metadata: authors: 0: author: Timilsina,Govinda R.
1: author: Pang,Jun
2: author: Chang,Yuanhua
count: China
docna: 0: docna: Economic Impacts of Meeting China’s NDC
            through Carbon Taxes with Alternative Schemes for Recycling
            Tax Revenues
docty: Policy Research Working Paper
bdmdt: 2022-06-14T00:00:00Z
abstracts: cdata!: As its Nationally Determined
            Contribution, China has committed to reduce the emission
            intensity of its economy by 65 percent below its 2005 level
            by 2030, to help achieve the objectives of the Paris Climate
            Agreement. This study examines the economywide impacts of
            meeting China&#x2019;s commitment through various policy
            instruments, including administrative/regulatory mandates
            and carbon taxes with alternative revenue recycling schemes.
            The policy scenarios are simulated using a dynamic-recursive
            computable general equilibrium model calibrated with the
            latest available data (2017). The simulations show that
            Chinese gross domestic product in 2030 would be about 1
            percent lower than that in the baseline if the country uses
            mandates to constrain national carbon dioxide emissions at
            its Nationally Determined Contribution target. The economic
            costs (loss of gross domestic product) of meeting the
            Nationally Determined Contribution target would be smaller
            than that in the carbon constraint case, no matter how the
            carbon tax revenues are recycled. The economic costs of the
            carbon tax would be lowest when the tax revenue is recycled
            to cut existing taxes on capital or corporate income.
            Recycling the carbon tax revenues to subsidize solar and
            wind electricity further helps reduce emissions; however, it
            causes higher economic costs than other recycling schemes.
            The results highlight the importance of proper design
            architecture for a carbon tax to make it more palatable to
            policy makers and taxpayers.
display_title: Economic Impacts of Meeting China’s
            NDC through Carbon Taxes with Alternative Schemes for
            Recycling Tax Revenues
closedt: 2022-06-14T00:00:00Z
listing_relative_url: /research/2022/06/33842630/economic-impacts-meeting-china’s-ndc-through-carbon-taxes-alternative-schemes-recycling-tax-revenues
topicv3: Carbon Tax m1326372 1845,Climate Change m1326829 567,Climate Change Mitigation m1327079 579,Carbon Pricing m1327308 1846,Carbon Trading m1327771 1776
publishtoextweb_dt: 2022-06-14T00:00:00Z
dois: 10.1596/1813-9450-10087
no_of_pages: 48
author_keys: 0: author_key: 940446
1: author_key: 1632828
2: author_key: NA
model_extractions: Policy Research Working Paper 10087 Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues _Govinda R Timilsina Jun Pang Yuanhua Chang_ Development Economics Development Research Group June 2022
Policy Research Working Paper 10087

# **Abstract**

As its Nationally Determined Contribution, China has committed to reduce the emission intensity of its economy by 65 percent below its 2005 level by 2030, to help achieve the objectives of the Paris Climate Agreement. This study examines the economywide impacts of meeting China’s commitment through various policy instruments, including administrative/regulatory mandates and carbon taxes with alternative revenue recycling schemes. The policy scenarios are simulated using a dynamic-recursive computable general equilibrium model calibrated with the latest available data (2017). The simulations show that Chinese gross domestic product in 2030 would be about 1 percent lower than that in the baseline if the country uses mandates to constrain national carbon dioxide emissions at its Nationally Determined Contribution target. The economic costs (loss of gross domestic product) of meeting the Nationally Determined Contribution target would be smaller than that in the carbon constraint case, no matter how the carbon tax revenues are recycled. The economic costs of the carbon tax would be lowest when the tax revenue is recycled to cut existing taxes on capital or corporate income. Recycling the carbon tax revenues to subsidize solar and wind electricity further helps reduce emissions; however, it causes higher economic costs than other recycling schemes. The results highlight the importance of proper design architecture for a carbon tax to make it more palatable to policy makers and taxpayers.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues**<sup>1</sup>

Govinda R. Timilsina, Jun Pang and Yuanhua Chang<sup>2</sup>

**Key Worlds** : Climate change, Paris climate agreement, Nationally determined contributions, Carbon tax, General equilibrium model, China

## **JEL Classification** : C68, Q43

> 1 The authors would like to thank Christophe de Gouvello, Ping Gao and ZhanFeng Dong and Carolyn Fischer for their valuable comments and suggestions. The views and interpretations are of authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. We acknowledge World Bank’s Research Support Grant (RSB) for financial support.

> 2 Govinda Timilsina (gtimilsina@worldbank.org) is a Senior Economist at the Development Research Group, World Bank. Jun Pang (pangjun2005@ruc.edu.cn) and Yuanhua Chang (changyuanhua2020@163.com) are, respectively, a Professor and a Ph. D candidate at the School of Environment and Natural Resources, Renmin University, Beijing, China.

1
# **Economic Impacts of Meeting China’s NDC through Carbon Taxes with**

# **Alternative Schemes for Recycling Tax Revenues**

# **1. Introduction**

China updated its Nationally Determined Contributions (NDCs) under the Paris Climate Accord in 2021 and now aims to reduce the emission intensity of its economy by over 65% below its 2005 level by 2030. The new climate change mitigation plan is to peak its CO2 emission before 2030 and achieve the carbon neutrality of the economy by 2060 (UNFCCC, 2021). Both market (carbon pricing) and non-market (i.e., regulatory or administrative) policy instruments are being considered to achieve the updated NDC targets in China. An emission trading scheme (ETS) has already been introduced for the electricity sector and its expansion to other emission-intensive sectors is being discussed among the policy makers. A carbon tax could be another policy instrument to help meet the NDC as well as China’s net zero emission target in the long run, by 2060. This is because carbon tax can cover other sectors, such as households and transportation, which cannot be directly covered by an ETS, and implementation of a carbon tax is simpler as compared to an ETS (Goulder and Parry, 2008; Goulder and Schein, 2013; Timilsina 2018). However, policy makers’ interest towards a carbon tax might be influenced by several factors, including its overall costs to the economy. Comparing the economywide costs of a carbon tax across alternative tax design architectures and emission standards would be highly helpful to policy makers. For this purpose, this study simulates, using a multi-sector dynamic computable general equilibrium (CGE), the economic impacts of meeting China’s NDC through an administrative/regulatory mandate and a carbon tax with nine alternatives for recycling carbon tax revenues.

The literature on the economic impacts of the carbon tax in China is rich. Most studies use CGE models for analysis. Key existing studies include Xu and Wei (2022), Cao et al. (2021), Hu et al. (2021), Zhang et al. (2019), Bi et al. (2019), Timilsina et al. (2018), Dong et al. (2018), Pradhan et al. (2017), Zhang and Zhu (2017), Zhang et al. (2016), Li et al. (2014), Li et al.

2
(2012), Sun and Ueta (2011), Wang et al. (2011), Brenner et al. (2007), Li and Higano (2004), Zhang (1998).<sup>3</sup> However, among these studies, only Cao et al. (2021) and Timilsina et al. (2018) analyze a carbon tax to meet China’s NDC. Other studies do not consider meeting the NDC, instead, they analyze some specific issues related to a randomly selected carbon tax, such as technological change (Dong et al. 2018), provincial impacts (Zhang et al. 2019), and impacts on sectoral competitiveness (Wang et al. 2011).

Cao et al. (2021) compare the economic impacts of a carbon tax on China simulated by eight different CGE models in the near term (2020), medium-term (2030) and distant future (2050). However, their objective is to compare the economic impacts across different models instead of comparing the impacts across alternative tax design schemes, particularly the revenue recycling. Moreover, they consider only a few revenue-recycling schemes which are different across the models. The differences in the tax design schemes (i.e., revenue recycling schemes) across the models might have constrained a proper comparison of results generated by the models. Timilsina et al. (2018) examine the economic impacts of a carbon tax aiming to meet China’s NDC. However, it considers only two options (i.e., lump-sum transfer to households and cutting personal income taxes) to recycle carbon tax revenues.

