Impacts of Carbon Tax Policy on CO2 Mitigation and Economic Growth in China

Abstract

In this paper we examine the impacts of carbon tax policy on CO₂ mitigation effects and economic growth in China by using a dynamic energy-environment-economy computable general equilibrium (CGE) model. The results show that 30, 60, and 90 RMB per ton CO₂ of carbon tax rate will lead to a reduction of CO₂ emissions by 4.52%, 8.59%, and 12.26%, as well as a decline in the GDP by 0.11%, 0.25%, and 0.39% in 2020, respectively, if carbon tax revenues are collected by the government. Moreover, with energy efficiency improvements the CO₂ emission per unit of GDP will equally drop by 34.79%, 37.49%, and 39.92% in 2020, respectively. Negative impacts on sectors and households will be alleviated if carbon tax revenues are returned to these sectors and households.

Keywords

dynamic energy-environment-economy CGE model ; carbon tax ; mitigation effect ; economic impact

1. Introduction

The Chinese government has announced to lower its CO₂ emission per unit of GDP by 40%–45% by 2020 compared with the 2005 level, and to increase the share of non-fossil energy in primary energy consumption to 15% by 2020. China is currently in the period of accelerated industrialization and urbanization, with steadily rising energy demand, which means that China’s CO₂ emissions will continue to increase in the short term. China is under high pressure and faces difficulties in controlling the growth of CO₂ emissions.

China has primarily been relying on administrative measures to promote energy saving and CO₂ mitigation. However, market-based policies mainly aim at influencing the market factors, i.e., commodity supply and demand, price, and competition, to stimulate economic agent to reduce energy consumption and CO₂ emissions.

Carbon tax, as a market-based approach, is a highly praised mitigation method. Firstly, it leads enterprises to control CO₂ emission through price change. Secondly, the implementation cost of carbon tax is relatively lower because it can be carried out on the basis of existing tax. Thirdly, it can create a good reputation and position for climate change negotiation. However, some disadvantages also arise from carbon tax. On the one hand, imposing carbon tax may bring negative impacts on economic growth and household income, which is likely to meet resistance from the related interest groups. On the other hand, China may face energy supply shortages in the long term, while the energy demand in China appears rigid. The increased cost owing to carbon tax may be transferred to the energy demand side, which will probably alter CO₂ mitigation effects.

The computable general equilibrium (CGE) model, which is used for simulating and analyzing policy effects, has recently become a popular tool for policy researchers. Carbon tax is turning into one of the research foci in the field of CO₂ mitigation policies. The first type of studies which concentrated on carbon tax by using the CGE model is to simulate the required carbon tax rate under a constraint of mitigation targets [ Zhang, 1998  ; Garbaccio et al., 1999  ; He et al., 2002  ; Wang et al., 2005  ; Liang et al., 2007 ]. The second type involves mitigation and economic effects under different carbon tax policies [ Cao, 2007  ;  Cao, 2009 ; Wang et al., 2009  ; Su et al., 2009a ]. According to Zhang [1998], the negative impact of carbon tax on GDP would be reduced if the carbon tax revenues were used to offset reductions in indirect taxes. Double dividend of environmental tax has also been discussed [ Bovenberg and de Mooij, 1994 ; Bovenberg and van der Ploeg, 1994  ;  Parry, 1995 ]. In regard to China’s carbon tax design, Cao [2009] has simulated the impacts of carbon tax policies with different tax rates and different use of carbon tax revenue. However, CO₂ emission per GDP is predicted to decrease quickly, with an average annual rate of nearly 4% in the future.

In this study, we indicate that additional efforts are needed to achieve the target of reducing CO₂ emissions by 40%–45% in 2020 compared with 2005 levels. In this research, we set a more reasonable baseline scenario, which is based on the results of a bottom up model and actual energy efficiency improvement, and then comprehensively assess the mitigation and economic effects of carbon tax policies, as well as the contribution to carbon intensity target for 2020.

