The energy, environmental and economic impacts of carbon tax rate and taxation industry: A CGE based study in China

Accepted Manuscript The energy, environmental and economic impacts of carbon tax rate and taxation industry: A CGE based study in China Boqiang Lin, Zhijie Jia PII:

S0360-5442(18)31232-5

DOI:

10.1016/j.energy.2018.06.167

Reference:

EGY 13209

To appear in:

Energy

Received Date: 9 April 2018 Revised Date:

15 June 2018

Accepted Date: 24 June 2018

Please cite this article as: Lin B, Jia Z, The energy, environmental and economic impacts of carbon tax rate and taxation industry: A CGE based study in China, Energy (2018), doi: 10.1016/ j.energy.2018.06.167. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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The energy, environmental and economic impacts of carbon tax rate and taxation industry: a CGE based study in China Boqiang Lin*, Zhijie Jia

*

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School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian 361005, PR China E-Mail: [email protected], [email protected]; E-Mail: [email protected]; Corresponding author

Highlights:

The negative impact of CTS on GDP is acceptable, and the maximum scenario will not

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exceed 0.5%.

The focus of carbon taxation should be on energy enterprises.




The carbon tax rate follows the "law of increasing marginal emission reduction".




China should adopt carbon tax policy that simultaneously imposes a high tax rate on

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energy companies and energy-intensive enterprises.

Human activities have led to increase in carbon dioxide emissions, and carbon tax is one

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of the main policy tools for reducing global emissions. This paper constructs nine scenarios considering different carbon tax rates and the different taxable industries to analyze the impact of Carbon Tax System (CTS) on energy, environment and the

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economy. We find that the negative impact of CTS on GDP is acceptable, and the maximum scenario will not exceed 0.5%. If carbon taxes are levied on energy-intensive

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Abstract:

enterprises, the impact on carbon emissions is also relatively small, even if the carbon tax rate is relatively high. Higher carbon tax rate will result in higher CO2 emission reduction

and higher marginal CO2 emission reduction of CTS. The carbon tax rate follows the "law of increasing marginal emission reduction". We also argue that the focus of taxation should be on energy enterprises. It is only in this way that the efficiency of the energy market can be fully implemented to conserve energy and reduce emissions. This paper suggests that China should adopt CTS that simultaneously imposes a higher tax on energy companies and energy-intensive enterprises. This will maximize emissions reductions

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ACCEPTED MANUSCRIPT and have only a small impact on GDP. Keywords: Computable General Equilibrium (CGE) model; carbon taxation scheme; carbon tax rate; energy consumption; CO2 emissions; CO2 reductions.

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1. Introduction Global warming is one of the highly discussed subjects among scholars, politicians and business communities. It is generally accepted that the main cause of global warming is excess greenhouse gas

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(GHG) emissions from the use of fossil fuel energy sources for human activities. Carbon dioxide (CO2) emission is one of the potent GHG emissions (Barrett et al., 2007). Reducing carbon emissions,

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promoting low-carbon economy and implementing low carbon economic actions have become a global consensus and inevitable for human sustainable development (Sun and Ouyang, 2016; Sun et al., 2016). Therefore, transitioning to a low-carbon economy in the shortest possible time has attracted attention in academia and policy arena. Carbon dioxide emissions are closely related to industrial structure, energy structure, consumption structure and economic development level (Chen et al., 2018;

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Lin et al., 2017; McPherson and Tahseen, 2018; Wang et al., 2018). In particular, how to achieve carbon dioxide emission reduction from a policy and institutional perspective has become one of the

Sun et al., 2017).

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important aspects of emission reduction research (Ellerman and Buchner, 2007; Chaabane et al., 2012;

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There are many policy instruments for reducing carbon dioxide emissions. The first is the policy instrument based on administrative orders. Such a measure is relatively rigid and cannot play an efficient role in emission reduction. It is also very difficult to stimulate participants' enthusiasm. Another policy instrument is based on economic incentives, which is divided into total control measures (emission trading scheme) (Hrindl, 2017; Hopkin, 2004), such as EU-ETS (Perino and Pioch, 2017), and price control means (carbon tax) (Brannlund and Persson, 2012; Bumpus, 2015; Dong et al., 2017). Economic incentives are more conducive to play a key role in the market mechanism. Carbon tax is a tax-based pricing mechanism and is easier to implement than cap and

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ACCEPTED MANUSCRIPT trade. Carbon tax is an environmental tax and product tax levied on the amount of carbon in fossil fuels or the amount of carbon dioxide emitted. The studies on the mechanism of carbon tax system is significant. Since September 24, 2016, the Climate Change Division of the National Development and Reform Commission of China announced

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that carbon tax might be levied on enterprises that are excluded in the carbon market system after 2020 (Tanpaifang.com, 2016). In addition, some scholars considered that carbon tax might be an effective way to reduce CO2 emission and protect the economy. Franks et al. (2017) developed a multi-region

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model of tax competition and resource extraction to assess the fiscal incentive of imposing a carbon tax rather than a capital tax. Meng et al. (2013) assessed the environmental and economic impacts of

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carbon tax in Australia. Gerlagh et al. (2009) studied the interaction between carbon taxes and innovation externalities, and argued that carbon taxes should be high compared to the Pigouvian levels when the abatement industry is developing.