This study examines the general equilibrium effects of a carbon tax to achieve China’s NDC under nine alternative carbon tax-design schemes (or scenarios). It also compares these effects with that of an administrative/regulatory mandate that constrains national CO2 emissions at the NDC target. The key finding of the study is that the economic costs of meeting China’s NDC would be smaller with a carbon tax than that with a mandatory emission constraint. Under the carbon tax cases, the economic costs would be smaller when the carbon tax revenues are recycled either to cut existing capital taxes or corporate income taxes than that in other revenue recycling schemes considered in the study.

This study contributes to the literature in multiple ways. First, it investigates the economic impacts of meeting the updated NDC target of China; existing studies analyze either

> 3 There are many other studies analyzing impacts of climate change policies for China using a CGE model. However, only those studies that explicitly focus on carbon taxes are referred here.

3
the old NDC target or a randomly selected carbon tax. Second, it compares a carbon tax policy with mandated emission constraints for China. Most existing studies for China either analyze the emission constraints or carbon tax; they do not compare them. Third, it examines the impacts of carbon tax more deeply by considering nine alternatives for recycling carbon tax revenues; whereas the existing studies for China consider only a few schemes. Fourth, it analyzes the criteria used to reallocate carbon tax revenues for a given purpose, particularly when the carbon tax revenue is recycled to cut corporate income taxes. No existing studies for China have gone this much deeper while designing a carbon tax. Fifth, the study uses the latest data (the social accounting matrix or SAM for year 2017); the existing studies use older SAMs.

The paper is organized as follows. Section 2 briefly describes the basic structure of the model and data used for model calibration. The policy scenarios are designed in Section 3. This section also offers detailed explanations of how these scenarios are incorporated into the model. Main results from model simulations are discussed in Section 4, followed by key conclusions and policy implications in Section 5.

# **2. The Model and Data**

## **2.1 Basic structure of the model**<sup>**4**</sup>

A recursive dynamic CGE model is developed to simulate the economic effects of the policies considered in this study. The model consists of four economic agents: producers, households, government, and international trade. It captures the interactions within and between the agents. The production sectors are classified into 27 sectors, including six sectors for electricity generation. Table 1 defines these sectors.

**Table 1 Sector description in the CGE model**

|**Sector**|**Description**|
|---|---|
|Agriculture|Agriculture, forestry, animal husbandryand fishery|
|Coal mining|Miningand washingof coal|
|Oil and gas|Extraction of crudepetroleum and naturalgas|

> 4 The detailed model is presented in the Appendix.

4
|Non-energymining|Miningandprocessingof metal and nonmetal|
|---|---|
|Food & tobacco|Food and tobacco manufacturing|
|Textile|Textile|
|Wood & furniture|Woodproducts and furniture manufacturing|
|Pulp,paper &printing|Paperprinting, cultural, educational and sportsgoods|
|Petroleum refinery|Processingof refinedpetroleum, coking,process of nuclear fuel|
|Chemicals|Manufacture of chemicalproducts|
|Non-metallic minerals|Manufacture of non-metallic mineralproducts|
|Primarymetals|Smeltingandprocessingof metals|
|Fabricated metals|Manufacture of metalproducts|
|Machinery & equipment|Manufacture of measuringinstruments|
|Other manufacturing|Other manufacture|
|Gas utility|Production and distribution ofgas|
|Construction|Construction|
|Wholesale & retail trade|Wholesale and retail trades|
|Transport|Transport storage andpostal services|
|Other services|Other Services|
|Thermal electricity|Thermalpowergeneration|
|Hydro electricity|Hydropowergeneration|
|Nuclear electricity|Nuclearpowergeneration|
|Windpower|Windpowergeneration|
|Solarpower|Solarpowergeneration|
|Other electricity|Otherpowergeneration|

Except for the electricity generation sector, producers’ behavior in each sector is modeled through a nested six-tier constant elasticity of substitution (CES) functional form, as shown in Figure 1a. The nesting is different for the electricity sector (Figure 1b) to allow a direct substitution between capital input and fossil energy input. This substitution is important to stimulate decarbonization of the electricity sector through investment in clean power technologies and efficiency improvement in the power sector. The multi-tier CES nested structure provides flexibility for substitution between the various groups of input at the different tiers of the nest. It is also important to note that the electricity sector is further disaggregated into various sub-sectors, as shown in the lower tiers in Figure 1a and 1b. It provides a more realistic representation of the electricity generation technologies because their input structure, particularly the fuel inputs, are significantly different. While renewables do not require any fuels for power generation (except own electricity consumption), coal is the main input in the coal-fired electricity generation sector. The breakdown is essential to model a 5
scenario where carbon tax revenues are used to subsidize renewable electricity, particularly solar power and wind power.

**Figure 1: Representation of production sectors**

**(a) Sectors other than the electricity sector**

<!-- Start of picture text --> (b) Electricity sector<br><!-- End of picture text --> 6
In the household sector, a representative household is assumed to maximize its utility from the consumption of various goods and services. The utility function is represented by a Cobb-Douglas functional form. The total household consumption is determined through an exogenously specified marginal propensity to consumption. Household income includes labor wages, capital rents, and transfer payments from the government and abroad. The remainder of the disposable income is allocated between household savings and transfers to other agents. The transfer coefficients are exogenously specified.

The government's income is composed of various taxes (i.e. personal income taxes, corporate income taxes, indirect taxes, import duties, and carbon taxes) and transfer payments from other agents. The ratios of total government revenue and the total government expenditure are fixed. Similarly, the ratios between the total government revenues and transfers to various agents are also fixed. The government saving is determined by subtracting the total government expenditures as well as the transfers to other agents from the total government revenues.

The enterprises generate their incomes from capital returns and transfer payments from government. They also pay corporate income taxes to the government. The remainder is retained as enterprise savings.

To model the international trade, we assumed imperfect substitution between the domestic products and their imported counterparts (Armington, 1969). Each tradable commodity is allocated between export and domestic markets following a constant elasticity of transformation (CET) function.

The model follows the Walras rule on the factor and commodity markets. Therefore, the total capital (or labor) supply equals total capital (or labor) demand. For each commodity, imports plus domestic production equals exports plus domestic consumption. Macroeconomic balance is maintained by equating total savings (i.e., household, enterprise, government, and foreign) to the total investment. The neoclassical closure rule is used in the model, which implies that labor supply at the national level is exogenous, and all factors are fully employed in the economy.

The model is made dynamic through population growth rate (i.e., labor supply growth rate), capital accumulation, total factor productivity and the technological progress represented by 7
the Autonomous Energy Efficiency Improvement (AEEI) index. The AEEI is assumed to be 1% per year, following the common assumptions in most CGE models (Chen et al. 2015). The capital accumulation is determined by the current capital stock, depreciation rate, and the new investment.

## **2.2. Data**

In this paper, the benchmark year for the CGE model is 2017. The Social Accounting Matrix (SAM) is developed using the 2017 Input-Output (IO) tables produced by the National Bureau of Statistics of China (CNBS, 2017). The CO2 emission factors of fossil fuels are calculated based on IEA statistical data (IEA,2017a). The tax data are obtained from the State Taxation Administration of China and Financial Yearbook of China (CMF, 2018). The values for the elasticity of substitution are taken from existing literature (Bao et al.2013, Cao et al. 2016). As for the baseline scenario, China's GDP growth rate from 2018 to 2021 is based on National Bureau of Statistic of China (CNBS, 2022), China's energy consumption structure (2018-2019) is based on China Energy Statistics Yearbook (CNBS, 2020) and China Electricity Statistics Yearbook (CEC, 2020). The baseline is consistent with the assumptions and forecasts made in China's Long-term Low-carbon Development Strategies and Pathways Comprehensive Report (ICCSD, 2021).

