Trade-offs between resource tax and carbon tax policies in China: Improvement in air quality and health co-benefits

Abstract

While resource tax and carbon tax have been confirmed to mitigate CO₂ emissions, the comparison between the two policies in terms of air quality and health co-benefits is rarely discussed. In this study, we employ a multi-regional computable general equilibrium model, an extend response surface model and health risks assessment model to assess the impacts of multiple regionally-varying resource and carbon tax scenarios on GDP, energy use, CO₂ and the atmospheric environment and public health through 2035 in China. Equal cumulative national GDP (2020–2035) across scenarios ensures policy comparability. Results show that the resource tax policy differentiated by the level of existing regional coal resource tax rate (RT1) achieves more reductions in CO₂ and air pollutants emissions with less GDP loss compared to other resource tax (RT2 – based on the level of regional coal output; RT3 – considering the spatial aggregation of ambient PM_2.5) and carbon tax (CT – aligned with RT3) policy scenarios in 2035 relative to BAU (Business-as-Usual). As to air quality improvement, RT1 leads to the most substantial reduction in average provincial PM_2.5 concentrations, by 8.1% in 2035 followed by RT2 (7.2%), RT3 (6.9%) and CT (5.1%). Additionally, RT1 generates the highest health co-benefit, avoiding 56,413 air pollution-related mortalities in 2035. Considering both emissions reduction and health co-benefits, RT1 emerges as a highly recommended approach over a carbon tax based on an integrated assessment. To achieve the 2035 ‘Beautiful China’ target and ensure steady growth, we recommend increasing the current regional-differentiated resource tax policy stringency except regions with good air quality, such as Ningxia and Qinghai.

Keywords

Resource tax carbon tax air quality health co-benefits multi-regional CGE model

Introduction

Climate change poses a growing threat to global community. As one of the main components of greenhouse gases, CO₂ drives climate change.¹ Most CO₂ emissions are caused by fossil fuel combustion.² As the biggest CO₂ emitter³ and the largest fuel consumer,⁴ China announced ‘dual carbon goal’ (‘Dual carbon goal’: carbon peaking before 2030 and zero carbon by 2060) and has taken practical measures to reduce fuel use and mitigate CO₂ emissions, such as resource tax and carbon pricing policies. China levied resource taxes on coal, petroleum and natural gas initially in 1984 and began to carry out based on price in 2010.⁵ The coal resource tax transformed to an ad valorem tax in 2014⁶ and was determined by each provincial government within a certain range of 2%‒10%.⁷ The resource tax was upgraded from administrative regulations to national laws in 2020⁸ and levied on use and extraction of natural resources, increases the cost of fossil fuels use, which contributes to energy transmission and CO₂ emission reduction.⁹ While China established the national emission trading scheme (ETS) in 2021,¹⁰ carbon tax was proven more effective at reducing emission than ETS.^11,12 Some studies called for an accelerated carbon tax in China.¹³ However, whether resource tax or carbon tax is more effective for emission reduction remains to be examined. In addition to CO₂ mitigation, China has been plagued by serious air pollution problems.¹⁴ Tremendous efforts are needed to solve the dual problems of global warming and air quality degradation.¹⁵ Hence, China has proposed to basically build a ‘beautiful China’ by 2035 and set the goal of fundamentally improving air quality.¹⁶ Due to the characteristics of ‘same root, same source and same process’, fuel-related CO₂ and air pollutants would be synergistically reduced.¹⁷ While both resource and carbon taxes offer potential to mitigate carbon and pollutants, a comparative assessment of their impacts on air quality improvement is still needed. Thus, we construct an integrated modeling framework to compare resource and carbon taxes regarding output, energy use, CO₂ and air pollutant reduction, air quality enhancement, and ancillary health benefits.