2. Analysis of China’s CO₂ intensity target for 2020

Predictions of GDP from 2011 to 2020 are based on China’s 12th Five-Year Plan, as well as on simulation by Pan et al. [2001] , He [2001] , AGDRCSC [2005] , Wei et al. [2008] , and EIA [2009] . Here, three scenarios with different GDP growth rates are taken into account (Table 1 ). Based on the reasoning that CO₂ accounts for almost 80% of the greenhouse gases (GHGs), and that around 90% of CO₂ emissions come from the burning of fossil fuels, in this paper we only consider the energy related CO₂ emission and proportionately reflect the 2020 carbon intensity target. Energy related CO₂ emissions and reduction under three economic development scenarios in 2020 are displayed in Table 2 .

Table 1. GDP growth rates under three economic development scenarios (%)

Scenario	2008	2009	2010	2011–2015	2016–2020
High economic growth	9.6	9.1	10.3	9.0	8.0
Middle economic growth	9.6	9.1	10.3	7.5	6.5
Low economic growth	9.6	9.1	10.3	6.0	5.0

Table 2. CO₂ emission and reduction under three economic development scenarios in 2020 (Gt)

	Scenario	Consistent with 2005	Reduction by 40%	Reduction by 45%
Total emission	High economic growth	19.41	11.65	10.68
	Middle economic growth	16.89	10.13	9.29
	Low economic growth	14.66	8.80	8.07
Reduction	High economic growth		7.76	8.73
	Middle economic growth		6.76	7.60
	Low economic growth		5.87	6.60

The USA announced a 17% CO₂ emission reduction target by 2020 from 2005 levels. The EU and its member states are committed to an independent quantified economy wide emission reduction target of 20% by 2020, compared with 1990 levels. This target could be increased to 30% under the conditions set out by the European Council. It should be noticed that total CO₂ reduction of the USA and EU are about 2.5–3.2 Gt according to the commitment, which is less than half of the CO₂ reduction under middle economic growth scenarios for China. While the conditional pledge of Japan for 2020 is even less than 1/10 of CO₂ reduction estimated for China. It gets clear that it is not an easy task to achieve the 2020 carbon intensity target set for China. However, China is bearing the high international pressure as it will be still the world’s leading CO₂ emitter, even when reaching the carbon reduction target for 2020. The total CO₂ emissions will account for approximately 1/4 of global emissions in 2020.

3. Methodology and data

3.1. Methodology

The dynamic energy-environment-economy CGE model used in this paper covers 39 sectors, including 1 agriculture sector, 36 industry sectors, and 2 service sectors, and relates to two sets of households and three production factors (labor force, capital, and energy). The inputs of energy factor are constituted of nine energy sectors, that is, coal, oil, natural gas, oil refined products, coke, fuel gas, thermal power electricity, other electricity, and heat.

The basic model is built according to the structure by Lofgren et al. [2002] , while the energy is embedded into the production module according to Wu and Xuan [2002] . Since the elasticity of substitution varies among different inputs, the production is described by a multi-level nested structure. Substitution occurs firstly among different energies, then between capital and energy, and finally between a combination of capital-energy and labor. The constant elasticity of substitution (CES) function (Eq. (1)) is adopted to describe the substitution relationship among them. Intermediate commodities enter into the model with a Leontief structure (Eq. (2)) to reflect the assumption of no substitution among different intermediate commodities, as well as between intermediate commodities bundles and factor bundles, which consists of a labor and a capital-energy bundle. The main production structure is presented in Figure 1 .

Figure 1. Structure of the production module in the dynamic CGE model

CES function:

$Y_{i}=f\left(X_{i1},X_{i2}\right)={\left(\alpha X_{i1}^{\rho }+\beta X_{i2}^{\rho }\right)}^{1/\rho }$ $=A{\left(\alpha X_{i1}^{\rho }+\left(1-\alpha \right)\beta X_{i2}^{\rho }\right)}^{1/\rho }$

( 1)

Leontief function:

$Y_{i}=min\left(\alpha X_{i1},\beta X_{i2}\right)$

( 2)

Where, Y_i denotes output of sector i ; A_i denotes shift parameter of sector i, α denotes share parameter of input X_{i 1} , β denotes share parameter of input X_{i 2} , ρ denotes substitution parameter.

Economic growth is driven by endogenous investment, consumption and net export. Investment is determined by savings which come from household, government, enterprise and abroad. Household income is used for tax payment, consumption and saving, while government income is expended for transfer payment, subsidy, 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. The year 2007 is regarded as the base year in this simulation. The dynamical modeling is realized by capital accumulation, labor growth and technological improvement.