# **3. Scenario design and incorporation in the model**

## **3.1 Design of Scenarios**

We have developed one baseline scenario and six main scenarios as defined in Table 2. Two main scenarios, Scenario 4 and Scenario 6, are further divided into sub-scenarios (4a, 4b, and 6a, 6b, 6c 6d) for the reasons explained later in this section.

8
**Table 2: Definition of Scenarios**

|**Scenario**|**Sub-Scenario**|**Definition**|
|---|---|---|
|**0**||Baseline scenario|
|**1**||Command & control approach: carbon emission target (i.e., NDC target) is<br>exogenously set. The model calculates the shadow carbon prices to meet the<br>emission target|
|**2**||Carbon tax with tax revenues allocated in the same way the government<br>allocates its total revenues in the baseline|
|**3**||Carbon tax with tax revenues used to subsidize wind and solar power<br>generations. China does not provide direct subsidies to renewables.<br>However, to expedite the penetration of renewables, they might need<br>subsidies.|
|**4**|**4a**|Carbon tax with tax revenues recycled to cut labor taxes in sectors excluding<br>fossil fuels and thermal electricity generation sectors|
||**4b**|Carbon tax with tax revenues recycled to cut capital taxes in sectors<br>excludingfossil fuels and thermal electricity generation sectors|
|**5**||Carbon tax with tax revenue recycled to households as a lump-sum rebate<br>(cash transfer)|
|**6**|**6a**|Carbon tax with tax revenues recycled to cut corporate income taxes in<br>sectors except for fossil fuels and thermal electricity generation sectors; tax<br>revenues are reallocated to a sector in proportion to the amount of income<br>tax paid by it (a sector that pays a higher amount of corporate taxes gets<br>higher rebate)|
||**6b**|Similar to Scenario S6a, but tax revenues are reallocated to a sector in<br>proportion to its income tax rate (a sector with a higher income tax rate gets<br>a higher rebate)|
||**6c**|Similar to Scenario S6a, but tax revenues are reallocated to a sector based on<br>its CO2intensity ( a sector with a higher emission-intensity gets a lower<br>carbon tax revenues as the rebate)|
||**6d**|Similar to Scenario S6a, but tax revenues are reallocated to a sector based on<br>its export intensity (a sector that exports a higher volume of outputs gets a<br>higher carbon tax rebate). Fossil fuel sectors including fossil fuel-based<br>electricity generation are excluded from receivingthe tax rebate.|

The baseline scenario is constructed based on assumptions about GDP growth and population growth and the projected primary energy supply mix. The GDP growth rate for 2018-2021 is based on the National Bureau of Statistics of China (CNBS, 2022). The GDP growth rate for 2022-2035 is from ICCSD (2021). The annual average GDP growth rate is about 5.1% for the period 2017–2035. Although China's population is declining, labor 9
productivity is increasing, so we assumed that effective labor supply would neither increase nor decrease during the study period. The baseline also aligned with the primary energy supply mix projected by ICCSD (2021) and IEA (2017b).<sup>5</sup> The first scenario assumes that China will meet its NDC through administrative or regulatory policies, voluntary mechanisms, government investments toward clean fuels and technologies, and energy efficiency improvements. It could also include limited market-based mechanisms, such as the national emission standard trading scheme the country has introduced for the electricity sector. For the purpose of simulating this scenario, we constrained the CO2 emissions to the level that meets the NDC target. The shadow carbon price implied by this constraint is 117 yuan/tCO2 (Figure 2), which is approximately US$17/tCO2 using the 2020 exchange rate.

Scenario 2 considers the government uses a carbon tax and the tax revenue in the same way it does for the rest of the revenues. It means the government reallocates the carbon tax revenues for government expenditures, savings and transfers in the same proportions it does in the baseline.

Environmental advocates argue that carbon tax revenue should be used to promote clean technologies to further reduce GHG emissions. Some existing studies (e.g. Ojha et al. 2020; Timilsina et al. 2011) analyze this scenario for other countries. Therefore, we recycle carbon tax revenues to subsidize solar and wind power under Scenario 3. While designing this scenario, we were careful that there would be no over subsidization of clean power technologies. Under this scenario, the share of wind and solar in total power generation would double in 2030 from the baseline level (16% under the baseline and 33% under Scenario 3). Since the emission reduction target is fixed (NDC target) in all scenarios, the required carbon tax rates vary due to general equilibrium effects. Under Scenario 3, the required carbon tax rate would be the lowest (yuan 109/tCO2) because the dedicated use of carbon tax revenues for promoting solar and wind electricity stimulates further substitution of fossil fuels with solar and wind for electricity generation.

Using carbon tax revenue to cut existing labor and capital tax rates is one of the most

> 5 We follow IEA’s reference scenario.

10
common carbon tax revenue recycling schemes studied in the literature. This is also true in the case of recycling carbon tax revenues to households through a lump-sum (cash) transfer (see Timilsina, 2022). Scenarios 4a and 4b consider recycling tax revenues to cut existing labor and capital tax rates, respectively. Scenario 5 is designed to simulate the lump-sum transfer of carbon tax revenues to households. The required carbon tax to meet the NDC target under Scenario 5 is slightly higher than those under Scenario 4a and 4b.

**Figure 2: Shadow carbon price under Scenario 1 and carbon tax rates in the rest of the scenarios to meet the NDC (Yuan/tCO2 in 2030)**

<!-- Start of picture text --> 121<br>120<br>119<br>117 116 117 116<br>115<br>114<br>109<br>S4a S4b S6a S6b S6c S6d<br>S1 S2 S3 S4 S5 S6<br><!-- End of picture text --> Although the economic impacts of a carbon tax with tax revenue recycled to cut corporate income taxes are also analyzed in some existing studies (e.g., McKibbin et al. 2015), the variations in the economic impacts due to the criteria selected to recycle the revenue have not been investigated yet. Our study aims to fill this literature gap by further dividing Scenario 6 into four sub-scenarios: Scenarios 6a, 6b, 6c and 6d. As defined in Table 2, the carbon tax revenues are recycled to all sectors except the fossil fuel and thermal power generation sectors based on four criteria. These are: the amounts of corporate income tax paid by a sector (Scenario 6a), the rate of corporate income tax faced by a sector (Scenario 6b), the carbon intensity of a sector (Scenario 6c) and the export intensiveness of the products from a sector (Scenario 6d). The carbon tax rates under these scenarios are relatively higher except in Scenario 6c, however, their economic impacts (to be discussed later in the next section) will 11
not be necessarily high except in Scenario 6c. This implies that the relationship between the carbon tax rate and corresponding economic impacts is non-linear. The carbon tax rate under Scenario 6c is lower than in some of the other scenarios because a sector with low emission intensity gets higher carbon tax rebates. Non-fossil electricity generation technologies (solar, wind, hydro, nuclear) get more output subsidies, enabling these technologies to substitute coalfired electricity generation in China. Note that the revenue recycling schemes considered while designing the scenarios here are also applicable to an ETS if the emission allowances are distributed through auctions.