Carbon tax has been studied since the late 1980s. Pearce (1991) argued that carbon tax has a

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double dividend: it can improve environmental quality, and the income from the carbon tax can offset other enterprise taxes, resulting in more employment and investment, and a more efficient economy. Vankooten et al. (1995) argued that carbon taxes and subsidies will affect optimal forest rotation and,

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consequently, the carbon stored in forests. Wang et al. (2017) analysed the interrelationship between corporate operations in the supply chain and carbon tax policies. Frey (2017) analysed the economic

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and environmental impacts of different levels of carbon tax in Ukraine using a computable general equilibrium model. Rosas-Flores et al. (2017) analysed the distributive effect of subsidies removal and carbon taxes on Mexican families. Jiang & Shao (2014) estimated the distributional effect of a carbon tax on households in various income groups using the input-output model and the Suits index. Insley (2017) developed profit maximization model with the option of exploiting a non-renewable resource, choosing the timing and pace of development. Ghaith & Epplin (2017) was conducted to determine the consequences of a carbon tax and determine if carbon tax would be sufficient to incentivize households to install either a grid-tied solar or wind system. Dong el al. (2017) aimed at forecasting -3-

ACCEPTED MANUSCRIPT the possible impact of carbon tax on both carbon reduction and economic losses of 30 Chinese provinces. Chen & Nie (2016) considered that a certain amount of carbon tax in the production process raises social welfare, while it lowers social welfare if imposed in the consumption and redistribution processes. Liu & Lu (2015) explored the impact of carbon tax and different tax revenue recycling

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schemes on China's economy using a Computable General Equilibrium (CGE) model. Although there are numerous studies on carbon tax policy, little research has been done on the impact of different carbon tax rates and industry coverage on China's economic development, energy consumption and

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carbon dioxide emissions. This paper focuses on the options of carbon tax rates and the coverage industry and seeks to determine the appropriate options for China.

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The innovation of this paper is:

1) This paper establishes a dynamic recursive CGE model to analyse the impact of carbon tax on energy, environment and economy. This paper has reference value to CGE modellers. 2) This paper analyses the impact of carbon tax on energy, economic and environment by using

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CGE model. Specially, we focus on different carbon tax rate and industry coverage and seek to determine the appropriate option for levying carbon tax in China. The structure of this paper is as follows. In section 2, we introduce the CGE modelling method,

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how to construct the social accounting matrix and the data sources. Section 3 introduces the scenario design of this paper. Section 4 presents the results and discussions of the model simulation. In section

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5, we propose the conclusion and policy suggestions.

2. Methodology 2.1 CGE model

CGE model is widely used in policy analysis (Lin and Jia, 2017; Paroussos et al., 2015; Lu et al., 2017; Zhao et al., 2018). The construction of all CGE models are based on traditional Walras paradigm, which means that the model can be described as a system of simultaneous equations deduced from all actors’ maximizing behavior. CGE model simulates the behavior of social agents, such as residents, -4-

ACCEPTED MANUSCRIPT enterprises, government, and foreigners (Bohringer et al., 2017; He and Lin, 2017). The CGE framework model in this paper is from Hosoe et al (2010). In this framework, we add some parts: sectoral classification, production function, energy factor, energy-policy block, dynamic recursion, two households. It consists of five blocks: production block, income-expenditure block, trade block,

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energy-policy block, and macroscopic-closure & market-clearing block. The general framework of the CGE model is illustrated in Fig. 1.

CGE model is a very powerful policy analysis tool, however, it still has limitations:

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1) CGE model assumes that policy changes do not affect the level of involuntary unemployment and capital of the labor force, the form of competition among firms, and the rate of

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technological progress.

2) The CGE model itself does not provide a valuable predictive tool, as the assumption of elasticity and growth rate of labor and capital.

3) The data required by the CGE model is even more complex and difficult to find than

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input-output analysis, because it analyzes not only industry, but also individuals and

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government decisions, which are beyond the scope of input-output analysis.

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Macroscopic-closure & Marketclearing Block

Import

Income Tariff

Household Consumption

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Consumption

Armington Commodity

Government Consumption

Consumption Investment

Saving

Intermediate Input

Consumption

Total Savings Saving Saving Income

Transfer payment

σ=0

VA-Energy

Policy cost

σ=0.4

Energy

VA

σ=0.5

Electricity

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Foreign

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Household

Export

Output

Intermediate

Direct tax

Government

Domestic

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Indirect tax & Fine of ETS

Investment & Savings

Wages

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Enterprise

σ=0.5

Non-electric

Labor

σ=1.0

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Coal

CO2 Emission

Fig. 1. General Framework of the CGE model.

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Non-solid

Capital

ACCEPTED MANUSCRIPT 2.1.1 Production block We assume that one sector produces only one product in the CGE model. Moreover, in this block, the output consists of policy cost, Value-Added & Energy (VAE) and intermediate input following a Leontief function. VAE is a bundle that consists of value-added (VA) and energy following a constant

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elasticity of substitution (CES) function. The next level is VA bundle and energy bundle, which consists of capital and labour, electricity and non-electricity energy (fossil energy) input following a CES function. The non-electricity energy bundle consists of coal and non-solid fuel (oil and gas)

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following a CES function. Because China's 139 sector input-output table does not separate the oil and

gas.

2.1.2 Income-expenditure block

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gas industries, and the main energy consumption in China is coal, this paper does not subdivide oil and

The four social agents are government, enterprise, household (both rural residents and urban

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residents), and foreigner. The CGE model embodies the balance and relationship of the four agents. For government, it gets fiscal revenue through direct tax, indirect tax, and tariff; and all the revenue are used for transfer payments, consumption and savings. For domestic enterprises, they get sales revenue

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from consumption by government, households, other enterprises and foreigners to support their own expenditure: indirect tax, households’ income, and savings. For the households, they get income

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through remunerations from enterprises and transfer payments from government, and the income is equal to the sum of their consumption, direct tax and savings.

2.1.3 Trade block

CGE model assumes that both domestic and foreign products of an industry are homogeneous. Thus, for one kind of product, imports and exports cannot exist at the same time. However, imports and exports do exist at the same time in one type of commodity in the real world. Therefore, as in the

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ACCEPTED MANUSCRIPT literature, the Armington assumption is introduced into the CGE model (Lin and Li, 2012; Hosoe, 2014; Lin and Jia, 2018) using CES and CET (Constant Elasticity of Transformation) functions to simulate import and export in the real world.