Most studies focused on how resource tax affects GDP, energy consumption and carbon emissions across national, provincial, and industrial scales. Ulucak et al.¹⁸ examined the impact of natural resource rent (NRR) on CO₂ emissions in OECD countries based on the augmented mean group method and revealed that NRR promotes CO₂ emissions due to an increase in fuel imports instead of renewable energy use. In contrast, Safdar et al.¹⁹ focused on South Asian economies and found that NRR reduces CO₂ emissions but decreases economic growth. Deserno and Sterk²⁰ found that implementing resource taxes in European countries enhanced resource productivity in the short term, thereby fostering greater circularity in the economy. For China-focused studies, Wen and Jia⁹ set a national coal tax of 10% and found it significantly reduces GDP, the share of coal use, and CO₂ emissions because of rising coal cost. Zhong et al.²¹ examined 2%, 5%, and 8% copper tax rate effects in China's four provinces including Gansu, Zhejiang, Inner Mongolia, and Jiangxi, and discovered that copper consumption in these provinces first increases and then decreases, and regional GDP increases at different levels. Similarly, focusing on electricity power industry in China, Li et al.⁷ found that 10% and 20% coal resource tax increases nuclear power and renewable energy use and reduces coal-fired units. Jiang et al.²² recommended 1% iron ore resource tax rate over 3% and 6% because of much smaller GDP impact of 1% tax rate. Some researchers referred to the firm-level economic effects of resource tax using Difference-in-Difference method. For instance, following resource tax reform, Wang et al.²³ alongside Song et al.²⁴ enclosed increased total factor productivity and green innovation in Chinese mining firms. Some studies included air pollutant emissions in environmental effect analysis of resource tax. For example, Tang et al.⁶ designed coal tax rates scenarios of 5%, 10% and 15% in China and found they reduce real GDP, sectoral output and total energy use, which benefits environment by reducing CO₂, SO₂ and NO_x emissions at different rates. Through a comparison of 2%, 5%, and 10% tax rates on coal, Xu et al.²⁵ concluded that a 5% rate most effectively reduced carbon and PM_2.5 emissions while promoting economic welfare because of consumption inertia under higher tax rates. However, health co-benefits, are barely included in current studies regarding resource tax. Meng et al.²⁶ and Liu et al.²⁷ focused on carbon tax in Australian and Canada respectively and found carbon tax can effectively reduce CO₂ emissions but leads to GDP loss. Specifically, Dong et al.²⁸ evaluated carbon tax impact and found that CO₂ emission reduction degree varies across provinces due to regional disparities in China. The GDP decreasing effect is alleviated and even offset if tax recycling^29,30 and technology progress³¹ are added in simulation in the computable general equilibrium (CGE) model. In terms of energy structure, coal use proportion declines the most while natural gas with a smaller emission coefficient is less affected. Several studies investigated the environmental co-benefits of carbon tax. Jiang et al.³² and Mao et al.³³ primarily measured the reduction of CO, NO_x, PM, and SO₂ emissions associated with carbon tax and found that the air pollutants reduction amount is close to that of CO₂. Other research combined the CGE model with methods to monetize the health co-benefit from reduced air pollutants under carbon tax implication. For example, Woollacott³⁴ used a Co-Benefit Risk Assessment model with simulation of carbon tax, which estimated that a $25 carbon tax with tax recycling generate health benefits of up to 162 billion dollars due to reduced mortality and morbidity related to air pollution. West et al.³⁵ confirmed that global GHG mitigation averts millions of premature deaths (0.5 ± 0.2 in 2030; 1.3 ± 0.5 in 2050).

Some studies compared the impacts of resource and carbon taxes on GDP, emission reduction effect and energy use using a CGE model, but did not consider health co-benefits from improved air quality. Specifically, Scrimgeour et al.³⁶ explored effects from carbon, energy and petroleum products taxes in New Zealand and concluded that if each tax collects 0.6% of GDP, the carbon tax performs the best in reducing fossil fuel energy use and CO₂ emissions. Solaymani³⁷ compared the required rates of carbon tax and energy tax under the 40% reduction Malaysia's Copenhagen target. The result showed that carbon tax is more effective in CO₂ emission reduction while energy tax causes less negative effect on GDP. Liu et al.,³⁸ found the low monetized value of carbon sequestration compared to economic output and this disparity suggests stringent carbon policies for China. Similarly, Liu et al.³⁹ found an initial 25 yuan/ton carbon tax rate with 10% annual increasing rate achieves better CO₂ reduction effects and causes smaller GDP loss than the current resource tax with 10% increasing rate from 2020 to 2035. Furthermore, Hu et al.⁴⁰ explored how resource and carbon taxes on influence energy saving, emission reduction, and economy and found that carbon tax's superior performance at cutting energy use and protecting the environment. However, Lin and Jia⁴¹ reached results different from them. They compared economic impacts of resource and carbon taxes based on a uniform CO₂ emissions benchmark and found that resource tax leads to smaller commodity price change due to the imported energy increase under resource tax scenario.

The above studies investigated how resource and carbon taxes influence emissions, output and energy use, and their common features are: (1) solely investigated resource tax or carbon tax, but lacked the integration of these two policies into research framework, and ignored health co-benefits comparison between resource and carbon taxes; (2) established nationally uniformed resource tax rate, although resource tax rates vary across China in reality; (3) used CGE model at national level, while the impacts of an environmental policy instrument vary in provinces due to regional heterogeneity. To address these gaps, our study aims to (1) incorporate resource tax and carbon tax into research framework and compare their impacts especially regarding atmospheric and public health co-benefits; (2) adopt actual varying coal, oil and natural gas tax rates from different provinces, increase them annually; (3) establish an integrated modeling framework including a CGE model, an extend response surface model (ERSM) model, and a health benefit assessment model, to estimate policy effects throughout 30 mainland Chinese provinces. The following sections are organized in this order. Methodology outlines the models, data sources, and scenarios, followed by the results and discussion in Results, and Conclusion and Policy suggestions concludes with policy implications.

Methodology

CGE model

For a national CGE model, the inputs for production activities include labor, capital, fuel, electricity, as well as other intermediate products, following the principle of minimizing costs. The model describes behaviours of economic agents which include government and consumers, assuming consumers pursue utility maximization, investment equals saving, government revenue and expenditure balance, commodity and factor market clear and foreign trade balances. In social welfare module, the changes of utility under the impact of different tax rate situation are measured by Hicksian equivalent variation. In dynamic module, driving forces are population growth, technology improvement and capital accumulation. In terms of estimation period, our dynamic CGE model focus from 2020 to 2035, mainly exploring the contribution or gap of resource and carbon taxes in different regions to the basic realization of Beautiful China in 2035.