We have taken two critical precautions while designing all carbon tax scenarios where tax revenues are recycled to the economy. First, the government revenue is kept neutral, meaning that only the revenue in excess of total government revenue in the baseline is recycled. This is a common principle in most existing carbon tax studies. The rationale behind this is that a carbon tax causes a contraction in the economy, and government revenues excluding carbon tax revenues will drop. It could adversely impact government budgets and public expenditures. Moreover, the governments eye on increasing government revenues when a new tax (here carbon tax) is introduced. Therefore, government revenues under the carbon tax should be at least equal to that in the absence of the carbon tax (i.e., baseline). Second, the objective of a carbon tax is to reduce GHG emissions. Recycling carbon tax revenues to fossil fuel industries through any channel (e.g., cutting corporate income taxes, cutting factor tax burden) will compromise this objective due to the rebound effect. Existing studies, such as Timilsina et al. (2021) have demonstrated it. Therefore, we have excluded fossil fuel industries and the thermal power generation industry from receiving any tax rebate under all carbon tax scenarios where tax revenues are recycled to the economy.

# **3.2 Incorporation of the scenarios into the CGE model**

# **_Incorporating Scenario 1 (NDC standards) and Scenario 2 (carbon tax with revenue_**

**_recycling like in the case of baseline)_ :** Modeling Scenarios 1 and 2 is straightforward and already captured in the base model. Under the scenario 1, total CO2 emission is estimated by setting the emission intensity at the NDC target level. The shadow carbon prices are

12
endogenously calculated. The government does not receive carbon tax revenue. In scenario 2, a carbon tax is introduced at the needed level to achieve the emission intensity target. The carbon tax revenue is retained in the government budget to improve the fiscal position. Below we discuss how other scenarios are incorporated into the model.

**_Incorporating Scenario 3 (recycling carbon tax revenues to stimulate wind and solar power):_** To incorporate this scenario, the equations used to represent the production function of solar and wind electricity generation sectors are modified as follows:

where _ws_ is the set to indicate solar power and wind power sectors, QEA is total activity output, _PEA_ is total activity price , tcinde is the rate of indirect tax paid to the government, QEKDis capital inputs, RPS is capital rental rate, QELMEL is composite of labor and intermediate input,PELMEL is composite of labor and intermediate input price, YG is government’s income including the carbon tax revenue, whileYGbauis government’s income in the baseline. (i.e., before the introduction of the carbon tax). ps _h_ arewsis the proportion allocated to ws sectors. Wind power and solar power sectors get output rebates based on their outputs in this scenario, so theps _h_ arewsis given by the following equation: Moreover, the government savings is replaced by the following equation as government revenue is maintained constant: GSAVt = YGbaut −EGt (4) where GSAVis government saving and EG is government expenditure.

# **_Incorporating Scenario 4 (recycling carbon tax revenues to cut labor and capital taxes):_**

Equations 5-8 illustrate how the carbon tax revenues are recycled to cut labor tax in the nonenergy and non-fossil fuel-based electricity generation sectors. Note that the fossil fuel sectors and fossil fuel using electricity generation sectors do not have carbon tax revenues to cut their labor taxes to avoid a rebound effect.

13
PKLne,t ⋅QKLne,t = RPSne,t ⋅QKDne,t + WLne,t ⋅QLDne,t −REVGAPt ⋅psharene,t PELMELnfee,t ⋅QELMELnfee,t = WLnfee,t ⋅QELDnfee,t + PEMELnfee,t ⋅QEMELnfee,t −REVGAPt ⋅psharenfee,t (6) Under this scenario, the ps _h_ are parameter is described as follows: where _ne_ is a subset of non-fossil energy sectors in non-electrical sectors, QKL is the composite of capital and labor input of non-electrical sectors, PKL is the composite of capital and labor input price of non-electrical sectors, QKD is the capital input of non-electrical sectors, QLD is labor input of non-electrical sectors, WL is wage rate, nfee is a subset of nonthermal power sectors in electrical sectors, QELMELis composite of labor and intermediate input of electrical sectors, PELMEL is composite of labor and intermediate input price of electrical sectors, QELD is labor inputs of electrical sectors, QEMEL is aggregated intermediate input of electrical sectors, PEMEL is aggregated intermediate input price of electrical sectors.

Equations 9-11 describe the modeling of recycling carbon tax revenues to cut capital taxes. PEAnfee,t ⋅QEAnfee,t ⋅(1 −tcindenfee) = RPSnfee,t ⋅QEKDnfee,t −REVGAPt Under this scenario, the _pshar e_ is defined as:

# **_Incorporating Scenario 5 (recycling of carbon revenues to households through a lump-_**

**_sum transfer)_ :** Equation 12 incorporates this scenario to the model where the excess government revenue is directly added to household income.

14
∑ee RPSee,t ⋅QEKDee,t + EXR⋅TSWTOHt + TSGTOHt + REVGAPt (12) where YGis households’ income, EXRis exchange rate, TSWTOHis transfer payments from foreign countries to households, TSGTOH is transfer payments from the government to the household.

# **_Incorporating Scenario 6 (recycling carbon tax revenues to cut corporate income taxes):_**

Under this scenario, all sectors, except fossil fuel sectors and the thermal power generation sector, receive carbon tax revenue to cut their corporate income taxes. The new corporate income tax is calculated as follows: where the NEWitax is the new corporate income tax rate of non-electrical sector, NEWitaxe is the new corporate income tax rate of electrical sector, Xtind is the reduction of tax rate, itax anditaxe are the corporate income tax rates of the non-electrical sector and electrical sectors in the baseline scenario, respectively.

Under this scenario, there are four sub-scenarios depending on how the carbon tax revenues are recycled to the targeted sectors, as defined in Table 2. In the first sub-scenario (6a), carbon tax revenues are recycled to the sectors in proportion to the volume of income tax they pay. It is described in Equations 16 and 17 by changing the ps _h_ are parameter is follows: In the second case (Scenario 6b), carbon tax revenues are recycled to the targeted sectors in proportion to their income tax rates (instead of income tax volume in sub-scenario 6a). The ps _h_ are parameter is calculated as follows: In the third case (Scenario 6c), the carbon tax revenues are recycled to the targeted sectors 15
in the reverse order of their emission intensities. It means a sector with high emission intensity gets low carbon tax rebates. Here parameter _pshar e_ is calculated as follows: where CIne is the carbon intensity of ne sectors, CInfee is the carbon intensity of nfee sectors, CIsumis the sum of CIne and CInfee.

In the last case (Scenario 6d), the carbon tax revenues are recycled based on the export intensity of sectoral outputs. To incorporate this scenario, the parameter ps _h_ are is calculated as: where QEiis export by sector i.

# **4. Results and Discussion**

## **4.1 Overall economic impacts**

Figure 3 illustrates the impacts of GDP under the different scenarios considered for the study. If the NDC target were to meet without market mechanisms (Scenario 1) it would cause about 1% loss of GDP in 2030. In the absence of market mechanisms, the government might implement administrative or regulatory measures, direct investments to reduce GHG emissions. As expected, these measures are less flexible and do not allow the market to exploit the cheapest options to reduce GHG emissions. The standard economic theory suggests that command and control policies are always less economical than market-based instruments for reducing environmental externalities (Tietenberg, 2006). Although the GDP losses due to carbon taxes are relatively small (less than 1%), they are large in terms of absolute amount due to the large size of China’s GDP.