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2.1.4 Energy-policy block At present, at least 20 countries in the world have imposed carbon taxes. These countries are broadly divided into two categories: the first category, such as Denmark and Netherlands, already have

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a comprehensive carbon tax system, started the implementation of the carbon tax system earlier than others, and have better policy efforts. The second category consists of countries that levied carbon tax

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in the context of the voice of a joint global emission reduction, but implementation is not adequate. Except for the rate of carbon tax and industry coverage, other mechanisms of carbon tax are modelled following the systems of the first category countries - Denmark and the Netherlands: 1) the carbon tax rate is fixed and will be paid in the form of energy tax; 2) for industries that pay carbon taxes, the tax

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can be used to deduct value-added tax (VAT), which can deduct at most 50% of VAT, and the exceeding parts of carbon tax will not be deductible. This block can be expressed by the following

‫ܯܧ‬௜ = ‫ܮܣܱܥ‬௜ × ߛ ௖௢௔௟ + ܱ_‫ܩ‬௜ × ߛ ௢_௚

(1)

ܲ‫ܥܮ‬௘௜ = ‫݌‬௧ ‫ܯܧ‬௘௜ ܴ௘௜

(2)

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equations:

Where the subscript ݅ and ݁݅ represent the whole industries and the industries that is covered by the carbon tax, respectively. ‫ܯܧ‬௜ is the emission by sector ݅. ‫ܮܣܱܥ‬௜ and ܱ_‫ܩ‬௜ are consumption of coal and oil-and-gas in sector ݅. ߛ ௖௢௔௟ and ߛ ௢_௚ denote CO2 emission factors. ܲ‫ܥܮ‬௘௜ is the carbon tax policy cost (before the deduction). ‫݌‬௧ is carbon tax rate. ܴ௘௜ is a virtual variable to control the relief policy of carbon tax in energy industries, which will be discussed in next section.

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ACCEPTED MANUSCRIPT 2.1.5 Macroscopic-closure & Market-clearing block Three principles of market closure are considered in this model: government budget balance, foreign trade balance, and investment-saving balance. The first two balances are introduced in section 2.1.2. As for investment-saving balance, CGE model assumes that all the savings are transformed into

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investment, which means that total investment is equal to total savings. Two principles are considered in market clearing. One is the market clearing of Armington composite commodity. The other is factor market clearing. The former shows that all Armington commodities are used for consumption of

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household and government, intermediate input and savings, without surplus. The latter is that there is

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no unemployment in the market.

2.2 Social accounting matrix

The most important basic data of CGE model is the social accounting matrix, which can be compiled by an input-output table. In this paper, the Input-Output Table of China in 2010 (CIOT) is

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used. The energy data in the SAM is from China Statistical Yearbook (National Bureau of Statistics, 2014) and the economic data is from China Input-Output Association (CIOA) (China Input-Output Association, 2017). An important point worth noting is that CO2 emissions in this paper is only from

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fossil fuel combustion, implying this paper does not consider microbial decomposition, animal and plant respiration. We reclassify the 43 sectors in the CIOT into 14 sectors, as shown in Table 1.

Sectors AGR COL O_G

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Table 1. Description of sector classification and population classification. Description

Agriculture, forestry, animal husbandry and fishery Coal mining and washing industry Petroleum and natural gas exploitation

PAP

Paper industry

CMT FER CMC

Cement Chemical fertilizer Chemicals

STL EQU

Steel smelting and rolling processing industry Equipment manufacturing industry

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ACCEPTED MANUSCRIPT Electricity Construction industry Transportation Other industry

SER

Service

RUR

Rural population

CTZ

Urban population

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ELC CST TRA OTH

2.3 Model dynamics

Capital depreciation is determined by the capital stock of the current period and investment.

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Capital stock is endogenous except for the first period, while investment is endogenous. Labour

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endowment is exogenous and determined by National Population Development Plan (2016-2030) (The Central People's Government of the People's Republic of China, 2017). Autonomous Energy Efficiency Improvement (AEEI) in CGE model is considered in this study, according to Li et al. (2017).

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3. Scenario design

According to the proposal of the relevant task group of China's Ministry of Finance (Beijing Daily, 2016), this paper simulates low, medium and high carbon tax rates at 10 yuan/ton, 50 yuan/ton

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and 100 yuan/ton respectively. As the unit of input-output table in China is yuan, we change the unit to

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USD for international readers by assuming that RMB exchange rate against the U.S. dollar is 7:1 in section 4. According to the design of carbon tax in different countries, this paper simulates the following industry that should pay carbon tax: 1) energy-intensive enterprises, 2) energy-intensive enterprises and the energy industries, 3) all industries, but reducing 40% tax rate in energy industries (before deduction). The scenario design of carbon prices is depicted in Table 2. BAU (Business As Usual) scenario is the scenario without carbon tax. I1, I2 and I3 scenarios are the scenarios that government will levy carbon tax of 10, 50, 100 yuan per ton respectively on energy-intensive enterprises in 2017. E1, E2 and E3 scenarios are that government will impose carbon tax on the

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ACCEPTED MANUSCRIPT energy-intensive enterprises and the energy industries at 10, 50, 100 yuan per ton, respectively. A1, A2 and A3 are scenarios that government will levy carbon tax at 10 ,50, 100 per ton respectively on the whole industry, where the energy industry can enjoy 40% carbon tax relief (referring to the Dutch carbon tax system experience, and after relief, carbon taxes can still be used to offset the indirect tax

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on enterprises). Table 2. Scenario design of taxation enterprises and carbon tax rate. Scenarios

Taxatation industries

Energy-intensive enterprisesa 10 Energy-intensive enterprises 50 Energy-intensive enterprises 100 Energy-intensive enterprises+energy industriesb 10 Energy-intensive enterprises+energy industries 50 Energy-intensive enterprises+energy industries 100 All industries, but reducing 40% tax rate in energy 10 industries (before deduction) A2 All industries, but reducing 40% tax rate in energy 50 industries (before deduction) A3 all industries, but reducing 40% tax rate in energy 100 industries (before deduction) a Energy-intensive enterprises include paper, cement, fertilizer, chemical steel and equipment industries. b Energy enterprises include coal, electricity and oil and gas industries.