Different from the existing national-level model, we develop a multiregional CGE model that covers China's 30 provinces (Xizang, Taiwan, Hong Kong and Macao are excluded due to data unavailability). First, we specifically articulate interprovincial flow of commodities. The inputs in a region may be provided by local area, other regions and overseas, following a constant elasticity of substitution (CES) functional form. The difference of substitution elasticity of products produced in a region sold at home and abroad is expressed by a constant elasticity of transformation (CET) function. The elasticity of interprovincial commodity substitution is measured by 1.5 times that of international trade, which is quoted from the 9th Global Trade Analysis Project, referring to Zhang et al. (2013). Second, we incorporate an environmental module including fuel-related CO₂, NO_X, PM_2.5, SO₂ and VOCs emissions and non-related fuel emissions. We introduce resource and carbon taxes into the environmental module to explore how these polices influence regional emissions and air quality from 2020 to 2035. Specifically, a resource tax is applied to the extraction and sale of coal, oil, and natural gas, whereas a carbon tax applies to the CO₂ emissions generated from fuel use. The tax revenue is collected by local governments and is then returned to households and firms through lump-sum transfers. The distribution proportion for these transfers is consistent with the base year data. Third, the model allows investment and labour to flow across regions. Difference in wages depicted by a distortion coefficient determines capital flows from low-income areas to high-income areas and the flow of labour. In addition, the economy includes eight sectors, namely, agriculture, chemical, non-metallic product manufacturing, metals smelt and press, other industries, construction, transport sector, remaining sectors, and five energy sectors (coal, petroleum, natural gas, thermal power, and clean power). More details can be found in Table S1 of Supplementary Information. The fundamental structure of the multiregional CGE and related formula details have listed in Zhang et al.⁴²

ERSM model

ERSM model was built by Tsinghua University (http://abacas.see.scut.edu.cn/abacas/Default.aspx) which is utilized to predict the PM_2.5 concentrations based on precursor emissions.⁴³ ERSM model built a real-time connection between the response variable and a set of control factors, quantifying the nonlinearity in PM_2.5 response to precursor emissions. This relationship is derived through the fitting of multiple simulations conducted using the Community Multiscale Air Quality (CMAQ) model. The ERSM with a set of polynomial functions (pf-ERSM) was applied to measure how ambient PM_2.5 concentrations react to shifts in precursor emissions.⁴³ The simulation is carried upon a 27 km × 27 km grid during the four representative months (January, April, July and October) in East Asia in 2015. 755 emissions scenarios were simulated, in which the emission ratios of different provinces were random. The details of the evaluation results of the CMAQ model are described in Ding et al.⁴⁴ The provincial annual PM_2.5 concentrations, serving as the response variables, are determined by averaging the concentrations across four representative months.⁴⁵ These concentrations are influenced by control factors including regional emission coefficients of NO_x, PM_2.5, SO₂, VOCs and NH₃.⁴³

Health benefits assessment

The assessment of premature deaths related outdoor PM_2.5 exposure was conducted based on Global Exposure Mortality Model (GEMM)⁴⁶ and Global Burden of Disease (GBD).⁴⁷ The main function of estimating the premature deaths of PM_2.5 exposure is determined by equation (1):

M = r_{o} \times (\frac{1 - R R}{R R}) \times P o p

(1)

Where M,

r_{o}

R R

, and

P o p

represent outdoor PM_2.5-attributable premature mortality, mortality rate in the baseline year, relative risk ratio, and population of adults older than 25 years, respectively. The mortality rate applied in the study was from database of the GBD, which we calculated the all-cause mortality attributed to outdoor PM_2.5 exposure. The provincial population data for China in 2015, 2020, 2025, 2030, and 2035 was derived from the previous study.⁴⁸

The RR of long-term outdoor PM_2.5 exposure is calculated using the GEMM, which the main mathematical formula is shown in equations (2)‒(3):

R R = \exp (\frac{β \log \frac{Δ z}{α} + 1}{1 + \exp (- \frac{Δ z - μ}{υ})})

(2)

Δ z = \max (0, z_{m} - z_{c})

(3)

Where

α

β

μ

, and

υ

are parameters of the exposure-response model within recommended values in previous study.⁴⁷

z_{m}

is the yearly average PM_2.5 concentrations.

z_{c}

is the theoretical minimum risk exposure level, which equals to 2.4 µg/m³. The key parameters used in GEMM and GBD are provided from our previous study.^49,50

Integrated modeling framework

Provincial annual NO_X, PM_2.5, SO₂ and VOCs emissions simulated based on the historical emission factors as well as the assumption that end-of-pipe technology improved annually⁵¹ compared to the baseline using a multi-regional dynamic CGE model. Projected emissions of these pollutants, based on the CGE model, the CGE model, we employ the ERSM prediction system which built a nonlinear response function between emissions and PM_2.5 concentrations to assess and predict provincial PM_2.5 concentrations to 2035. Details on the basic settings of ERSM model and CMAQ model can be found in ERSM model subsection. Utilizing the GEMM methodology, we estimate the outdoor PM_2.5-attributable premature mortality across all age groups, using the provincial-level PM_2.5 concentrations projected by the ERSM model. Since the outcome varies considerably depending on the age group classification, we use the widely accepted classification scheme based on the 2015 Global Burden of Disease Study (GBD 2015) combined with GEMM model under the assumption that a fixed classification scheme (e.g., <25, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, >80).⁵² Details on the connections between these three core models are illustrated in Figure 1.

Figure 1.

Integrated modeling framework.