16
**Figure 3. GDP impacts of meeting NDC target under various scenarios (% reduction of GDP in 2030 from the baseline)**

<!-- Start of picture text --> S1 S2 S3 S4 S5 S6<br>S4a S4b S6a S6b S6c S6d<br>-0.143 -0.116 -0.143 -0.153 -0.113<br>-0.185 -0.183 -0.182<br>-0.534<br>-0.954<br><!-- End of picture text --> Figure 3 shows that carbon tax, a market-based mechanism, is always more economical as compared to the command and control approach as GDP losses in all carbon tax scenarios (S2 – S6) are lower than that in Scenario 1. GDP loss of carbon tax would be the lowest when more carbon tax revenue is recycled to export-intensive non-energy firms to cut their corporate income taxes (Scenario 6d). The negative GDP impact of the carbon tax is one of the lowest when carbon tax revenues are used to cut capital taxes (Scenario S4b). The results from these two scenarios (i.e., Scenarios 6d and 4b) reveal that tax paid by the firms, either in the form of corporate income tax rates or taxes for their capital inputs, causes the most distortion in the economy. Recycling carbon tax revenues to cut these taxes lowers the costs of carbon taxes. This can be interpreted as the double dividend of a carbon tax (Goulder, 1995; Parry et al. 1999; Parry and Bento, 2000). The existence of double dividends is influenced by the level of distortions caused by the existing taxes and the ability of the carbon tax to offset the distortions by recycling the carbon tax revenues (Aldy et al. 2010). These findings are also similar to some existing studies (e.g., Bohringer et al. 2017; Gray and Metcalf, 2017), which show output-based rebating for emission-intensive and trade-exposed (EITE) sectors would be beneficial for a country like China with an energy and export intensive economy.

Our results suggest that a carbon tax with tax revenue recycled to subsidize clean technologies (Scenario 3), here solar and wind, would be the most expensive among the revenue recycling alternatives considered, although it would still be cheaper as compared to 17
command and control approach (Scenario 1). The results of our study further strengthen the findings of the literature as summarized in a review article by Timilsina (2022). Based on carbon tax studies published since 1990, Timilsina (2022) presents detailed discussions on the economic impacts of a carbon tax under alternative schemes of revenue recycling. It concludes that recycling carbon tax revenue to households through a lump-sum transfer would be more economical (i.e., causes lower loss in welfare and GDP) than when the government uses revenue like in the status quo. Recycling carbon tax revenues to cut labor and capital tax would be further economic. Recycling carbon tax revenues to cut corporate income taxes or investing on productive sectors would be most economical as it would cause the lowest GDP loss or even cause GDP gain (McKibbin et al. 2015). A carbon tax with tax revenue recycled for subsidizing clean or green technologies to encourage a further reduction of GHGs and local air pollutants would cause higher economic losses.

Our study brings additional insights to the existing literature as it goes one step further to investigate the sensitivity of criteria used for recycling carbon tax revenue to a given purpose. For example, many studies consider recycling tax revenues to cut corporate income taxes, but they do not specify how individual sectors receive back the carbon tax revenues. As specified in the preceding section (Table 2), we consider four criteria to recycle the tax revenues to cut corporate income taxes. One critical insight is that using the carbon tax revenues to cut corporate income taxes does not always cause the carbon tax efficient. We find that when the carbon tax revenues are reallocated to non-energy firms based on their CO2 emission intensities to cut their corporate income taxes, it will cause higher economic loss (Scenario 6c). When carbon tax revenue is recycled to non-energy sectors based on their corporate income tax rates (Scenario 6b), the GDP loss caused by the carbon tax would be higher than that when carbon tax revenues are used to cut existing labor and capital taxes (Scenario 4a and 4b). Therefore, while designing a carbon tax, policy makers should consider not only where in the economy the carbon tax revenue is utilized, but also how the tax revenue is relocated for the targeted purpose.

18
# **4.2 Impacts on production sectors**

The impacts of meeting China’s NDC through the regulatory and carbon tax policies under alternative schemes of revenue recycling are presented in Table 3. There would be a loss in total gross output under all scenarios. It is interesting to note that the policies affect the GDP and total gross output differently. While losses in GDP and total gross output would be the lowest under Scenario 6d, the loss of total gross output would be highest under Scenario 2. Note that loss of GDP is highest under Scenario 1. The reason is that the impacts on GDP are influenced by impacts on final demands for goods and services and impacts on international trade, whereas impacts on gross output are influenced by the impacts on intermediate goods and factors.

The impacts of the carbon tax in a sector vary across the scenarios and the impacts under a scenario vary across the sectors. The variations depend on several factors, such as the carbon intensity of a sector, labor and capital tax burdens a sector has, its corporate income tax rate and exports share of commodities produced from a sector. The impacts in a sector under different scenarios are not different only in magnitudes but also in direction. For example, agriculture sector outputs drop under all scenarios except Scenario S4a where the use of carbon tax revenues to cut labor tax provides a lot of relief because the agriculture sector is laborintensive. The outputs from non-fossil fuel electricity sectors increase under all scenarios as the carbon tax causes significant substitution of fossil fuels with non-fossil fuels in the electricity sector. There would be a large expansion of solar and wind power under Scenario 3 when carbon tax revenues are used to subsidize these technologies. Solar and wind power generations also get expanded under Scenario 6c when less carbon-intensive sectors get higher carbon tax revenues to cut their corporate income taxes.

**Table 3: Impacts on sectoral outputs in 2030 (% change from the baseline)**

|**Sector**|**S1**|**S2**|**S3**|**S4**||**S5**||**S6**|||
|---|---|---|---|---|---|---|---|---|---|---|
|||||S4a|S4b||S6a|S6b|S6c|S6d|
|Agriculture|0.01|-0.02|-0.26|0.57|-0.08|0.49|-0.49|-0.41|-0.46|-1.04|
|Coal mining|-13.04|-13.02|-12.96|-13.21|-13.23|-13.12|-13.36|-13.36|-13.32|-13.29|
|Oil and gas|-0.79|-0.65|-0.69|-0.74|0.10|-0.57|-0.81|1.42|1.66|-1.90|
|Other mining|-1.62|-1.98|-1.81|-1.89|-1.64|-1.95|-2.18|-1.57|-1.85|-1.85|
|Food & tobacco|-0.38|-0.32|-0.42|0.26|-0.02|0.28|0.34|0.37|0.32|-0.37|

19
|Textile|-0.41|-0.36|-0.53|0.05|-0.31|0.03|-0.93|-0.29|-0.51|-0.81|
|---|---|---|---|---|---|---|---|---|---|---|
|Wood & Furn.|0.14|-0.34|-0.89|-0.01|-0.20|-0.17|-0.76|-0.42|-0.45|-1.15|
|Pulp & paper|-1.08|-0.85|-0.99|-0.87|-0.78|-0.84|-1.18|-0.58|-1.16|-1.14|
|Oil refinery|-3.25|-3.26|-3.25|-3.17|-3.10|-3.16|-3.13|-3.00|-2.96|-3.34|
|Chemicals|-1.64|-1.61|-1.62|-1.52|-1.38|-1.51|-1.76|-1.67|-1.83|-1.72|
|Non-metals|-0.15|-1.00|-1.76|-0.70|-0.71|-0.95|-0.52|-0.45|-0.62|-1.19|
|Primary metals|-1.24|-1.60|-1.77|-1.49|-1.16|-1.55|-1.58|-1.58|-1.49|-0.24|
|Fab. metals|-0.78|-1.15|-1.30|-1.03|-0.83|-1.12|-1.20|-0.90|-0.79|-0.54|
|Machinery|-0.67|-0.79|-0.95|-0.73|-0.22|-0.70|-0.71|-1.06|-0.67|3.12|
|Other manuf.|-1.08|-0.87|-0.64|-0.88|-0.86|-0.87|-0.40|1.98|1.90|-0.25|
|Gas utility|-4.95|-4.75|-4.51|-4.53|-4.52|-4.37|-2.99|-2.89|-2.79|-3.55|
|Construction|1.23|0.04|-1.20|0.45|0.22|0.07|0.84|0.90|0.62|-0.72|
|Trade|-0.12|-0.15|-0.45|0.14|0.22|0.14|0.58|0.41|0.46|0.29|
|Transport|-0.46|-0.40|-0.65|-0.28|-0.08|-0.27|-0.45|-0.50|-0.55|-0.65|
|Other services|-0.07|0.46|0.15|0.08|0.11|0.07|0.21|0.16|0.05|-0.51|
|Thermal elec.|-7.65|-7.62|-8.07|-7.74|-7.76|-7.59|-8.02|-8.44|-8.82|-7.77|
|Hydro|7.88|7.61|7.31|8.26|8.71|7.96|8.73|10.41|10.27|8.35|
|Nuclear|13.08|12.42|15.48|13.50|13.19|12.92|17.47|17.87|17.95|17.83|
|Wind|1.06|0.84|171.10|1.20|1.16|1.08|3.34|11.47|38.44|3.18|
|Solar|1.66|1.59|141.72|1.87|2.43|1.79|2.30|5.29|36.98|2.28|
|Other elec.|8.30|8.03|8.19|8.71|9.22|8.40|8.89|11.58|11.24|8.53|
|Total|-0.49|-0.53|-0.31|-0.45|-0.37|-0.49|-0.44|-0.44|-0.35|-0.05|