3.2. Data

The basic dataset used in this model is the China 2007 Social Accounting Matrix, which is constructed by using 135-sectors input-output table [ NBSC, 2009 ] and other data of 2007 including custom, tax, international balance of payment, and the flow of funds. The 135-sectors input-output table is aggregated and disaggregated into 39 sectors. Substitution elasticity among different energy, energy and capital, energy-capital combination and labor refers to the researches of Wu and Xuan [2002] , and Paltsev et al. [2005] . Substitution elasticity between import and domestic commodities refers to GTAP-6^① . The data of labor by sectors come from The Fifth Population Census^② and the China Economic Census Yearbook 2004 [ LGSCFNEC, 2004 ]. The data of fixed assets investment and population are from the China Statistical Yearbook 2008 [ NBSC, 2008 ].

Energy consumption should be treated in physical terms, while the input-output table provides energy consumption in each agent in monetary terms. The base year conversion coefficient is obtained by converting monetary terms to physical terms [ NBSC and CDNEA, 2008 ], and is also used in the forecast of future energy consumption with the CGE model. As recommended by the IPCC [2006] , the CO₂ emissions are calculated by the method of multiplying each type of fossil fuel consumption with its CO₂ emission factors and oxidation rates.

4. Scenario design

4.1. Baseline

The GDP growth rates for 2008–2010 are actual measured data, while from 2011–2020 are estimated according to the middle economic growth rate mentioned in Table 1 .

It is assumed that the proportion of effective labor force to total population is constant in the simulation period, while the predicted population is taken from the Research Report on National Population Development Strategy [ NPDSR, 2007 ].

The commodity price index (CPI) is based on historical data. The average value of the CPI from 1990–2010 is 102.4 (once outliers of 114.7, 124.1, and 117.1 for 1993, 1994, and 1995 are eliminated). According to international experience, it is likely that the CPI will not always be at such a high level, therefore the CPI is anticipated to be 102 per year from 2011 to 2015, and 101 per year from 2016 to 2020.

It is assumed that energy efficiency improvements exist under the baseline scenario, which refers to the AIM model^③ with calibrations according to the Medium and Long Term Energy Conservation Plan^④ including the efficiency gap of developed countries. The average annual increase rate of energy efficiency is 2.1%, and for details refer to Shi and Zhou [2010] .

As coal prices are primarily controlled by the domestic and central government, it is endogenous by the equilibrium of market demand and supply. Although oil and gas prices are guided by the government, the external dependence of oil is as high as 50%, and the price reformation is gradually reducing the gap of the international market. Hence, the tendencies of domestic oil and gas consumption prices are exogenous and in line with international markets, which refer to the forecast by the World Energy Outlook 2009 [ IEA, 2009 ] and the Poles model [ Kitous et al., 2010 ]. Electricity prices for China are provided by the predictions of Kitous et al. [2010].

Global prices of commodities are taken from the predictions by Peterson et al. [2011] for the baseline run. The future appreciation of the RMB is also considered and assumed to rise by 10% until 2020.

4.2. Carbon tax scenarios

Economic and environmental effects compared with baseline are analyzed in carbon tax scenarios with the following features (Table 3 ). Scope on carbon tax. It is suggested that carbon tax should be levied at the beginning only on CO₂ emissions from fossil fuel. This is based on the reason that CO₂ accounts for almost 80% of GHGs and 90% of CO₂ emissions come from fossil fuels, moreover, these emissions are relatively concentrated and easy to measure.

Table 3. Carbon tax revenue scenarios

Scenario	Tax rate (RMB per ton CO2 )	Carbon revenue
S1a	30	Government income
S2a	60	Government income
S3a	90	Government income
S2b	60	50% as government income, 50% reduction of other tax rates of the most impacted sectors
S2c	60	50% as government income, 50% as household income
S2d	60	50% as household income, 50% reduction of other tax rates of the most impacted sectors

Carbon tax incidence: consumer or producer? Although carbon tax levied on the consumption process is good for restraints on energy demand, it is easier for tax collection and administration, as well as source control when carbon tax is imposed on the production process.