4.1 Economic impact

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4.1.1 GDP

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4. Results and Discussion

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BAU I1 I2 I3 E1 E2 E3 A1

Carbon tax rate (yuan/ton)

In all the CM scenarios, the variation in GDP relative to the BAU scenario in 2030 is shown in Fig. 2. Under the BAU scenario, GDP will reach 12.41 trillion USD in 2030, while the GDP in the I1, I2, E1, and A1 scenarios is the same as in the BAU scenario. Will the implementation of carbon tax mechanism not harm China's economy? This is explained in 4.1.3 and 4.1.4. The GDP in I3, E2 and A2 scenarios will be 12.404 trillion USD, 12.403 trillion USD and 12.407 trillion USD respectively, which indicates a GDP reduction by 0.025%, 0.046% and 0.004%. This reduction shows that the

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ACCEPTED MANUSCRIPT negative impact of the three carbon taxation mechanisms on GDP is small. In the E3 and A3 scenarios, GDP will be 12.377 trillion USD and 12.396 trillion USD respectively, which is 0.256% and 0.101% lower compared to the GDP estimate in the BAU scenario. This indicates that the negative impact of the two carbon tax scheme on GDP will be greater than others, but will not exceed 0.5%. This

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conclusion supports earlier findings by Grottera (2015). Taken together, GDP will be hardly affected in the scenarios of low carbon tax, and as the carbon tax rate increases, the negative impact of the carbon tax system on GDP is almost exponential (see the

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impact in El, E2, and E3 scenarios, or other scenarios). In the case that taxation is levied only on energy-intensive enterprises, the impacts on GDP is less than in other scenarios. GDP is affected most

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in the case of energy-intensive enterprises and energy enterprises. With the introduction of a carbon tax for all industries less 40% reduction for the energy industry, the negative impact on GDP is in the

12.414 12.407

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Trillion USD

range of the first two types of carbon tax mechanism.

12.400 12.393

12.379 12.371

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12.364

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12.386

12.357

I1

I2

I3

E1

E2

Other scenarios

E3

A1

A2

A3

BaU scenario

Fig. 2. The variation in GDP relative to the BAU scenario in 2030.

4.1.2 Government Carbon tax revenue The government's total carbon tax revenue is illustrated in Fig. 3. It should be noted that the carbon tax revenue depicted here is the tax that enterprises need to pay in principle, including the part

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ACCEPTED MANUSCRIPT of the indirect tax deduction and the actual portion to pay. Under the low carbon tax rate, different patterns of industries covered in the carbon tax system have little effect on government tax revenue, which will range from 0.005 trillion USD to 0.017 trillion USD. In the medium and high carbon tax rates scenarios, carbon tax revenues will increase almost linearly: the medium carbon tax rate will

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generate government revenue to the tune of 0.025 trillion USD - 0.083 trillion USD; and government revenue will be greater in the scenario with high carbon tax rate. In the I3, E3 and A3 scenarios, government revenue from carbon tax will be 0.050, 0.128 and 0.157 trillion USD, respectively. Based

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on this result, the carbon tax rate and carbon tax revenue will show a linear relationship. Moreover,

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Trillion USD

revenue will be higher if the number of industries included in the carbon tax system increases.

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0.11

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0.09

0.06

0.00 BAU

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0.03

I1

I2

I3

E1

E2

E3

A1

A2

A3

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Fig. 3. Government total carbon tax revenue (including deduction part).

4.1.3 Cost of carbon tax in industries The cost of carbon tax in industries is the carbon tax that enterprises still have to pay after deducting all the deductible indirect tax. This part of the carbon tax will directly increase government tax revenue. Carbon tax cost in the coverage industries in 2030 is illustrated in Fig. 4. In the I1, I2, E1 and A1 scenarios, the carbon tax cost is zero because of the existence of the carbon tax deduction mechanism and the small amount of carbon tax, which also results in no change in the total tax of the enterprise. Thus, the carbon tax mechanism has almost no impact on the economy in these four - 13 -

ACCEPTED MANUSCRIPT scenarios, which is why the GDP did not change as explained in section 4.1.1. In the I3, E2, E3, A2 and A3 scenarios, five industries (agriculture, cement, fertilizer, steel and electricity) actually pay carbon taxes in the different scenarios. Carbon tax costs exist in the cement, fertilizers and steel industries in the I3 scenario; carbon tax costs exist only in the electricity sector in the E2 and A2

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scenarios; carbon tax costs exist in varying degrees in all the industries except agriculture in the E3 scenario; carbon tax costs exist in all the five industries in the A3 scenario, reaching 33.539 billion USD, but the total amount of carbon tax will be less than in the E3 scenario by 56.230 billion USD.

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From this result, carbon tax relief mechanism in the power industry plays an important role.