# **4.3 Impacts on households**

# **_4.3.1 Household income_**

Impacts of household income or disposable income can also measure household welfare. Like in the case of GDP impact, Scenario 1 causes the largest reduction in household income. There would be gains in household income under Scenarios 5 and 6. The lump-sum transfer of carbon tax revenues to households is the primary reason for household income rise under Scenario 5. The reasons for household income rise under Scenario 6 are not straightforward. Under Scenario 6, wage rates increase in all sectors, whereas they decrease under other scenarios. Similarly, the capital prices increase under this scenario in all sectors except fossil fuel and emission-intensive sectors. Capital prices fall in most of the sectors under other scenarios. The increases in capital prices and wage rates raise household income.

20
**Figure 4. Impacts on the household income of meeting NDC target under various scenarios (% reduction in disposable income in 2030 from the baseline)**

<!-- Start of picture text --> S1 S2 S3 S4 S5 S6<br>S4a S4b S6a S6b S6c S6d<br>0.88 0.87<br>0.81<br>0.02 0.02<br>-0.08 -0.12<br>-0.67<br>-0.79<br>-0.93<br><!-- End of picture text -->

# **_4.3.2 Household consumption_**

The total consumption of goods and services would increase under Scenario 5, 4a and all sub scenarios under Scenario 6. The total household consumption increases under Scenario 5 due to the lump-sum transfer of carbon tax revenues to the households. The labor tax cut under scenario 4a also increases real household income and therefore raises household consumption. Household consumption would be the highest under Scenario 6 for a reason explained earlier.

The impacts on household consumption of individual goods and services vary significantly across the scenarios and across the goods/services (Table 4). Consumption of fossil fuels decreases under all scenarios, including where total household income increases (Scenarios 5 and 6). Electricity consumption decreases in all scenarios except Scenarios 3 and 6c where substitution of fossil fuels with renewables causes electricity prices to be smaller as compared to those in the rest of the scenarios. Household consumption of energy-intensive chemical products also decreases under all scenarios. The degree of change in household consumption depends upon these products' emission intensities and the interactions of alternative scenarios with various variables such as factor prices, real household income, and share of particular goods/services in total household expenditures.

21
**Table 4: Impacts on household consumptions in 2030 (% change from the baseline)**

|**Sector**|**S1**|**S2**|**S3**|**S4**<br>|<br>|**S5**||**S6**<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|||||S4a|S4b||S6a|S6b|S6c|S6d|
|Agriculture|-0.34|-0.31|-0.35|0.49|-0.07|0.49|0.12|0.13|0.10|-0.65|
|Coal|-4.20|-3.98|-3.62|-3.69|-3.74|-3.28|-2.66|-2.59|-2.47|-3.59|
|Food & tobacco|-0.57|-0.48|-0.52|0.25|-0.01|0.30|0.66|0.69|0.63|-0.26|
|Textile|-0.77|-0.67|-0.63|0.02|-0.16|0.09|0.34|0.59|0.47|-0.25|
|Wood & Furn.|-0.86|-0.71|-0.56|-0.02|-0.15|0.07|0.26|0.43|0.44|-0.47|
|Pulp & paper|-1.36|-1.22|-1.07|-0.55|-0.55|-0.43|0.10|0.48|0.09|-0.49|
|Petroleum|-3.63|-3.40|-3.13|-2.87|-2.88|-2.69|-2.01|-1.88|-1.82|-2.99|
|Chemicals|-1.94|-1.76|-1.43|-1.12|-1.07|-1.00|-0.41|-0.38|-0.54|-1.41|
|Non-metals|-3.64|-3.31|-2.69|-2.75|-2.62|-2.58|-2.24|-2.07|-2.16|-3.19|
|Primary metals|-1.69|-1.45|-0.89|-0.82|-0.77|-0.67|-0.22|0.18|0.23|-1.02|
|Machinery|-1.15|-0.93|-0.78|-0.28|-0.21|-0.14|0.58|0.49|0.54|0.68|
|Other manuf.|-1.42|-1.27|-0.98|-0.60|-0.66|-0.49|0.61|2.45|2.34|-0.02|
|Gas|-3.20|-3.01|-2.76|-2.56|-2.58|-2.30|-0.36|-0.23|-0.17|-1.32|
|Trade|-0.35|-0.21|-0.41|0.46|0.50|0.57|1.58|1.32|1.37|0.36|
|Transport|-0.94|-0.76|-0.91|-0.13|0.13|0.03|0.25|0.17|0.04|-0.82|
|Other services|-0.47|-0.42|-0.56|0.35|0.36|0.46|1.94|1.88|1.73|0.66|
|Electricity|-6.59|-6.29|14.53|-5.92|-5.97|-5.66|-5.00|-3.82|0.65|-6.07|
|**Total**|**-0.76**|**-0.66**|**-0.46**|**0.06**|**-0.01**|**0.16**|**1.03**|**1.01**|**0.98**|**0.04**|

# **4.4. Impacts on international trade**

The impacts on international trade of meeting China’s NDC through the alternative policy instruments considered in this study are presented in Figure 5. The total exports would decrease under all scenarios except Scenario 6d, where export-intensive sectors are incentivized through carbon tax revenues. The higher a sector exports its outputs, the higher carbon tax revenues it receives to cut its corporate income taxes. The other three sub-scenarios of Scenario 6 (Scenarios 6a, 6b and 6c) would experience relatively higher reductions in exports because more domestic outputs are needed for meeting domestic demand as their imports decrease for the reasons explained later.

The impacts on total imports are significantly different across the scenarios. There would be reductions in total imports under Scenarios 1, 2, 3 and 5. Imports are found to increase under Scenarios 4 and 6, at relatively higher. To understand the reasons for total imports change, 22
we need to dig upon imports of individual commodities which are displayed in Table 5. A carbon tax increases the prices of goods and services, particularly fossil fuels and fossil fuelintensive products. While the price of a fossil fuel increases at the same proportion irrespective of its origin – domestically produced or imported, domestically produced fossil fuel (or emission) intensive products would be relatively expensive as compared to their imported counterparts. It would increase imports of emission-intensive products. Table 5 shows increased imports of most emission-intensive products (e.g., pulp & paper, chemicals, nonmetals and primary metals) under all scenarios. However, these increased imports are offset by decreased imports of fossil fuels and low-emission intensive products in all scenarios except in Scenario 6.

Under Scenario 6, the required rates of carbon tax are bigger (except in Scenario 6c) than that in other scenarios (see Figure 2). It will cause imports of commodities cheaper as compared to their domestic counterparts. As displayed in table 6, imports of most goods and services are higher under Scenario 6 than those under other scenarios. Therefore, total imports increase under this scenario. For the same reason, the drops in exports are relatively higher under Scenario 6a, 6b and 6c.