Date for carbon tax implementation. The first commitment period of the Kyoto Protocol is until the end of 2012, and developing countries should take measureable, reportable, and verifiable (MRV) actions against climate change according to the “Bali Road Map”. In China, carbon tax should be coordinated with other energy related taxes, while the period of tax reform can be regarded as a good opportunity to include carbon tax. For the above reasons, the carbon tax is suggested to start from 2013, and being constant until 2020.

Carbon tax rate. The carbon tax rate primarily ranges from 7 to 44 Euro (73 to 458 RMB)^⑤ per ton CO₂^⑥ in international experiences [ Su et al., 2009b ]. The tax rate should not be too high in the beginning, considering the negative impacts on Chinese enterprises and international competitiveness. Average clean development mechanism (CDM) price in international market is from 3.1 Euro (32 RMB) in 2004 to 14.8 Euro (151 RMB) in 2008 per ton CO₂ . Hence, different tax rates of 30, 60, and 90 RMB per ton CO₂⁶ are set for the simulation.

Carbon tax revenue use. Three types of carbon tax revenue use are considered in this paper. (a) Treatment of the carbon tax revenue as income of the central government. (b) Imposing carbon tax on energy producer while cutting other taxes off in most impacted sectors, proportionately. (c) Transfer of the carbon tax revenues to the households (Table 3 ).

5. Results

5.1. Baseline scenario

The baseline scenario shows that the GDP will reach 70.46 trillion RMB in 2020, with the primary, secondary and tertiary industries accounting for 8.03%, 47.98% and 43.99%, respectively. China’s total energy consumption will grow from 2.53 Gtce in 2007 to 5.66 Gtce in 2020, which means that a GDP growth rate of 82.97% can be achieved while only 61.20% more energy is consumed. Total energy related CO₂ emission is expected to reach 11.52 Gt in 2020, with a growth rate of 5.86% per year from 2007 to 2020, which corresponds to a 31.79% reduction of carbon intensity compared with 2005 (Table 4 ).

Table 4. Simulation results of major indicators under the baseline scenario

Year	GDP (trillion RMB)	Energy consumption (Gtce)	Energy related CO2 emission (Gt)
2007	27.16	2.53	5.50
2009	32.48	3.01	6.48
2011	38.51	3.51	7.51
2013	44.50	3.97	8.40
2015	51.43	4.48	9.36
2018	62.12	5.16	10.61
2020	70.46	5.66	11.52

5.2. Results of different carbon tax rate scenarios

CO₂ emissions and energy consumption are restricted by carbon tax policies. Total CO₂ emissions are predicted to be 11.00, 10.53, and 10.11 Gt under carbon tax rates of 30, 60, and 90 RMB per ton CO₂ in 2020, respectively. This is 4.52%, 8.59%, and 12.26% lower than in the baseline scenario, and compared with 2005 reduces the carbon intensity by 34.79%, 37.49%, and 39.92% in 2020, respectively. Total energy consumption will grow from 2.53 Gtce to 5.47, 5.29, and 5.14 Gtce in 2020, which is 3.43%, 6.51%, and 9.28% less than in the baseline scenario under a carbon tax rate of 30, 60, and 90 RMB per ton CO₂ , respectively (Table 5 ).

Table 5. CO₂ emission and energy consumption for different carbon tax rate scenarios in 2020

Scenario	CO2 emission (Gt)	CO2 reduction rate compared with the baseline scenario (%)	Decreases in CO2 intensity rate from 2005 (%)	Total energy consumption (Gtce)	Energy saving rate compared with the baseline scenario (%)
S1a	11.00	–4.52	34.79	5.47	–3.43
S2a	10.53	–8.59	37.49	5.29	–6.51
S3a	10.11	–12.26	39.92	5.14	–9.28

Table 6 displays the economic effects under different carbon tax rate scenarios in 2020 compared with the baseline scenario. The GDP is modestly negative impacted, with a change rate of –0.11% to –0.39% under carbon tax rate of 30 to 90 RMB per ton CO₂ . The government consumption will increase due to a larger government income from carbon tax revenues. The carbon tax policy will have adverse effects on household income and consumption as these sectors are less competitive in international market, since carbon tax will lead to higher energy and energy intensive commodities prices. It is obviously that the total export will drop significantly with carbon tax policy compared with the baseline scenario.