The main reason for carbon tax cost in the agriculture industry is because the indirect tax on

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agriculture is inherently low compared to agricultural carbon emission, and a small amount of indirect taxes cannot exceed the large amount of carbon tax. Therefore, agriculture has a certain amount of carbon tax cost in the A3 scenario. The electricity industry requires huge coal consumption to support its output so that the power industry has become the biggest carbon taxpayer. This paper also confirms

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that based on the Dutch carbon tax experience, the carbon tax relief mechanism for the energy industry can reduce the pressure on these enterprises. At the same time, the paper also proves that the burden caused by carbon tax relief for the energy industry can be partially distributed to other industries. For

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example, in the E3 scenario, the carbon tax costs for the steel, fertilizers and cement industries are 5.64 billion USD, 0.36 billion USD and 2.93 billion USD respectively, but they will increase by 10.58%,

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25.81% and 10.94% to 6.25 billion USD, 0.46 billion USD and 3.24 billion USD respectively in the A3 scenario.

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Billion USD

ACCEPTED MANUSCRIPT 60 50 40 30 20

ELC STL FER CMT AGR

BAU 0.00 0.00 0.00 0.00 0.00

I1 0.00 0.00 0.00 0.00 0.00

I2 0.00 0.00 0.00 0.00 0.00

I3 0.00 6.83 0.54 3.53 0.00 AGR

E1 0.00 0.00 0.00 0.00 0.00 CMT

FER

E2 16.86 0.00 0.00 0.00 0.00 STL

E3 47.30 5.64 0.36 2.93 0.00

A1 0.00 0.00 0.00 0.00 0.00

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0

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10

A2 2.32 0.00 0.00 0.00 0.00

A3 23.33 6.25 0.46 3.24 0.25

ELC

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Fig. 4. Carbon tax cost in taxation industries in 2030.

4.1.4 Commodity price

The variation in commodity price in the CM scenario compared with the BAU scenario in 2030 is

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illustrated in Fig. 5. It can be seen that when all the carbon taxes are used to deduct indirect taxes (all the carbon tax is no more than 50% of indirect tax in each industry), it can basically be interpreted that there will be no impact on the society and no changes in commodity prices. This also can explain why

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there is no change in the GDP in the I1, I2, E1 and A1 scenarios in section 4.1.1. In the following analysis, we ignore the data of the above four scenarios because their results are the same as in the

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BAU scenario. In this section, we find the following: 1) the higher the carbon tax rate, the higher the commodity price, given that the carbon tax rate is large enough to effectively affect the market. 2) If the carbon tax is only levied on energy-intensive enterprises, even with a very high carbon tax rate, the effect on commodity prices is minimal. 3) Taxation on energy-intensive enterprises and energy enterprises will significantly increase the prices of electricity, cement and steel. Particularly, the price of electricity will rise by 2.51% and 7.71% in the E2 and E3 scenarios respectively. 4) Mechanism that relief carbon tax on energy industries will significantly reduce the rising energy prices.

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%

ACCEPTED MANUSCRIPT 9 8 7 6 5 4 3

1 COL 0.00 0.00 0.24 0.00 0.45 1.53 0.00 0.06 0.85

O_G 0.00 0.00 0.34 0.00 0.39 1.44 0.00 0.05 0.86

PAP 0.00 0.00 0.36 0.00 0.42 1.55 0.00 0.06 0.93

CMT 0.00 0.00 1.78 0.00 0.43 2.77 0.00 0.06 2.25

FER 0.00 0.00 1.14 0.00 0.44 2.16 0.00 0.06 1.62

I1

I3

I2

CMC 0.00 0.00 0.52 0.00 0.42 1.70 0.00 0.06 1.08

STL 0.00 0.00 1.50 0.00 0.44 2.55 0.00 0.06 2.00

EQU 0.00 0.00 0.41 0.00 0.44 1.63 0.00 0.06 0.99

ELC 0.00 0.00 0.13 0.00 2.51 7.71 0.00 0.33 3.67

E1

E2

E3

CST 0.00 0.00 0.37 0.00 0.45 1.63 0.00 0.06 0.97

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I1 I2 I3 E1 E2 E3 A1 A2 A3

AGR 0.00 0.00 0.21 0.00 0.47 1.57 0.00 0.06 0.88

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0

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2

A1

A2

TRA 0.00 0.00 0.20 0.00 0.42 1.41 0.00 0.06 0.78

OTH 0.00 0.00 0.25 0.00 0.45 1.54 0.00 0.06 0.87

SER 0.00 0.00 0.17 0.00 0.41 1.36 0.00 0.05 0.73

A3

Fig. 5. The variation of commodity price in CM scenario compared with BAU scenario in 2030.

4.1.5 Industry output

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Fig. 6 shows the variation in industry output in the CM scenarios compared with the BAU scenario in 2030. Carbon tax can reduce the output of energy industries, regardless of whether the energy industries are covered by carbon tax or not. Under the medium and high carbon tax mechanism

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except in the A2 scenario, the output of coal, oil-gas and electricity industries will reduce by 4.39-12.93%, 2.53-7.65% and 3.81-11.34% respectively, followed by cement, steel, equipment

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manufacturing and other energy-intensive enterprises. Because of the increase in the carbon tax cost for the electricity industry, the industry will directly incur increasing costs and reduce output. As a result, the coal industry as an upstream industry of the electricity sector will encounter significant setback in output. The energy-intensive enterprises will also be affected by the cost of energy price, especially electricity, which will result in a certain level of output reduction.

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%

ACCEPTED MANUSCRIPT 2 0 AGR

COL

O_G

PAP

CMT

FER

CMC

STL

EQU

ELC

CST

TRA

OTH

SER

-2 -4

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-6 -8 -10 -12

I3

E2

E3

A2

SC

-14 A3

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Fig. 6. The variation of industry output in CM scenarios compared with BAU scenario in 2030.