**Figure 5. Impacts on international trade of meeting NDC target under various scenarios (% change in imports and exports in 2030 from the baseline)**

<!-- Start of picture text --> 2.44<br>Imports Exports<br>1.07<br>0.90 0.91 0.86<br>0.03 0.14<br>-0.14 -0.03<br>-0.26<br>-0.45<br>-1.05<br>-1.74 -1.60 -1.49 -1.71 -1.58<br>-2.09<br>-2.37<br>-2.49<br>S4a S4b S6a S6b S6c S6d<br>S1 S2 S3 S4 S5 S6<br><!-- End of picture text --> 23
**Table 5: Impacts on imports and exports of goods and services in 2030 (% change from the baseline)**

|**Sector**|**S1**|**S2**|**S3**|**S4**||**S5**||**S**|**6**||
|---|---|---|---|---|---|---|---|---|---|---|
|||||S4a|S4b||S6a|S6b|S6c|S6d|
|||||<br>Imp|<br>orts||||||
|Agriculture|-2.1|-1.7|-1.4|-1.4|-0.3|-1.2|2.2|2.3|2.1|1.4|
|Coal|-2.3|-2.6|-3.4|-1.4|-1.4|-2.4|-2.0|-2.3|-2.8|-1.5|
|Oil and gas|-3.9|-4.0|-3.3|-3.7|-4.1|-3.8|-3.2|-4.6|-4.6|-2.7|
|Other mining|0.8|0.2|-1.0|0.5|0.7|0.3|1.1|0.3|0.9|2.4|
|Food & tobacco|-1.6|-1.3|-0.9|-0.8|-0.4|-0.6|1.1|0.9|0.9|0.6|
|Textile|-1.0|-0.8|-0.7|-0.4|-0.1|-0.3|1.2|0.8|0.9|0.3|
|Wood & Furn.|-0.1|-0.7|-1.3|-0.2|-0.1|-0.4|1.6|1.3|1.0|0.8|
|Pulp & paper|0.7|0.9|0.6|1.0|1.0|1.0|2.0|1.0|1.8|0.9|
|Petroleum|3.4|3.1|2.7|3.6|3.6|3.4|3.9|3.6|3.4|4.0|
|Chemicals|2.5|2.4|1.5|2.8|2.5|2.6|3.5|3.4|3.7|4.2|
|Non-metals|13.0|11.0|7.7|12.0|11.1|11.3|14.4|13.5|13.5|14.2|
|Primary metals|4.4|3.6|1.8|4.0|3.8|3.8|5.0|4.4|4.6|7.8|
|Fab. metals|2.1|1.4|-0.5|1.8|1.6|1.5|2.9|1.7|1.4|3.4|
|Machinery|0.5|0.0|-0.4|0.3|0.2|0.1|0.8|0.9|0.8|-0.4|
|Other manuf.|0.9|1.1|0.6|1.2|1.3|1.2|0.7|-4.3|-4.1|-0.1|
|Construction|2.3|1.0|-0.5|1.5|1.3|1.1|2.2|2.1|2.0|1.1|
|Trade|-1.4|-1.4|-1.0|-1.1|-1.2|-1.1|-1.0|-0.6|-0.8|-0.5|
|Transport|-0.4|-0.5|-0.1|-0.2|-0.6|-0.3|0.9|1.0|1.1|1.2|
|Other services|-1.0|-0.3|-0.1|-0.8|-0.9|-0.9|-2.0|-1.9|-1.8|-1.8|
|Electricity|16.1|15.4|6.7|16.6|16.8|16.0|17.2|15.4|11.8|18.2|
|||||Exp|orts||||||
|Agriculture|2.5|2.1|1.1|3.0|0.1|2.5|-3.7|-3.5|-3.4|-3.8|
|Coal|-26.8|-26.4|-25.4|-28.1|-28.2|-26.9|-27.9|-27.5|-26.8|-28.2|
|Oil and gas|1.1|1.4|0.9|1.1|2.8|1.4|0.7|5.3|5.7|-1.4|
|Other mining|-5.1|-5.2|-3.0|-5.3|-5.1|-5.3|-6.9|-4.3|-5.8|-7.9|
|Food & tobacco|1.6|1.4|0.4|2.0|0.6|1.8|-0.8|-0.6|-0.6|-1.9|
|Textile|0.3|0.2|-0.3|0.5|-0.5|0.3|-3.3|-1.5|-2.0|-2.0|
|Wood & Furn.|0.4|0.0|-0.4|0.3|-0.3|0.1|-3.5|-2.4|-2.1|-3.4|
|Pulp & paper|-2.9|-2.7|-2.7|-2.9|-2.6|-2.8|-4.5|-2.2|-4.2|-3.3|
|Oil refinery|-13.1|-12.7|-12.2|-13.2|-13.0|-12.9|-13.4|-12.8|-12.4|-14.1|
|Chemicals|-5.7|-5.5|-4.7|-5.7|-5.2|-5.6|-6.8|-6.6|-7.1|-7.4|
|Non-metals|-12.0|-11.9|-10.5|-12.1|-11.5|-12.1|-13.7|-12.9|-13.2|-14.8|
|Primary metals|-8.5|-8.3|-6.4|-8.6|-7.6|-8.4|-10.0|-9.3|-9.3|-10.3|
|Fab. metals|-3.8|-3.8|-2.2|-4.0|-3.4|-3.9|-5.5|-3.6|-3.1|-4.7|
|Machinery|-1.5|-1.3|-1.4|-1.5|-0.5|-1.3|-1.8|-2.4|-1.7|5.6|
|Other manuf.|-3.0|-2.7|-1.8|-2.9|-3.0|-2.8|-1.5|8.3|8.0|-0.4|
|Construction|-0.9|-1.8|-2.6|-1.5|-2.0|-1.9|-1.9|-1.5|-2.0|-4.2|
|Trade|1.6|1.6|0.3|1.7|2.0|1.8|2.6|1.7|2.1|1.3|

24
|Transport|-0.5|-0.3|-1.3|-0.4|0.6|-0.2|-2.2|-2.5|-2.7|-3.1|
|---|---|---|---|---|---|---|---|---|---|---|
|Other services|1.4|1.6|0.5|1.4|1.6|1.4|3.5|3.2|2.8|1.5|
|Electricity|-7.0|-6.9|279.4|-7.0|-6.8|-6.8|-5.9|0.0|34.9|-6.3|

# **4.5 Impacts on emissions**

Although the NDC target of China, which was set in terms of CO2 emission intensity of the economy, by 65% below in 2030 from its 2005 level, is fixed, the reductions of CO2 emissions vary across the scenarios (Figure 6). The variations are caused by two factors. First, the total demand for fossil fuels and their mixes would be different across the scenarios due to different general equilibrium effects. Secondly, although targeted emission intensity in 2030 is the same across the scenarios, GDP will be different, again due to differing general equilibrium effects. However, the differences in percentage reductions of total CO2 emission across the scenarios are not large, vary from 10.4% under Scenario 1 to 10.7% under Scenario S6c. Since the required carbon tax rates are higher under Scenario 6, it causes higher substitution of domestic production by imports, and therefore, emission reductions would be higher.

**Figure 6. Impacts on CO2 emissions while meeting NDC target under various scenarios (% change in CO2 emissions in 2030 from the baseline)**

<!-- Start of picture text -->
-10.44<br>-10.46<br>-10.49<br>-10.50 -10.50<br>-10.53<br>-10.56<br>-10.65<br>-10.66 -10.67<br>S4a S4b S6a S6b S6c S6d<br>S1 S2 S3 S4 S5 S6<br><!-- End of picture text -->

There are larger variations in emission reductions across individual sectors for a given scenario. However, emission reductions from a sector do not vary much across the scenarios. Sectoral emissions reductions under all scenarios are presented in Table 6. As expected, coal 25
and electricity generation would experience the highest percentage reductions in CO2 emissions. This is followed by oil refinery, gas utility, chemicals, non-metallic minerals, primary metals, pulp & paper food & tobacco and textile sectors as these are relatively emission-intensive sectors in China.