Table 6. Economic effects in different carbon tax rate scenarios versus the baseline scenario (%)

Scenario	Year	GDP	Government consumption	Household consumption	Net export	Government income	Household income
							Urban	Rural
S1a	2013	–0.11	1.52	–0.45	–4.90	1.21	–0.53	–0.49
	2020	–0.11	1.24	–0.40	–5.66	0.94	–0.51	–0.51
S2a	2013	–0.24	2.90	–0.88	–10.11	2.27	–1.05	–0.98
	2020	–0.25	2.36	–0.77	–11.55	1.77	–0.99	–0.98
S3a	2013	–0.39	4.13	–1.29	–15.56	3.20	–1.55	–1.45
	2020	–0.39	3.38	–1.12	–17.62	2.50	–1.45	–1.43

The energy production sectors will be most impacted, especially the coal mining and washing sector, whose output will reduce by 8.92%, 16.74%, and 23.59% in 2020 under carbon tax rate of 30, 60, and 90 RMB per ton CO₂ , compared with the baseline scenario, respectively. The energy intensive sectors will also be negatively affected, but not so dramatically (Table 7 ).

Table 7. Change rate of output^⑦ in different carbon tax rate scenarios compared with the baseline scenario in 2020 (%)

Industry	S1a	S2a	S3a
Coal mining and washing	–8.92	–16.74	–23.59
Coking	–4.66	–9.36	–14.01
Raw chemical materials and chemical products	–0.76	–1.51	–2.22
Smelting and pressing of ferrous metals	–0.63	–1.24	–1.84
Ferrous metals mining and dressing	–0.62	–1.23	–1.82
Glass and glass products manufacturing	–0.54	–1.07	–1.58
Other nonmetal mineral products	–0.50	–0.98	–1.45
Gas production and supply	–0.50	–1.00	–1.51
Metal products	–0.48	–0.95	–1.41
Nonferrous metals mining and dressing	–0.48	–0.95	–1.42

Note: This table outlines the top 10 negative impacted sectors, and sectors are sized down according to influence extent

5.3. Results of different scenarios of carbon tax revenue

The use of carbon tax revenues will have different impacts on the CO₂ emission and the economy. Table 8 outlines the CO₂ emission and energy consumption under different carbon tax revenue scenarios compared with the baseline scenario in 2020. Total CO₂ emissions reach 10.53, 10.61, 10.53, and 10.63 Gt in 2020 under scenarios of s2a, s2b, s2c, and s2d, respectively, which are 8.59%, 7.92%, 8.59%, and 7.72% less than in the baseline scenario. This simultaneously decreases the carbon intensity by 37.49%, 37.06%, 37.45%, and 36.89% from the 2005 level. Total energy consumption is predicted to be approximately 5.3 Gtce in different carbon tax revenue scenarios in 2020. It can be seen that using carbon tax revenue as household income has similar CO₂ mitigation effects as government income, while levying carbon tax when reducing other tax rates of most impacted sectors will have relative smaller CO₂ mitigation effects.

Table 8. CO₂ emission and energy consumption in different carbon tax revenue scenarios compared with the baseline scenario in 2020

Scenario	CO2 emission (Gt)	CO2 reduction rate compared with baseline (%)	CO2 intensity reduction compared with 2005 (%)	Total energy consumption (Gtce)	Energy saving rate compared with baseline (%)
S2a	10.53	–8.59	37.49	5.29	–6.51
S2b	10.61	–7.92	37.06	5.32	–5.97
S2c	10.53	–8.59	37.45	5.30	–6.49
S2d	10.63	–7.72	36.89	5.33	–5.79

In Table 9 the economic effects in different carbon tax revenue scenarios compared with the baseline in 2013 and 2020 are displayed. It is illustrated that using carbon tax revenue as household income will have a negative effect on the GDP, whereas the adverse impacts on household income and household consumption can be alleviated. Although the CO₂ mitigation effects are less obvious with the policy of levying a carbon tax while reducing other tax rates of more vulnerable sectors, the GDP loss appears to be smaller and the negative impacts on net export will be alleviated to a certain extent.

