National Bureau of Statistics, and Comprehensive

Department of National Energy Administration (2008) China Energy Statistical

Yearbook 2008, China Statistics Press, Beijing.

Department of Energy Statistics of the National Bureau of Statistics (2009) China Energy

Statistical Yearbook 2009, China Statistics Press, Beijing.

EIA (2009) International Energy Outlook 2009, Energy Information Administration,

Washington, DC.

Gupta, S., Tirpak, D.A., Burger, N., Gupta, J., Höhne, N., Boncheva, A.I., Kanoan, G.M.,

Kolstad, C., Kruger, J.A., Michaelowa, A., Murase, S., Pershing, J., Saijo, T. and

Sari A. (2007) Policies, Instruments and Co-operative Arrangements. In Climate

Change 2007: Mitigation. Contribution of Working Group III to the Fourth

Assessment Report of the Intergovernmental Panel on Climate Change (B. Metz,

O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer, Ed.) Cambridge University Press,

Cambridge, UK.

Han, Y.J. and Liu, X.L. (2010) Measurement of Incremental Cost for CO2 Mitigation in

China, Management Review 22(06): 100-105.

Howitt, R.E. (1995) Positive mathematical programming. American Journal of

Agricultural Economics 77, 329–342.

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 2.

Energy. Prepared by the National Greenhouse Gas Inventories Programme, (H.S.

Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, Ed.). Published: IGES,

Japan.

IEA (2005) Projected Costs of Generation Electricity, International Energy Agency,

Paris.

IEA (2007) World Energy Outlook 2007, International Energy Agency, Paris.

IEA (2008) World Energy Outlook 2008, International Energy Agency, Paris.

IEA (2009) World Energy Outlook 2009, International Energy Agency, Paris.

IEA (2010) World Energy Outlook 2010, International Energy Agency, Paris.

IEA (2011) World Energy Outlook 2011, International Energy Agency, Paris.

Jiang, K. and Hu X. (2009) China's Low Carbon Development Scenario in 2050. In 2050

China Energy and CO2 Emissions Report. (Study Team of 2050 China Energy and

CO2 Emissions, Ed.). Science Press, Beijing.

Li, H.Q. and Wang, L.M. (2010) China's Low Carbon Energy Development Potential and

Its Contribution to Accomplishing the National GHG Mitigation Target. Quaternary

Sciences 30(3):473-480.

Liu, J. (2010) The Research of China’s Carbon Trade Market Mechanism Based on

Intensity Reduction. Ph.D. thesis. Fudan University. Shanghai.

Lofgren, H., Harris, R.L. and Robinson, S. (2002) A Standard Computable General

Equilibrium (CGE) Model in GAMS, International Food Policy Research Institute.

McKibbin, W.J., Morris, A. and Wilcoxen, P.J. (2010) Comparing Climate

Commitments: A Model-Based Analysis of the Copenhagen Accord. The Harvard

Project on International Climate Agreements. Discussion paper 10-35。 National Bureau of Statistics of China, 2008, China Statistical Yearbook 2008. China

Statistics Press, Beijing.

Paltsev, S., Reilly, J.M., Jacoby, H.D., Eckaus, R.S., McFarland, J., Sarofim, M.,

Asadoorian, M. and Babiker, M. (2005) The MIT Emissions Prediction and Policy

Analysis (EPPA) Model: Version 4. MIT [Available at

http://globalchange.mit.edu/pubs/abstract.php?publication_id=697].

Peterson, E.B., Schleich, J. and Duscha, V. (2011) Environmental and Economic Effects

of the Copenhagen Pledges and More Ambitious Emission Reduction Targets.

Energy Policy. 39(6): 3697-3708.

Shi, M.J. and Zhou, S.L. (2010) The Impact of Low Carbon Technology Development on

China’s CO2 Emission Reduction. Management Review 22(6): 48-53.

Stern, D.I and Jotzo F. (2010) How Ambitious are China and India’s Emissions Intensity

Targets? Environmental Economics Research Hub Research Reports, No. 51,

Crawford School of Economics and Government, Australian National University,

Canberra, Australia.

Sue Wing, I. (2006) The Synthesis of Bottom-up and Top-down Approaches to Climate

Policy Modeling: Electric Power Technologies and the Cost of Limiting US CO2

Emissions. Energy Policy, 34(18), 3847-3869.

The Academic Group of Development Research Center of State Council (2005) The

Prospects of China's Economic Growth. Decision and Information, 8:4.

The Leading Group of the State Council on the First National Economic Census (2004)

China Economic Census Yearbook 2004. China Statistics Press, Beijing.

UNDP and Renmin University of China (2010) Chinese Human Development Report

2009/10. China Translation & Publishing Corporation, Beijing.

Wang, F. and Feng, G.F. (2011) Contribution of Improving Energy Mix to Carbon

Intensity Target in China ： Potential Assessment. China Industrial Economics

4:127-137.

Wang, K., Wang, C. and Chen, J.N. (2009) Analysis of the Economic Impact of Different

Chinese Climate Policy Options Based on a CGE model Incorporating Endogenous

Technological Change. Energy policy, 37(8), 2930-2940.

Wei, Y.M., Liu, L.C., Fan, Y., Wu, G. et al. (2008) China Energy Report (2008):CO2

Emissions Research. Economic Science Press, Beijing.

Wu, Y.J. and Xuan, X.W. (2002) Economic Theory of Environmental Tax and Analysis of

Their Application in China. Economic Science Press, Beijing.