Scenario design

Five scenarios are designed (see Table 1): the business as usual (BAU), three resource tax (RT1, RT2, and RT3) and one carbon tax (CT) policy scenarios. The BAU scenario without any policy implementation is used as a baseline scenario. RT1–3 and CT policies divide 30 provinces into high, medium and low areas. This classification is based on differences in regional policy strengthen due to the regional disparities. Furthermore, these four regional differentiated policies have the same accumulated national GDP during 2020–2035. Specifically, the regional categorization under the RT1 scenario is determined by the level of existing regional coal resource tax rate. Under RT1 scenario, resource tax rates (coal, petroleum and natural gas) in high area (Inner Mongolia, Shaanxi, Ningxia, Shanxi, Qinghai, Xinjiang, Zhejiang, Yunnan, Hebei, and Guangdong), medium area (Guizhou, Shandong, Jiangsu, Heilongjiang, Beijing, Liaoning, Chongqing, Guangxi, Anhui, and Gansu) and (Jilin, Fujian, Hunan, Sichuan, Jiangxi, Henan, Hubei, Tianjin, Shanghai, and Hainan) starting from the actual resource tax rates in each province in 2020, would be annually increased by 10%, 5% and 2.5%, respectively, until 2035. We adopted the growing tax rates from Zhang et al.⁴⁵ because the gradually increasing tax rates from a moderate level reduces the negative effect on economy. RT2 and RT3 classifies provinces into three groups based on the level of regional coal output (2012–2019) and the spatial cluster level of PM_2.5 concentrations (2005–2019), respectively. The original price of resource tax rates in 2020 and their growth rates under RT2 and RT3 scenarios are set the same as RT1. In addition to resource tax, the regional division in CT scenario is designed the same as RT3. Carbon tax rate in CT scenario is set according to Zhang et al.,² in which this scenario has been confirmed as the best effect in air quality improvement. Thus, the addition of a CT scenario allows a comparison of the air quality and health co-benefits from resource and carbon taxes.

Table 1.

Scenario design.^a

Scenario	Regional division/tax rate
BAU	No policy implemented.
	High area	Medium area	Low area
Resource tax	A 10% annual tax rate (coal, oil, and natural gas) increase starting in 2020.	A 5% annual tax rate (coal, oil, and natural gas) increase starting in 2020.	A 2.5% annual tax rate (coal, oil, and natural gas) increase starting in 2020.
RT1 (current regional coal resource tax rates)	NM, SAX, NX, SX, QH, XJ, ZJ, YN, HEB, GD;	GZ, SD, JS, HLJ, BJ, LN, CQ, GX, AH, GS;	JL, FJ, HUN, SC, JX, HEN, HUB, TJ, SH, HAN;
RT2 (historical regional coal output)	SAX, GZ, XJ, SD, NM, SX, HEN, AH, NX, HEB;	HLJ, SC, YN, LN, GS, HUN, CQ, JL, JX, JS;	FJ, QH, HUB, GX, BJ, TJ, SH, GD, HAN, ZJ;
RT3 (historical PM_2.5 Spatial Clustering)	BJ, TJ, HEB, SD, HEN, AH, HUB, SX, SAX, XJ, SC;	LN, NM, NX, GS, CQ, HUN, JX, ZJ, JS;	SH, JL, QH, HLJ, GX, GZ, GD, YN, FJ, HAN;
	High area	Medium area	Low area
Carbon tax	Beginning at 40 yuan/t CO₂ in 2020 and rising 10% per year.	Beginning at 10 yuan/t CO₂ in 2020 and rising 5% per year.	Beginning at 5 yuan/t CO₂ in 2020 and rising 2.5% per year.
CT (historical PM_2.5 Spatial Clustering)	BJ, TJ, HEB, SD, HEN, AH, HUB, SX, SAX, XJ, SC;	LN, NM, NX, GS, CQ, HUN, JX, ZJ, JS;	SH, JL, QH, HLJ, GX, GZ, GD, YN, FJ, HAN;

For full names of provinces, see Table S1 of Supplementary Information.

Data sources

China's Interprovincial Input-Output Table built by Liu et al. (2018) provides the data on key economic flows, such as production, trade, consumption, investment and saving. We collect provincial resource tax rates from the official websites of the local tax bureaus and people's congresses (e.g., for Zhejiang province, the link is https://zhejiang.chinatax.gov.cn/art/2020/8/3/art_24101_471824.html). The values of elasticities of substitution are referred to Zhang et al.⁴² Calibration of key endogenous variables (e.g., scale/share parameters, income/output tax rates, emission coefficients) is estimated through solving equations with the same number of unknown endogenous variable, which is the calibration method.⁴⁵ Furthermore, fuel-related NO_x, PM_2.5, SO₂, and VOCs emissions in non-metallic mineral and metal smelting/pressing industries are estimated from emission factors and fuel consumption, with non-fuel-related emissions determined by output. Provincial CO₂, NO_X, PM_2.5, SO₂, and VOCs emission inventories in 2015 base year in both CGE and ERSM models are from ABaCAS-EI v2.0 of Li et al.⁵³ More information about emission reduction ratios have listed in Zhang et al.⁴⁵