Table 9. Economic effects in different carbon tax revenue scenarios compared with the baseline scenario (%)

Scenario	Year	GDP	Government consumption	Household consumption	Net export	Government income	Household Urban	income Rural
S2a	2013	–0.24	2.90	–0.88	–10.11	2.27	–1.05	–0.98
	2020	–0.25	2.36	–0.77	–11.55	1.77	–0.99	–0.98
S2b	2013	–0.19	2.05	–0.65	–7.39	1.51	–0.82	–0.86
	2020	–0.20	1.80	–0.62	–8.98	1.33	–0.83	–0.89
S2c	2013	–0.29	1.45	–0.33	–12.17	0.96	–0.69	0.19
	2020	–0.31	1.18	–0.36	–13.73	0.74	–0.72	–0.18
S2d	2013	–0.23	0.39	–0.04	–8.88	0.00	–0.41	0.35
	2020	–0.26	0.44	–0.17	–10.47	0.00	–0.51	–0.06

It can be seen from Table 10 that the change rate of output under the policy of using carbon tax revenue as government income and household income are similar, while reducing other taxes of those most impacted sectors will decrease the negative impacts to some degree.

Table 10. Change rate of output^⑧ in different carbon tax revenue scenarios compared with the baseline scenario in 2020 (%)

Industry	S2a	S2b	S2c	S2d
Coal mining and washing	–16.74	–15.72	–16.74	–15.45
Coking	–9.36	–8.46	–9.38	–8.23
Raw chemical materials and chemical products	–1.51	–1.28	–1.54	–1.25
Smelting and pressing of ferrous metals	–1.24	–1.04	–1.27	–1.02
Ferrous metals mining and dressing	–1.23	–0.68	–1.27	–0.57
Glass and glass products manufacturing	–1.07	–0.93	–1.08	–0.90
Gas production and supply	–1.00	–0.91	–0.96	–0.85
Other nonmetal mineral products	–0.98	–0.86	–1.01	–0.85
Metal products	–0.95	–0.80	–0.99	–0.79
Nonferrous metals mining and dressing	–0.95	–0.77	–1.03	–0.80

Note: This table outlines the top 10 negative impacted sectors, and sectors are sized down according to influence extent

6. Sensitivity analyses

To get a reliable CO₂ emission projection and evaluate the economic and CO₂ mitigation effects the baseline scenario is investigated. Hence, it is worth to test the robustness of the baseline results in face of the uncertainties of future projections. To perform a sensitivity analysis of the baseline results, several key parameters are examined and divided into three types of alternative scenarios. The first type is a high or low energy efficiency improvement, since energy efficiency improvement makes great contribution to the reduction of carbon intensity. The second category is chosen to understand the robustness of model by changing the elasticity of substitution. The third is motivated by the fact that exchange rates are uncertain. Table 11 shows the detailed description of alternative scenarios.

Table 11. Assumptions under different alternative scenarios

Scenario	Assumptions (all changes are compared with the baseline scenario)
Ssa	20% higher of energy efficiency improvement
Ssb	20% lower of energy efficiency improvement
Ssc	Elasticity of substitution between labor and capital-energy bundle increase by 20%
Ssd	Elasticity of substitution between labor and capital-energy bundle decrease by 20%
Sse	Elasticity of substitution between capital and energy increase by 20%
Ssf	Elasticity of substitution between capital and energy decrease by 20%
Ssg	RMB appreciated by 12%
Ssh	RMB appreciated by 8%

It can be seen from Table 12 , lower energy efficiency improvement will require more energy use and hence more CO₂ emissions. CO₂ mitigation effects and energy saving rates are quite sensitive to the energy efficiency improvement rate. However, the industry is not significantly affected. Moreover, the results suggest that changes in elasticity of substitution between labor and capital-energy bundle have a larger impact than elasticity of substitution between capital and energy. Whereas, the influence of the exchange rate is relatively moderate.