4.2 Energy impact 4.2.1 Coal consumption

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The coal consumption of all the industries in 2030 is shown in Table 3. The variation in coal consumption in all the CM scenarios compared with the BAU scenario in 2030 is illustrated in Fig. 7. The total coal consumption of all the industries in the 2030 BAU scenario is 4.75 Billion tons of coal

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equivalent (Btce). The total consumption in the I3, E2, E3, A2 and A3 scenarios will fall to 4.71 Btce, 4.55 Btce, 4.14 Btce, 4.73 Btce and 4.43 Btce, respectively, or decrease by 0.93%, 4.38%, 12.86 %,

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0.59% and 6.80%. We find that although the electricity industry is the largest tax-paying industry under the high-carbon tax scenario, the electricity industry is not the largest coal-reducing enterprise, rather it is the coal industry. The industries that reduce energy consumption most are the energy industries such as coal, oil, gas and electricity. On the other hand, the results illustrate the effectiveness of the carbon tax. This is because there is a basic economic law between fossil energy consumption and industrial output, that is, to increase output under the same level of production technology, there must be corresponding increase in the input factors. Therefore, this result also confirms the results of industry output changes in section 4.1.5. In addition, we find that the greater the carbon tax rate, the - 17 -

ACCEPTED MANUSCRIPT more obvious the role in reducing energy consumption, and the reduction is at an exponential growth to a certain extent. Energy industry relief mechanism will significantly increase energy consumption under the carbon tax mechanism. Table 3. Coal consumption of all industries in 2030*（unit: Million tons of coal equivalent, Mtce） I3

E2

E3

A2

A3

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BAU

-4.00% -6.00% -8.00% -10.00% -12.00% -14.00% -16.00%

O_G

PAP

CMT

FER

CMC

STL

EQU

ELC

CST

TRA

OTH

SER

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-2.00%

COL

EP

AGR 0.00%

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SC

AGR 25 25 24 23 25 24 COL 429 424 402 352 425 388 O_G 8 8 8 7 8 8 PAP 68 68 66 63 68 65 CMT 252 246 246 229 252 238 FER 1 1 1 1 1 1 CMC 23 23 22 21 23 22 STL 463 459 450 424 461 442 EQU 465 462 453 428 463 446 ELC 2196 2175 2071 1832 2179 2004 CST 10 9 9 9 10 9 TRA 7 7 7 7 7 7 OTH 611 608 596 565 609 588 SER 195 194 190 181 194 188 Total 4753 4709 4545 4142 4725 4430 * For simplicity and readability, the scenarios I1, I2, E1 and A1 have been omitted because their energy data is consistent with the BAU scenario.

-18.00% -20.00% I3

E2

E3

A2

A3

Fig. 7. The variation of coal consumption in all CM scenarios compared with BAU scenario in 2030.

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ACCEPTED MANUSCRIPT 4.2.2 Oil and gas consumption Oil and gas consumption in all industries in 2030 is shown in Table 4. The total oil and natural gas consumption in 2030 will be 1286.06 Million tons of coal equivalent (Mtce) in the BAU scenario. Total consumption in the I3, E2, E3, A2 and A3 scenarios are 1280.13 1251.68, 1182.00, 12.81.39 and

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1232.68 Mtce respectively, or a decrease of 0.21%, 0.262%, 7.94%, 0.36% and 4.08% respectively. The rates of decrease are smaller than those of coal consumption, but the basic trend is the same: the higher the carbon tax, the greater the reduction in oil and gas consumption. The reduction effects in the

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E2 and E3 scenarios are better than in the A2 and A3 scenarios respectively. The reduction in the

other industries.

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energy consumption of the energy industries is larger than that of energy-intensive enterprises and

Table 4. Oil and gas consumption of all industries in 2030*（unit: Million tons of coal equivalent, Mtce） I3 0.21 1.52 63.09 0.23 0.39 74.53 40.18 6.57 20.42 48.81 0.23 25.93 936.20 61.82 1280.13

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0.21 1.54 63.51 0.24 0.40 75.00 40.39 6.62 20.53 49.29 0.23 26.03 940.14 61.93 1286.06

E2

E3

A2

A3

0.20 1.45 60.61 0.23 0.39 73.20 39.38 6.44 19.99 46.48 0.23 25.35 917.22 60.51 1251.68

0.19 1.26 54.95 0.22 0.37 69.42 37.27 6.07 18.90 41.12 0.22 23.98 870.37 57.66 1182.00

0.21 1.53 63.12 0.23 0.40 74.76 40.25 6.59 20.45 48.90 0.23 25.94 937.04 61.74 1281.39

0.20 1.39 59.10 0.23 0.38 72.06 38.78 6.33 19.69 44.98 0.23 24.99 904.49 59.83 1232.68

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BAU AGR COL O_G PAP CMT FER CMC STL EQU ELC CST TRA OTH SER Total

4.3 Environment impact

4.3.1 CO2 emission reduction Table 5 shows the annual carbon dioxide (CO2) emission reductions from the implementation of the carbon tax mechanism in 2017 until 2030 compared with the BAU scenario. The lowest capacity of - 19 -

ACCEPTED MANUSCRIPT emission reduction is the A2 scenario. Starting from 2023, the carbon tax on electricity industries will exceed 50% of indirect taxes. Therefore, the emission reduction effect is only reflected from 2023, and the growth rate is very slow. From 2023 to 2030, total emission reduction will be 0.27 billion tons of CO2 (Bt-CO2). The emission reduction effect in the I3 scenario will not also be satisfactory, with a

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cumulative emission reduction of 0.95 Bt-CO2 over 2017-2030. E2, E3 and A3 scenarios are more capable of reducing CO2 emissions, and the cumulative emission reductions under the scenarios from 2017 to 2030 will reach 5.20 Bt-CO2, 15.11 Bt-CO2 and 7.94 Bt-CO2, respectively. We find that over

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time, the carbon abatement capacity of different scenarios will gradually increase, indicating that carbon tax is an effective emission reduction tool. In addition, we also find that to a certain extent, the