Results

GDP

Resource and carbon taxes slightly reduce national GDP at similar rates ranging from 0.4% to 0.5% in 2035 (see Figure 2). This is because the implementation of these tax policies increases production costs, resulting in output reductions, especially in energy-intensive sectors. Contrary to findings by Solaymani³⁷ that carbon taxes have less economic impact than energy taxes in Malaysia, our results show the economic effect of resource taxes marginally exceeds that of carbon taxes. The primary reasons for this are differing economic sizes and revenue recycling schemes. However, some regions dominated by the tertiary or high-tech industry would be seldom influenced by these polices which provide more opportunities to increase GDP. For example, the GDP of Zhejiang and Guangdong provinces of the High area in RT1 scenario increased by 2.7% and 2.6%, respectively (see Figure S1 of Supplementary Information). Coal, petroleum and natural gas input decrease in all sectors in Zhejiang and Guangdong. The input of clean power increases by 4.8% in the production and supply of thermal power sector in Zhejiang province and increases in all sectors excluding energy production sector in Guangdong province in RT1 scenario. Thus, Zhejiang and Guangdong have an advantage over low-carbon transition when setting with high resource tax rates. This corresponds to Li et al.,⁵⁴ in which Zhejiang and Guangdong are two of the provinces that are estimated to have highest CO₂ emission efficiency, which is smaller ratios of CO₂ emission to GDP. It indicates that Zhejiang and Guangdong could be an example for other regions to improve energy mix under the resource tax. However, due to their backward production capacity, the GDP of industrial-intensive provinces Hebei, Shanxi and Shandong will drop by about −3% in 2035 (Figure S1 of Supplementary Information). Different from RT1, an increase in GDP in Low area in RT2, RT3 and CT scenarios covers part of GDP loss in other areas, such as Fujian. This is because a petroleum and natural gas input rise in chemical industry, metal smelting, transportation, and other services in Fujian under RT2, RT3 and CT scenarios.

Figure 2.

GDP changes by region and across RT1–CT scenarios relative to BAU (2035).

Fuel use

Table 2 shows a decrease in fuel use (coal, petroleum and natural gas) across High, Medium, and Low regions under RT1-RT3 and CT scenarios compared to the BAU level in 2035. This is attributed to mineral extraction/processing and other energy-intensive sectors (e.g., non-metallic mineral products, transportation), according to Guo et al.⁵⁵ Additionally, RT1-RT3 reduce coal, petroleum and natural gas use by closing magnitudes, ranging from 12% to 20%, superior to CT scenario. This is because resource tax can directly increase energy price in resource-producing provinces, increasing the cost of energy utilizer in other provinces, that is working on both supply and demand sides, while carbon tax affects demand side directly, which needs a lengthy transmission mechanism to influence the supply side. For instance, Table S3 of SI confirms that both energy suppliers (Inner Mongolia, Shaanxi, Shanxi, and Xinjiang) and energy consumers (Hebei, Zhejiang, Jiangsu, and Guangdong) reduce more fuel consumption under RT1-RT3 scenarios than reductions under CT scenario. It indicates that the increase in energy price in resource-producing provinces has stronger inhibitory effect than the increase in energy use costs in resource-using provinces. To be specific, RT1 reduces coal, petroleum and natural gas use by 23.8%, 25.1% and 33.6% respectively in High area, the largest among all scenarios (see Table 2). For example, the fossil fuel use in Yunnan, Qinghai and Zhejiang in 2035 decreased by 20.3%, 29.1% and 24.8%, respectively, which contribute a much larger reduction magnitude in High area in RT1 scenario than in the other three scenarios (see Table S3 of Supplementary Information). The fossil fuel use is reduced mostly in mining of coal and processing of coking, construction, transportation, other services and chemical industry in Yunnan province, thermal power industry and ‘other industry’ in Zhejiang province, and coal and petroleum industry in Xinjiang province.

Table 2.

Changes in fuel use in high, medium, and low areas under RT1−RT3 and CT scenarios relative to the BAU level (%).

Region	Fuels	RT1	RT2	RT3	CT
High area	Coal	−23.8	−19.8	−15.7	−18.5
	Petroleum	−25.1	−24.7	−24.2	−13.4
	Natural gas	−33.6	−28.8	−23.9	−7.5
Medium area	Coal	−9.7	−7.7	−12.6	−4.7
	Petroleum	−15.5	−11.5	−15.0	−4.4
	Natural gas	−17.1	−14.5	−16.7	−2.2
Low area	Coal	−4.5	−10.4	−9.3	−2.1
	Petroleum	−8.7	−14.1	−13.8	−2.7
	Natural gas	−9.7	−14.0	−18.2	−1.4

CO₂ emissions

All scenarios lead to progressively deeper cuts in CO₂ emissions between 2020 and 2035. Raising the resource tax or carbon price in each scenario results in the rising of the cost for fossil fuels use, which consisted with the result of Liu et al.³⁹ Figure 3 shows effects of resource and carbon taxes on CO₂ reductions by High, Medium and Low regions compared to BAU (2035). RT1 has the largest CO₂ reduction (−17.5%) in High area compared to the BAU level, followed by RT2 (−13.9%), CT (−13.8%) and RT3 (−10.9%), respectively. Specially, coal-producing provinces (i.e., Inner Mongolia and Xinjiang), and high-GDP-value province (i.e., Zhejiang), mainly contribute 30% of CO₂ emission reduction in High area under RT1 scenario (as shown in Figure S2 of Supplementary Information). This is because CO₂ emission reductions in these three provinces are mainly achieved by cutting about 30% coal use in chemical industry, transportation, mining and processing industry of coal and production and supply of thermal power under RT1 scenario. This indicates that improving the energy mix in some critical industries is the key factor for mitigating CO₂ emissions, according to Dong et al.²⁸ From the perspective of the economic and CO₂ emission reduction effects, compared with other resource tax scenarios, RT1 scenario greatly reduces CO₂ emissions with less GDP loss. This result is different from Hu et al.⁴⁰ that carbon policy contributes more carbon mitigation than resource tax. This is because that various policy scenarios designed by Hu et al.⁴⁰ were not on the basis of a unified measurement standard (cumulated GDP or cumulated emission level during simulation period) to compare their impacts. That is, high carbon price results in more emission reduction while lower resource tax rate cause less reduction. Similarly, Scrimgeour et al.³⁶ enclosed that carbon tax in New Zealand would be the most effective policy for reducing CO₂ emissions compared to energy tax and petroleum products tax due to its high tax rate.