Zhang, Z.X. (2010) Assessing China’s Carbon Intensity Pledge for 2020: Stringency and

Credibility Issues and Their Implications. FEEM, Nota di Lavorno 158.2010.

Zhang, Z.X. (2011) Breaking the Impasse in International Climate Negotiations: A New

Direction for Currently Flawed Negotiations and a Roadmap for China to 2050.

FEEM, Nota di Lavoro 49.2011.

Zhou S.L., Shi M.J., Li N., and Yuan Y.N. (2011) Impacts of Carbon Tax Policy on CO2

Mitigation and Economic Growth in China. Advances in Climate Change Research

2(3): 124-133.

Appendix

Table 16 Parameters for the substitution elasticities

　 Armington K-L KL-EE EH-FF F-F E-H1 Agriculture 2.42 0.63 0.25 0.7 1.5 1.52 Coal Mining and Washing 3.05 0.63 0.24 0.65 1.3 1.53 Crude Oil Extraction 5.20 0.63 0.24 0.65 1.3 1.54 Natural Gas Extraction 17.20 0.63 0.24 0.65 1.3 1.55 Ferrous Metals Mining and Dressing 0.90 0.63 0.25 0.7 1.5 1.56 Nonferrous Metals Mining and Dressing7Other Miners Mining and Dressing 0.90 0.63 0.25 0.7 1.5 1.58 Food and Beverage 2.49 0.56 0.25 0.7 1.5 1.59 Tobacco Processing 1.15 0.56 0.25 0.7 1.5 1.510 Textile Industry 3.75 0.63 0.25 0.7 1.5 1.511Wearing Clothes Fur 3.80 0.63 0.25 0.7 1.5 1.512 Furniture 3.40 0.63 0.25 0.7 1.5 1.513 Papermaking 3.04 0.63 0.25 0.7 1.5 1.514 Printing Cultural Education and Sports Articles 2.95 0.63 0.25 0.7 1.5 1.515 Petroleum and Nuclear Fuel Processing 2.10 0.63 0.23 0.6 1.25 1.516 Coking 2.10 0.63 0.23 0.6 1.25 1.517 Raw Chemical Materials and Products 3.10 0.63 0.25 0.7 1.5 1.518Medical and Pharmaceutical Products 3.10 0.63 0.25 0.7 1.5 1.519 Chemical Fiber 3.10 0.63 0.25 0.7 1.5 1.520 Rubber Products Plastic Products 3.10 0.63 0.25 0.7 1.5 1.521 Cement, Lime and Plaster Manufacturing 2.90 0.63 0.25 0.7 1.5 1.522 Glass and Glass Products Manufacturing 2.90 0.63 0.25 0.7 1.5 1.523 Other Nonmetal Mineral Products 4.20 0.63 0.25 0.7 1.5 1.524 Smelting and Pressing of Ferrous Metals 3.42 0.63 0.25 0.7 1.5 1.525 Smelting and Pressing of Nonferrous Metals 2.95 0.63 0.25 0.7 1.5 1.526 Metal Products 3.75 0.63 0.25 0.7 1.5 1.527 General and Special-purpose Machinery 3.99 0.63 0.25 0.7 1.5 1.528 Transportation Equipment 3.15 0.63 0.25 0.7 1.5 1.529 Electric Equipment and Machinery, Electronic and

Telecommunication Equipment4.40 0.63 0.25 0.7 1.5 1.5

30 Instruments Meters Cultural and Office Machinery 4.40 0.63 0.25 0.7 1.5 1.531 Other Machinery Industry 2.95 0.63 0.25 0.7 1.5 1.532 Electricity Production and Supply 2.80 0.63 0.23 0.6 1.25 1.533 Heat Production and Supply 2.80 0.63 0.23 0.6 1.25 1.5

34 Gas Production and Supply 2.80 0.63 0.23 0.6 1.25 1.535 Tap Water Production and Supply 2.80 0.63 0.25 0.7 1.5 1.536 Construction 1.90 0.7 0.25 0.7 1.5 1.537 Transport, Storage and Post Telecommunication 1.90 0.84 0.25 0.7 1.5 1.538 Other Services 1.90 0.63 0.28 0.9 1.6 1.5

Note: K, L, EE, F, E and H stand for capital, labor, energy composite, fossil fuel, electricity and heat, respectively.

1 http://unfccc.int/meetings/cop_15/copenhagen_accord/items/5265.php2 http://www.ndrc.gov.cn/xwfb/t20110310_399044.htm3 http://paper.people.com.cn/gjjrb/html/2010-05/31/node_651.htm4 http://www.ccchina.gov.cn/WebSite/CCChina/UpFile/2007/20079583745145.pdf5 http://nyj.ndrc.gov.cn/zywx/W020071102318742621534.pdf6 http://news.xinhuanet.com/energy/2011-12/16/c_122432442.htm7 The data come from http://cait.wri.org/ and ‘China Energy Statistics Yearbook 2008’8 https://www.gtap.agecon.purdue.edu/databases/default.asp9 http://www.stats.gov.cn/tjsj/ndsj/renkoupucha/2000pucha/pucha.htm10 Provided by Liu Qiang from ERI11 http://www.ndrc.gov.cn/xwfb/t20050628_27571.htm12 1 RMB=US$0.133 by exchange rate in 2007. Hence, 40 RMB correspond to about US$5.32.13 http://www.ndrc.gov.cn/zcfb/zcfbtz/2007tongzhi/t20070904_157352.htm14 Primary electricity is converted according to 1KWh=0.343 kg coal equivalent in 2007 to 1KWh=0.305 kg coal equivalent in 2020.