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greater the carbon tax rate, the higher the abatement capacity and the marginal abatement ability of carbon tax rate. This implies that the carbon tax rate follows the "law of increasing marginal emission". For example, when we studied the E1, E2 and E3 scenarios, we found that when the tax rate is 10 yuan/ton, the abatement capacity is 0; when the tax rate is raised to 50 yuan/ton, the emission

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reduction capacity is 5.00 Bt-CO2; and when the tax rate is doubled to 100 yuan/ton, the abatement capacity increased to 300% of the initial capacity by 15.11 Bt-CO2. Therefore, this paper argues that the carbon tax mechanism of low-carbon tax rate is unnecessary, and the medium carbon tax rate must

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meet the reasonable choice of the coverage industry to play a certain emission reduction effect. Table 5. CO2 emission reduction per year. (Unit: billion tons of CO2) E2

E3

A2

A3

0.04

0.24

0.31

0.00

0.29

0.05

0.25

0.57

0.00

0.42

0.05

0.27

0.82

0.00

0.45

0.05

0.28

0.98

0.00

0.47

2021

0.06

0.30

1.02

0.00

0.50

2022

0.06

0.32

1.06

0.00

0.52

2023

0.06

0.34

1.11

0.00

0.55

2024

0.07

0.36

1.16

0.01

0.58

2025

0.07

0.38

1.21

0.02

0.61

2026

0.08

0.40

1.26

0.03

0.64

2027

0.08

0.43

1.32

0.04

0.68

2017 2018 2019 2020

I3

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Year

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ACCEPTED MANUSCRIPT 2028

0.09

0.45

1.37

0.05

0.71

2029

0.09

0.48

1.43

0.06

0.75

2030

0.10

0.50

1.49

0.07

0.79

Total

0.95

5.00

15.11

0.27

7.94

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4.3.2 Carbon intensity The carbon emission intensity in all scenarios is depicted in Fig. 8. The intensity of China's carbon emissions in the BAU scenario from 2017 to 2030 will be reduced from 1.1697 tons/thousand

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USD to 1.0178 tons/thousand USD. There is significant difference in the impact on carbon intensity depending on the carbon tax mechanisms. Under the I3 and A2 scenarios, the capacity of carbon

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emission intensity reduction is very limited, only decreasing by 0.0049-0.0077 tons/thousand USD and 0.0007-0.0056 tons/thousand USD, or 0.43-0.77% and 0.09-0.54% respectively. The emission intensity in the E2 and A3 scenarios will decrease by 0.0287-0.0399 tons/thousand USD and 0.0343-0.0623 tons/thousand USD, respectively, or by 2.48-3.93% and 2.96-6.12%. The intensity of

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carbon emission decreases most obviously in the E3 scenario, decreasing by 0.0371 ~ 0.1183 tons/thousand USD from 2017 to 2030, indicating a reduction of 3.2-11.59%. Based on these results, we find that the trend of the decline in carbon intensity becomes more obvious over time. However, a

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low carbon tax does not contribute to reduction in carbon intensity, while a high carbon tax can

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significantly reduce carbon emission intensity, especially in the E3 scenario. From the perspective of reducing carbon intensity, it is most effective for the government to levy carbon tax on both energy and energy-intensive industries.

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tons/thousand USD

ACCEPTED MANUSCRIPT 1.190 1.155 1.120 1.085 1.050 1.015

0.945 0.910 0.875 0.840 2018

2019

2020

2021

2022

BAU

I3

2023 E2

2024 E3

A2

2025

2026

2027

2028

2029

2030

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2017

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0.980

A3

5. Conclusion and Suggestion

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Fig. 8. Carbon emission intensity in all scenarios during 2017-2030.

By constructing a CGE model, this paper establishes nine scenarios considering low, middle and high carbon tax rates, as well as three different carbon tax mechanism of coverage industries, and

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analyze the impact of these two key factors of the carbon tax mechanism. The following conclusions emerge from the results of the study.

In the case of low-carbon taxes, GDP will be almost unaffected. As the carbon tax rate increases,

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the negative impact of the carbon tax system on GDP will gradually increase. However, even in the situation with the most negative impact, it will not exceed 0.5%. The government can increase its

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income through the carbon tax mechanism. If the carbon tax mechanism is set according to the Danish carbon tax system, which can deduct part of the corporate tax, government revenue from carbon tax will be zero at the low carbon tax rate mechanism and high under the medium and high carbon tax rates. The electricity industry will be the largest taxpayer under the high-carbon tax rate mechanism. This paper also confirms that based on the experience of the Dutch carbon tax, carbon tax relief for the energy industry can significantly reduce the pressure on enterprises. This paper also proves that the tax burden caused by carbon tax relief for the energy industry can be partially distributed to other industries. If carbon taxes are levied only on energy-intensive companies, the effect on commodity - 22 -

ACCEPTED MANUSCRIPT prices, GDP or other emission indicators are minimal even if the carbon tax rate is very high. However, taxing the energy industries will significantly increase the prices of electricity, cement and steel industries, with electricity having the highest price increase. As a result of the increase in the carbon tax cost for the electricity industry, the electricity industry will directly incur increasing costs

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and reduce output. Because of this, the coal industry, which is as an upstream industry of the electricity sector, will encounter a setback in output. As energy-intensive enterprises, they will also be affected by the cost of energy price, especially electricity, which will result in a certain reduction in output.