Figure 3.

Impacts of RT1 and CT on emissions in different areas from the BAU level in 2035.

However, regionally differentiated resource tax and carbon tax policies result in CO₂ leakage. Because energy price in Low area is much lower, these provinces can easily increase fossil fuel input, which is consist with Zhang et al.⁵⁶ and Bistline et al.⁵⁷ Importantly, RT1 causes less leakage in some provinces compared to other scenarios (as shown in Figure S2 of Supplementary Information). For instance, CO₂ emission leakage associated with RT1 policy happening in Guangxi and Chongqing is less than RT2 scenario. This is because smaller rise in Guangxi province of input of coal, petroleum and natural gas in chemical, metals smelt and press and thermal power industries in RT1 scenario. As to Chongqing province, the input of fossil fuel increase in ‘other industries’, transportation and other services is smaller in RT1 scenario.

Air pollutant emissions

Similar to CO₂ emission reduction, High-region emissions see a much greater reduction than Medium and Low regions, relative to BAU (Figure 4). RT1 scenario has an edge over air pollutant emission reduction in High area. Specifically, provincial NO_X, PM_2.5, SO₂, and VOCs emissions in the High region see reductions of 15.9%, 11.9%, 17.3%, and 14.2% respectively under the RT1 scenario, 1.5–1.7 times the NOx reduction rates, 0.9–1.4 times the PM_2.5 reduction rates, 1.1–1.4 times the SO₂ reduction rates and 1.1–1.7 times VOCs reduction rates in High area under RT2, RT3 and CT scenarios. The Guangdong, Yunnan and Qinghai provinces under RT1 scenario reduce much more air pollutants than the other three scenarios. This is because in RT1 scenario, they are divided into High area and the reduction of fossil fuel input is greater compared with other scenarios. In addition, the reduction of SO₂ emissions is close to the reduction rate of CO₂ emissions in all scenarios, which due to the same source of SO₂ and CO₂ (i.e., fossil fuel consumption).⁴⁵

Figure 4.

Impacts of RT and CT on air pollutants emissions in 30 provinces relative to BAU in 2035. Note: For full names of provinces, see Table S1 of Supplementary Information.

Emission leakage can be seen in air pollutants emission at provincial level in both resource tax and carbon tax scenarios (see Figure 4). RT1 scenario causes smallest air pollutant emission leakage in 2035 at provincial level compared to other resource tax scenarios. For example, Chongqing is listed in the Medium area in RT1‒RT3 scenarios, while the magnitude of air pollutant emission leakage in RT1 (NO_X, −0.9%; PM_2.5, −0.4%; SO₂, 3.8%; VOCs, 0.7%) is smallest compared to RT2 (NO_X, 0.3%; PM_2.5, −0.2%; SO₂, 4.6%; VOCs, 1.1%) and RT3 (NO_X, 0.8%; PM_2.5, 0.5%; SO₂, 6%; VOCs, 2%) scenarios. This is because strict resource tax policies in the neighbors of Chongqing, such as Sichuan, Hunan, and Hubei, are implemented under RT2 and RT3 scenarios, which results in fuel-intensive firms transition to their neighbor Chongqing. Furthermore, CT results in PM_2.5 emission leakage in Guangdong (1.1%), Yunnan (0.1%) and Guizhou (0.1%), while no PM_2.5 emission leakage happens in RT1 scenario. This is because these provinces have weak carbon tax policies and are surrounded by regions with strict policy, resulting in emission transfer. For regions subjected to air pollutant emission leakage, additional measures should be put forward, such as tax recycling method to subsidize low-carbon firms and invest in renewable energy which proposed by Xu and Zhang.⁵⁸ Furthermore, the implementation of these additional measures depends on the disparities in the emission intensities of goods produced by various sectors, along with the nature of their linkages within production networks and consumption patterns.⁵⁹

Air quality co-benefits

Chinese government in 2024 newly issued the guidelines to promote the development of a ‘Beautiful China’, which outlined that national average ambient PM_2.5 should be reduced to 28 μg/m³ in 2027 and 25 μg/m³ in 2035, respectively (https://www.gov.cn/zhengce/202401/content_6925405.htm). Figure 5 shows that the changes in provincial ambient PM_2.5 under RT1-RT3 and CT scenarios in 2035 relative to BAU. The mean ambient PM_2.5 level across all provinces in 2035 relative to the BAU level of RT1, RT2, RT3 and CT scenario reduced by 8.1%, 7.2%, 6.9% and 5.1%, respectively. While resource tax policies outperform carbon tax in expanding the coverage of provinces below 28 μg/m³ in 2035 compared to BAU, only RT1 scenario further extends this coverage to the 25 μg/m³ threshold. It implies that RT1 scenario has a better effect on improving air quality. If resource tax rates are further raised under RT1 scenario, air quality in more provinces will be improved. Specifically, a largest decrease in PM_2.5 concentrations in 2035 under RT1 scenario happens in Zhejiang at 23.2 μg/m³, meeting the ‘Beautiful China’ standard in RT1 scenario. This is because a 24.8% reduction in coal, petroleum and natural gas input. Additionally, regional disparities in air quality improvement, all of which have a big gap with the targets in 2035. For instance, average annual PM_2.5 levels across the Yangtze River Delta (2035) under RT1 scenario are decreased by 14.2% from the BAU level, at 34.1 μg/m³, followed by Fenwei Plain (9.2%) at 35.2 μg/m³, Pearl River Delta (8.6%) at 20.1 μg/m³ and Beijing-Tianjin-Hebei region (7.5%) at 50.4 μg/m³. Hence, regional collaborative emission reduction is needed to achieve emission reduction targets due to complex air pollutant emissions of different industrial structures regions. For instance, sharing emission reduction technologies, including transferring technology and experience, to help developing regions embark on a path of green and sustainable development.