**Table 6: Impacts on sectoral CO2 emissions in 2030 (% change from the baseline)**

|**Sector**|**S1**|**S2**|**S3**|**S**|**4**|**S5**||**S**|**6**||
|---|---|---|---|---|---|---|---|---|---|---|
|||||S4a|S4b||S6a|S6b|S6c|S6d|
|Agriculture|-4.77|-4.67|-4.42|-4.24|-4.74|-4.27|-5.00|-4.84|-4.73|-5.60|
|Coal mining|-18.85|-18.72|-18.40|-18.92|-18.99|-18.91|-19.23|-19.18|-19.04|-19.20|
|Oil and gas|-5.79|-5.56|-5.05|-5.74|-5.00|-5.57|-5.91|-3.81|-3.40|-6.94|
|Other mining|-6.51|-6.76|-6.30|-6.77|-6.57|-6.83|-7.13|-6.57|-6.61|-6.79|
|Food & tobacco|-7.19|-6.98|-6.51|-6.57|-6.82|-6.54|-6.54|-6.43|-6.26|-7.25|
|Textile|-7.12|-6.92|-6.52|-6.67|-7.01|-6.67|-7.63|-7.00|-6.97|-7.63|
|Wood & Furn.|-5.51|-5.85|-5.84|-5.64|-5.84|-5.78|-6.37|-6.00|-5.83|-6.81|
|Pulp & paper|-7.85|-7.50|-7.14|-7.63|-7.61|-7.61|-8.05|-7.48|-7.76|-8.13|
|Oil refinery|-9.35|-9.24|-8.96|-9.25|-9.23|-9.25|-9.33|-9.18|-9.01|-9.55|
|Chemicals|-7.34|-7.20|-6.88|-7.21|-7.15|-7.21|-7.49|-7.34|-7.30|-7.48|
|Non-metals|-6.56|-7.25|-7.67|-7.07|-7.18|-7.32|-6.95|-6.85|-6.81|-7.61|
|Primary metals|-7.28|-7.51|-7.36|-7.51|-7.28|-7.58|-7.66|-7.61|-7.34|-6.37|
|Fab. metals|-5.49|-5.76|-5.46|-5.73|-5.60|-5.82|-5.90|-5.61|-5.34|-5.35|
|Machinery|-5.36|-5.40|-5.03|-5.43|-4.99|-5.40|-5.43|-5.66|-5.11|-2.02|
|Other manuf.|-6.22|-5.91|-5.26|-6.02|-6.03|-6.01|-5.65|-3.68|-3.56|-5.61|
|Gas utility|-9.36|-9.08|-8.62|-8.91|-8.94|-8.79|-7.67|-7.55|-7.35|-8.23|
|Construction|-1.36|-2.47|-3.39|-2.13|-2.35|-2.49|-1.81|-1.70|-1.84|-3.32|
|Trade|-3.85|-3.80|-3.57|-3.60|-3.56|-3.59|-3.07|-3.10|-2.91|-3.32|
|Transport|-3.17|-3.05|-3.05|-2.99|-2.88|-2.98|-2.95|-2.92|-2.87|-3.13|
|Other services|-4.15|-3.53|-3.40|-4.00|-4.00|-4.00|-4.06|-4.03|-3.96|-4.70|
|Electricity|-16.82|-16.67|-17.13|-16.87|-16.93|-16.79|-17.19|-17.58|-17.95|-16.89|

# **5. Conclusions and policy implications**

China is committed, as specified in its Nationally Determined Contributions, to reduce its emission intensity of the economy by 65% below its 2005 level by 2030. Both market (carbon pricing) and non-market (i.e., regulatory or administrative) policy instruments are being considered to achieve the NDC targets. An emission trading scheme (ETS) has already 26
been introduced for the electricity sector as a carbon pricing policy. Other carbon pricing instruments, such as carbon tax, could be another policy instrument to achieve its NDC. However, the government’s interest in enacting a carbon tax might be influenced by several factors, including its overall costs to the economy. This study assesses the general equilibrium effects of a carbon tax policy with several alternatives for carbon tax revenue recycling. It also compares the economic impacts of the carbon tax with those when the NDC will be met through an administrative/regulatory mandate on a national CO2 emission standard. We use a computable general equilibrium model of China calibrated with the most recent data (2017 Social Accounting Matrix) to simulate the carbon tax and emission standard policies.

The study finds that Chinese GDP in 2030 would be about 1% lower than that in the baseline if the country constraints its national CO2 emissions at its NDC target without specifying any policy measures. Emitters reduce their GHG emissions the way they find most economical, such as cutting their production, switching to cleaner fuels and technologies, deploying energy-efficient processes and devices, etc. If a carbon tax is introduced, it will cause lower economic costs no matter how the carbon tax revenue is recycled into the economy. Among the nine alternative design architectures considered for the carbon tax in terms of its revenue recycling, the economic costs would be lowest when the tax revenue is recycled to non-energy/electricity sectors, in proportion to the export intensiveness of their outputs, to cut their corporate income taxes. Recycling tax revenues to cut capital taxes would be the next better scheme. Recycling the carbon tax revenues to subsidize solar and wind electricity would be instrumental in promoting these clean technologies; however, doing so is the most expensive carbon tax design scheme considered in the study.

Several policy implications can be drawn from the analysis. First, the economic impacts of a carbon tax are sensitive to its design architecture, particularly the use of carbon tax revenues. There are many alternatives to recycling tax revenues; however recycling the revenues to reduce distortions faced by the production sectors (e.g., taxes on corporate income, taxes on capital rents) causes the carbon tax to be more efficient. One critical policy insight drawn from the study is that the implications of revenue recycling on costs of a carbon tax depend on the criteria chosen to recycle the revenues. Our study shows that when the carbon tax revenues are recycled to cut corporate income taxes, the economic costs of carbon are 27
significantly influenced by the criteria used to allocate the tax revenues. While recycling the carbon tax revenues to cut the corporate income tax of non-energy sectors, the cost of carbon tax would be lowest (0.11% GDP loss) when the export intensity of sectoral outputs is used as the criteria to recycle the revenues. On the other hand, if the carbon tax revenues are recycled to the same sectors, again to cut their corporate tax rates based on their emission intensity, the costs of the carbon tax would be significantly higher (0.18% GDP loss). Thus, while designing a carbon tax it is not only important where the carbon tax revenue is recycled, but also how it is recycled.

One important aspect missing in this analysis is the distributional impacts of the carbon tax. It refers to the impacts of a carbon tax across households with different income levels. For policy makers, it would be crucial to understand how the impacts of a carbon tax with a given design architecture vary across the different income-level households. Similarly, it would be important to understand how the impacts of a carbon tax on a given income level household vary across the alternative tax design schemes. These investigations will be natural extensions of the current analysis when the necessary data are available.

Many issues are beyond the scope of this analysis and could be interesting to consider in future studies. For example, the model does not capture the labor-leisure trade-off or informality. It does not explicitly capture induced technical change, innovation spillovers, and network effects. Behavioral barriers would be important while analyzing the impacts of a carbon tax on renewables as they influence the relative cost of recycling to renewables. Other distortions include energy price rigidities and other regulation/state interference in the market economy. These distortions are ignored due to the standard assumptions in CGE modeling, such as perfectly competitive markets and constant returns to scale in production. Analytical models, such as Fischer et al. (2017) and Fischer et al. (2021), are more capable than CGE models of capturing these market distortions.

28