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Although the electricity industry is the largest tax-paying industry under the high-carbon tax scenario, it is not the largest enterprise to reduce coal consumption. In addition, the industries that

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reduce the most energy consumption are the energy industries such as coal, oil, natural gas and electricity, which illustrate the effectiveness of the carbon tax mechanism. The main reason for this effectiveness is that the carbon tax will cause an increase in energy prices. The rise in energy price, as one of the basic factor price, will directly promote the change in production preference of all

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enterprises. Enterprises and households will change their energy consumption according to the new energy prices to fit their production structure and consumption preference, and achieve optimal solutions. It also explains why the emission reduction effect of taxing energy-intensive enterprises is

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less effective than taxing energy enterprises. We also find that to a certain extent, the greater the carbon tax rate, the more the abatement capacity and the marginal abatement ability of carbon tax rate,

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meaning that the carbon tax rate follows the "law of increasing marginal emission". Moreover, the trend of decline in carbon intensity becomes obvious over time. This paper argues that the carbon tax mechanism, as a simple and easy-to-operate policy, has a very prominent contribution to emission reduction. The low carbon tax rate mechanism is completely unnecessary because it has almost no effect on the economy and energy use. However, the medium carbon tax rate meet the reasonable carbon tax coverage industry so that China can achieve certain emission reduction effects. The emission reduction effect of high carbon tax rate is very significant. It

- 23 -

ACCEPTED MANUSCRIPT is important that energy-intensive enterprises should not be the only industry covered under the carbon tax system. The focus of taxation should be on energy enterprises. Only in this way can the sub-initiative of the market be fully implemented to save energy and reduce emissions while adjusting the energy factor market. The future Power Market Reform in China will be very conducive for the

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implementation of carbon tax system and effectiveness of carbon tax. This paper suggests that China can adopt a carbon reduction scheme that simultaneously imposes

reductions and only have a small impact on China's GDP.

A.1 Production block

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Appendix A. Equation system of the dynamic CGE model

NOEi = αinoe [δ inoeCOALρi i + (1 − δ inoe ) NOSiρi ]1 ρi noe

noe

(1− ρinoe )

noe

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PCOALi δ noe  NOSi  = i noe   PNOSi 1 − δi  COALi 

NOEi PNOEi = COALPCOAL i i + NOSi PNOSi

ENEi = α iene [δ iene ELEiρi + (1 − δ iene ) NOEiρi ]1 ρi ene

ene

(1− ρiene )

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ene

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PELEi δ ene  NOEi  = i ene   PNOEi 1 − δ i  ELEi 

ENEPENE i i = ELEPELE i i + NOEPNOE i i VAi = α iva [δ iva LABiρ i + (1 − δ iva ) CAPi ρ i ]1 ρ i va

PLABi δ iva  CAPi  =   PCAPi 1 − δ iva  LABi 

va

va

(1− ρiva )

VAPVA i i = LABPLAB i i + CAPPCAP i i VAEi = α ivae [δ ivaeVAiρi + (1 − δ iva ) ENEiρi ]1 ρi vae

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a high tax rate on energy industries and energy-intensive industries. This will maximize emissions

vae

vae

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ACCEPTED MANUSCRIPT PVAi δ vae  ENEi  = i vae   PENEi 1 − δ i  VAi 

(1− ρivae )

VAEPVAE i i = ENEPENE i i +VAPVA i i INTi , j = a iIN, j T Z j

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FVAE j = aVAE j Zj INT PZ j = avae j PVAE + ∑ ai , j PQi

A.2 Income-expenditure block

i

  SG = ss g  ∑ TDl + ∑ TZi + ∑ TM i  i i  l 

XPi ,l =

µi 

 TD + TZ + TM − SG ∑ ∑ ∑ l i i   PQi  l i i 

β i ,l xp 

 γ llab LABi ⋅ PLABi + γ lcap CAPi ⋅ PCAPi ) − SPl − TDl  ( ∑  PQi  i 

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XGi =

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SPl = ssl p ∑ γ llab LABi ⋅ PLABi + γ lcapCAPi ⋅ PCAPi

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i

TDl = τ ld ∑ ( γ llab LABi ⋅ PLABi + γ lcap CAPi ⋅ PCAPi )

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i

TZi =τ z PZi Zi (1−cori ) + PLCi

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TMi = τ m PMi Mi A.3 Trade block

PEi = ε PWEi

PMi = ε PWMi

∑ PWE E + SF =∑ PWM M i

i

i

i

i

i

- 25 -

ACCEPTED MANUSCRIPT

(

Q i = γ i δ m i M i η i + δ d i D iη i

)

1 ηi

1

η

 γ i i δ mi PQi 1−ηi Mi =   Qi m  (1+ τ i )PMi   γ δ di PQi  Di =  i   PDi 

1 1−ηi

Qi

(

Z i = θi ξ ei Eiφi + ξ di Diφi

)

1

φi

1

1

SC

 θ φi ξ ei [(1 + τ iz )(1 − cori ) + PLC i / PZ i / Z i ] PZ i  1− φi Ei =  i Zi  PEi  

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 θ i φi ξ d i [(1 + τ iz )(1 − cori ) + PLCi / PZ i / Z i ]PZ i  1− φi Di =   Zi PDi  

A.4 Energy-Policy block

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EMi = ENE _ COALi ×γ coal + ENE _ O _ Gi ×γ o _ g COALi =χicoal × ENE _ COALi NOSi =χinos × ENE _ O _ Gi

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PLCei = p t EM ei Rei

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A.5 macroscopic-closure & market-clearing block

λi

XVi =

PQi

( ∑ SP + SG + ε SF ) l

l

Qi = ∑ XPi ,l + XGi + XVi + ∑ X i , j l

j

∑ LAB = ∑ FF i

i

l

i

i

lab

l

∑ CAP = ∑ FF

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ηi

cap

l

l

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ACCEPTED MANUSCRIPT Acknowledgments The paper is supported by Xiamen University - Newcastle University Joint Strategic Partnership Fund, the Grant for Collaborative Innovation Center for Energy Economics and Energy Policy (No:

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1260-Z0210011), and Xiamen University Flourish Plan Special Funding (No:1260-Y07200).

Conflicts of Interest We declare no conflict of interest.

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