Figure 5.

Spatial distribution of regional ambient PM_2.5 (RT1, RT2, RT3 and CT scenarios, 2035).

Health co-benefits

Air quality improvement leads to health co-benefits.⁶⁰ Provincial avoided death under RT1-RT3 and CT scenarios relative to BAU in 2035 is listed in Table S4 of SI. RT1 contributes to most national avoided mortalities (56,413) in 2035 compared with RT2 (48,255), RT3 (46,364) and CT (34,364) scenario. Among all the provinces in RT1 scenario, Zhejiang has largest number of avoided mortality (8382) due to its air quality improvement, followed by Jiangsu (5002), Shandong (4551) and Henan (4252) due to their large populations and heavily air pollution, aligning with results reported by Zhang et al.⁵⁶ and West et al.³⁵ Importantly, some regions that are not divided into High area, such as Jiangsu (RT1-RT3 and CT), Zhejiang (RT2, RT3, CT), Shandong (RT1), Henan (RT1) and Anhui (RT1), have larger health co-benefits than most other regions. These regions form a tightly-knit geographical cluster, constituting a key coastal-to-central Chinese zone. Interestingly, both resource-based regions, such as Shanxi, Inner Mongolia and Shaanxi, and resource-inflow provinces, such as Guangdong, Zhejiang, and Jiangsu, exhibit greater sensitivity to the resource tax, which in turn generates greater health co-benefits compared to the carbon tax. However, Sichuan is highly sensitive to both the resource and carbon taxes. For some regions with good air quality, however, RT1 slightly contributes to air quality improvement, avoiding very few premature deaths, such as Qinghai, Hainan and Ningxia. Considering the GDP loss in these three provinces, their welfare is more affected compared with eastern provinces. It is reasonable for them to lower the resource tax rate temporarily due to the low mitigation potential.

Sensitivity analysis

To test the robustness of our findings, according to Li et al.,⁶¹ Karapinar et al.⁶² and Zhang et al.,⁶³ we conduct sensitivity tests by varying the value ranges of key parameters, such as the magnitude of elasticity values and tax rates. The elasticities of substitution for regional commodities, as a core element of the CES function, are critical for building a provincial CGE model. Regional commodity substitution elasticities affect the allocation of regional resources, thereby influencing local economy and the environment. Thus, referring to Zhang et al.⁴⁵ that scaled these elasticities +33.3% (high case) and −33.3% (low case) relative to the base cases, we observe effects of resource versus carbon taxes on national growth, fossil fuel use, emissions, air quality, and health co-benefits under different scenarios. Given that the tax rates in all scenarios in this study grow exponentially over a long simulation period, excessively large tax rates lack practical relevance. Therefore, with reference to existing literature, such as Li & Peng⁶⁴ and Karapinar et al.,⁶² the tax rates for all scenarios are set at ±30% of the base case to assess the reliability of the conclusions.

Taking R1 and CT scenarios as examples, Table S5 of Supplementary Information shows that the impacts of increasing or decreasing these elasticities or tax rate on the national economy, energy, emissions, air quality, and public health in 2035 exhibit a linear relationship. In both the high- and low- cases, the changes in various indicators across regions in 2035 relative to the base case follow nearly identical patterns, with no anomalous deviations observed. For instance, under the high elasticity case, the changes in all variables under the R1 scenario relative to BAU are much lower than these in the Base case, while the changes are smaller under the low elasticity case. Overall, the model's results remain robust and applicable across varying tax rate levels.

Conclusion and policy suggestions

This study investigates effects of multiple regionally varying resource and carbon tax scenarios on GDP, energy use, CO₂ and air pollutants emissions, air quality and health benefits from 2020 to 2035, based on the multiregional CGE, ERSM and health benefits assessment models.

Resource tax results in lower national GDP loss and decreases more national fuel use in 2035 comparted to carbon tax. RT1 especially has advantages in national CO₂ and air pollutant mitigation, which is nearly 1.5 times the reductions under CT in 2035. While emission leakage happens in all scenarios in 2035, RT1 scenario is with smallest leakage effect. Provincial average PM_2.5 concentrations associated with RT1 decrease the largest by 8.1% in 2035 from the BAU level among three resource tax policy scenarios, which is rough twice the reductions under CT scenario. Resource tax policies outperform carbon tax in enabling more provinces to achieve the 15 μg/m³ target compared to BAU in 2035, and promote Zhejiang exposing to below 25 μg/m³ in 2035 achieving the ‘Beautiful China’ target. Additionally, RT1 contributes to 56,413 avoided mortalities for China in 2035, followed by RT2 (48,255), RT3 (46,364) and CT (34,364). Among all the provinces, Zhejiang, Jiangsu, Shandong and Henan have highest avoided mortality due to large populations and heavily air pollution. Based on these findings, we propose the following policy recommendations.