Table 12. Key variables under the baseline and alternative scenarios (%)

	Baseline	Ssa	Ssb	Ssc	Ssd	Sse	Ssf	Ssg	Ssh
CO2 emission change rate compared with the baseline		6.09	–6.69	–4.99	5.89	1.76	–2.05	–0.64	0.59
Energy consumption change rate compared with the baseline		4.75	–5.17	–4.36	5.18	2.17	–2.47	–0.62	0.56
Carbon intensity decreasing rate from 2005	31.79	35.94	27.22	28.38	35.81	32.99	30.39	31.35	32.19
Primary industry proportion	8.03	8.00	8.06	7.98	8.08	8.03	8.03	7.98	8.08
Secondary industry proportion	47.98	48.07	47.89	48.37	47.56	47.91	48.07	47.75	48.20
Tertiary industry proportion	43.99	43.93	44.05	43.65	44.35	44.07	43.90	44.27	43.72

7. Discussion and conclusions

This paper provides an analysis of the impacts of CO₂ mitigation and economic effects under different carbon tax policies by using a dynamic energy-environment-economy CGE model.

(1) Carbon tax is one of the important policy choices to stimulate the realization of the CO₂ intensity target for 2020. Compared with the baseline scenario, carbon tax rate of 30, 60, and 90 RMB per ton CO₂ will reduce CO₂ emission by 4.52%, 8.59%, and 12.26% in 2020, respectively, under the condition that carbon tax revenues belong to the government. The reduction will lower CO₂ emission per unit of GDP by 34.79%, 37.49%, and 39.92% in 2020 with energy efficiency improvement. However, the simulations do not consider the CO₂ mitigation from non-fossil energy developments. This paper preliminary estimates the contribution to CO₂ abatement from structural energy changes based on the projection of electricity generation by IEA [2009] , along with electricity composition in different pathways by Jiang et al. [2009] . The results show that the proportion of thermal power generation to total power generation drops to 70% and 65% in 2020. This will bring an additional CO₂ abatement of 0.56 Gt and 0.85 Gt. Hence, if the energy efficiency improvement and CO₂ mitigation from structural energy change can be achieved, the policy of levying a 60 RMB per ton CO₂ carbon tax can promote the realization of the 2020 carbon intensity goal.

(2) The negative impacts on GDP from carbon tax are not obvious for the reason that the tax revenue is put into the economic system that can offset GDP loss to some extent. Relative to the GDP of the baseline scenario, the GDP loss rate is 0.11%, 0.25%, and 0.39% in 2020 with the carbon tax rate of 30, 60, and 90 RMB per ton CO₂ if the carbon tax revenue is treated as government income.

(3) Imposing carbon tax will have adverse impacts on energy production, energy intensive sectors, and on household income. Levying a carbon tax and reducing the other tax rates of more vulnerable sectors can alleviate the negative impacts on sectors, while CO₂ abatement effects are less obvious. Using carbon tax as household income will have greater impacts on the GDP, whereas it can improve the household income level.

(4) Carbon tax will place restrictions on energy demand through an increasing price signal. A carbon tax of 60 RMB per ton CO₂ equals to a raise in costs of 109, 168, 180, 176, 182, 186, and 130 RMB per ton of raw coal, coke, crude oil, gasoline, kerosene, diesel oil, and per 1,000 m³ of natural gas, respectively. However, the rigid demand of energy in China will lead to the result that the increasing costs owing to carbon tax are transferred to the energy demand side. This possibly brings uncertainty of CO₂ mitigation effects.

The model used in this paper is based on the assumption of perfect competition. It exists deviations to reality, which may bring some bias with the simulations. The parameters selections have significant influence on the simulation results. However, some data are difficult to get or estimate, such as the capital composition coefficient and the elasticity of substitution. Hence, it is necessary to proceed in the calibration and improvement of the model in future work.

Acknowledgements

The project was supported by National Natural Science Foundation of China (No. 70941034), and “Chinese Environmental Tax” Project of Peking University-Lincoln Institute Center for Urban Development and Land Policy.

Received: 15 June 2011

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Notes

⑦. Output in 2020 mentioned here are calculated based on constant price of the base year (2007)

⑧. Output in 2020 mentioned here are calculated based on constant price of the base year (2007)

①. https://www.gtap.agecon.purdue.edu/databases/default.asp

②. http://www.stats.gov.cn/tjsj/ndsj/renkoupucha/2000pucha/pucha.htm

③. Simulated by Qiang Liu from ERI

④. http://www.ndrc.gov.cn/xwfb/t20050628_27571.htm

⑤. Carbon tax are implemented in different time among countries, and here using the average exchange rate in 2007, that is, 1 Euro=10.42 RMB by exchange rate

⑥. 1 RMB=0.133 US$ by exchange rate in 2007