To effectively improve regional environment and human health with small GDP loss, resource tax highly suggested, rather than carbon tax, especially the regional-differentiated resource tax referencing existing regional coal resource tax rate. Even under the regional-differentiated resource tax policy scenario, however, there are still relatively few regions that can achieve the 2035 ‘Beautiful China’ target. Thus, we suggest increasing the regional-differentiated resource tax policy stringency and advocating additional reduction measure, such as the end-of-pipe control. The high-GDP-value and resource-producing provinces are recommended with higher tax rates, while regions with good air quality, such as Ningxia and Qinghai, are suggested to lower their resource tax rate.

Recycling the resource tax revenue is suggested to improve energy mix especially for energy- and pollution- intensive key sectors (e.g., chemicals, metal smelting, and transportation). Although fuel use decreased with resource and carbon tax implementation, coal use share still accounts for over half in 2035. In addition to firms actively improving energy mix, thus, the government should increase investment and provide policy support that stimulates firms to realize electrification reform.

Although the air quality improvement in Shandong, Henan and Hebei lead to considerable number of avoided mortality in these provinces, PM_2.5 levels in these regions are projected to remain elevated (exceeding 50 μg/m³ in 2035). This implies that a large gap exists for these regions to achieve ‘Beautiful China’ targets. Thus, current resource tax rates in these regions should be decisively raised, especially coal tax rate. The tax revenue should be transferred to promote their energy transition.

The resource tax policies generate more air quality and health co-benefits in both resource-based provinces and resource-inflow regions compared to the carbon tax. Thus, we suggest that these regions should continue imply resource tax and enhance the tax rate accordingly. Particular attention must be paid to resource-dependent provinces, as their resource-reliant economies are highly vulnerable to disruptive shocks during the policy implementation. Such economic pressure risks triggering local protectionism. Thus, national tax rate design must incorporate mechanisms to prevent this outcome. For provinces that rely heavily on external energy resources, a strategic resource tax rate increase would foster local industry restructuring toward greener supply chains.

Supplemental Material

sj-docx-1-eae-10.1177_0958305X251410116 - Supplemental material for Trade-offs between resource tax and carbon tax policies in China: Improvement in air quality and health co-benefits

Supplemental material, sj-docx-1-eae-10.1177_0958305X251410116 for Trade-offs between resource tax and carbon tax policies in China: Improvement in air quality and health co-benefits by Wenwen Zhang, Cui Wu, Bin Zhao, Yisheng Sun, Shuxiao Wang, Basil Sharp, Yu Gu, Fenfen Zhang and Mei Wan in Energy & Environment

Footnotes

ORCID iDs

Wenwen Zhang

Fenfen Zhang

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China (No. 22YJC790167), Shanghai Pujiang Programme (No. 23PJC050), and National Key Research and Development Program of China (No. 2023YFC3709403).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

Author biographies

Wenwen Zhang is an associate professor at the School of Economics, Shanghai University. She holds a PhD in Economics from the University of Auckland. Her research centers on energy and environmental economics, environmental policy, and synergistic governance of climate and environment.

Cui Wu is a master candidate at the School of Economics, Shanghai University. Her research direction is Environmental Economics.

Bin Zhao is an associate professor in the School of Environment, Tsinghua University. He focuses on an extended response surface model of CMAQ and the interactions and coordinated control of aerosol pollution and climate change.

Yisheng Sun is a PhD student at the School of Environment, Tsinghua University. He works on an integrated assessment model, specifically GCAM-China.

Shuxiao Wang is a professor in the School of Environment, Tsinghua University. Her research interests include emission characteristics of air pollutants, regional air pollution modeling, exposure assessment, and control policies.

Basil Sharp is a professor at the University of Auckland, New Zealand. His research centers on resource and environmental economics, with a focus on sustainable energy policy, emission trading, and the economics of renewable energy integration.

Yu Gu is an associate director at the Joint Institute for Regional Earth System Science and Engineering at the University of California, Los Angeles (UCLA). Her work focuses on atmospheric aerosol science, satellite remote sensing, and assessing the climate co-benefits of environmental policies.

Fenfen Zhang is a postdoctoral researcher under a joint program between the Department of Environment, Yangtze Delta Region Institute of Tsinghua University, Zhejiang and Tsinghua University. The research interests include emission characteristics of air pollutants, regional air pollution modeling, exposure assessment, and control policies.

Mei Wan is a senior engineer in the Department of Environment, Yangtze Delta Region Institute of Tsinghua University, Zhejiang. Her research interests include emission characteristics of air pollutants and air pollution control.

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Supplementary Material

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Trade-offs between resource tax and carbon tax policies in China: Improvement in air quality and health co-benefits

Abstract

Keywords

Introduction

Methodology

CGE model

ERSM model

Health benefits assessment

Integrated modeling framework

Scenario design

Data sources

Results

GDP

Fuel use

CO2 emissions

Air pollutant emissions

Air quality co-benefits

Health co-benefits

Sensitivity analysis

Conclusion and policy suggestions

Supplemental Material

sj-docx-1-eae-10.1177_0958305X251410116 - Supplemental material for Trade-offs between resource tax and carbon tax policies in China: Improvement in air quality and health co-benefits

Footnotes

ORCID iDs

Funding

Declaration of conflicting interests

Supplemental material

Author biographies

References

Supplementary Material

CO₂ emissions