Impact of Carbon Tax on Renewable Energy Development and Environmental–Economic Synergies

Abstract

Global warming caused by greenhouse gas emissions has become a worldwide environmental problem, posing a great threat to human survival. As the world’s largest emitter of carbon dioxide, China has pledged to reach peak carbon emissions by no later than 2030 and carbon neutrality by 2060. It is found that a carbon tax is a powerful incentive to reduce carbon emissions and promote an energy revolution, but it may have negative socio-economic impacts. Therefore, based on China’s 2020 input–output table, this paper systematically investigates the impacts of a carbon tax on China’s economy, carbon emissions, and energy by applying a computable general equilibrium model to determine the ideal equilibrium between socio-economic and environmental objectives. Based on energy use characteristics, we subdivided the energy sector into five major sectors: coal, oil, natural gas, thermal power generation, and clean power. The results show that when the carbon emission reduction target is less than 15%, that is, when the equilibrium carbon tax price is less than 54 yuan/ton, the implementation of a carbon tax policy can significantly reduce carbon emission and fossil fuel energy consumption, while only slightly reducing economic growth rate, and can achieve the double dividend of environment and economy. Moreover, because the reduction of coal consumption has the greatest impact on reducing carbon emissions, the ad valorem tax rate on coal after the carbon tax is imposed is the highest because coal has the highest carbon emission coefficient among fossil fuels. In addition, as an emerging clean energy source, hydrogen energy is the ideal energy storage medium for achieving clean power generation in power systems. If hydrogen energy can be vigorously developed, it is expected to greatly accelerate the deep decarbonization of power, industry, transportation, construction, and other fields.

1. Introduction

Since the Industrial Revolution, the rapid growth of global energy demand has led to the massive use of fossil energy sources such as coal, oil, and natural gas and the excessive emission of greenhouse gases such as carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄), which in turn has caused global warming and the frequent occurrence of extreme weather, and has posed a great threat to natural ecosystems and socio-economic life [1,2]. According to data compiled by the World Resources Institute (WRI), China is currently the world’s largest emitter of carbon dioxide, accounting for about 30% of global emissions [3], and is under enormous pressure to cut carbon emissions [4].

In addition, China’s energy consumption structure is still dominated by fossil energy, which is rich in coal, poor in oil, and less in gas [5]. With the rapid development of China’s economy and society, the demand for energy consumption has risen rapidly, and the development of energy is limited by natural endowments [6]. Under the dual constraints of environment and natural resource endowment, in order to get rid of dependence on fossil energy and respond to the energy consumption demand of social development, the Chinese government has successively introduced relevant policies to promote energy transformation, such as imposing a carbon tax on fossil energy [7], developing renewable energy [8], improving energy efficiency [9], etc. It has also made commitments to reach a carbon peak by 2030 and carbon neutrality by 2060 [10].

Research has shown that the implementation of carbon pricing policies by Governments is a powerful incentive to reduce carbon emissions and promote the energy revolution. Carbon pricing policies include two kinds of carbon tax and carbon emissions trading mechanisms. Carbon tax is a tax directly levied by the government on enterprises or residents in relation to carbon emissions, which increases the production and consumption costs of carbon-intensive commodities, influences market behaviors through price signals, and indirectly controls carbon emissions; it lacks a taxing sector at the global level, and the effect of emission reduction is uncertain. The carbon emissions trading mechanism sets a cap on carbon emissions and directly controls carbon emissions, and enterprises freely trade emission limits through the market, which requires sound regulatory rules. Currently, more than 30 countries and regions are implementing carbon tax policies, and more than 21 countries and regions are implementing carbon trading systems, covering more than 9% of total global emissions, as well as regions where the two policies are carried out jointly. It is worth noting that as the imposition of carbon taxes increases, the marginal cost of abatement increases significantly, which may have an adverse impact on economic growth. This is because a carbon tax on fossil fuel production will increase the cost of energy inputs relative to the cost of other intermediate inputs, and producers will take advantage of the adjustment of the fossil energy share, which will help to improve the efficiency of the use of fossil energy and promote the development of energy in the direction of low-carbon and cleaner energy; at the same time, producers will also pass part of the carbon tax on to the consumer to pay for through the price of the product, or reduce labor inputs by lowering the remuneration of laborer’s and so on, which will, in turn, affect the consumer demand for energy commodities, resulting in a cascading effect, leading to repeated taxation of consumers at the end, but also prone to a huge impact on carbon-intensive industries, which affects social welfare and is not conducive to economic growth. Therefore, to balance the needs of the environment, socio-economics, and energy development, the government needs to comprehensively assess the direct and indirect impacts of implementing a carbon tax to formulate a reasonable carbon tax policy.

The integrated assessment model (IAM) is a tool to assess the impact of specific policies or actions on the economy, environment, and society. It can capture the interaction and feedback between policy actions in different fields and is widely used in the design and evaluation of climate change policies such as carbon tax and carbon trading. Computational general equilibrium model (CGE), as a kind of IAM, has a strong multi-sector and multi-regional modeling ability, which can subdivide multiple economic sectors (such as energy, manufacturing, agriculture, etc.) and regional markets (such as various countries or regions), and cover multiple economic entities at the same time. It can simulate not only individual economic behavior from the micro level but also individual economic behavior. It can also capture the equilibrium state of the macro-economy so as to achieve cross-industry and cross-regional policy evaluation. Whalley et al. [11] applied the CGE model to the problem of gas research candidate changes and developed a cross-country static CGE model involving global trade and carbon emissions to analyze the international effects of the carbon tax. Wing [12] incorporated technical details into the electricity sector of a computable general equilibrium model of the U.S. economy to characterize the technical margin of electricity adjustment to the carbon tax and to elucidate its general equilibrium effects. Wang et al. [13] developed a comprehensive model of endogenous technological change in the economy, energy, environment, and dynamics of the CGE model. Dai et al. [14] used a hybrid static CGE model to decompose the electric power sector into 12 power generation technologies and studied the gross domestic product (GDP) loss and CO₂ emission reduction under different carbon constraint scenarios. Guo et al. [15] investigated the impacts of levying a carbon tax on energy consumption and carbon emission based on a static CGE model. Li et al. [16] used a dynamic CGE model to evaluate the economic and climate impacts of a carbon emissions trading system, showing that carbon pricing is an important factor in China’s efforts to reduce CO_2, which showed that carbon pricing is an effective policy for reducing CO₂ emissions in China and found that linking carbon revenues to electricity tariffs is a more effective mitigation policy in the long run. Ojha et al. [17] explored the impacts of a revolving carbon tax on promoting inclusive green growth by investing carbon tax revenues in sectoral constructions using cleaner energy production using a dynamic recursive CGE model for India. Li et al. [18] established a pollution treatment sector for solid waste, wastewater, and exhaust management, collected satellite data for 18 pollutants, and evaluated China’s latest environmental tax policies through a CGE model. Fu et al. [19] established a stepped carbon tax CGE model, analyzed the interaction effects of different policy choices, and laid the foundation for the formulation of ideal carbon emission reduction policies. Zhang et al. [20] analyzed a pure carbon emissions trading system including only the electricity sector and a hybrid carbon tax and carbon emissions trading system in 2021 through a dynamic computable general equilibrium model and found that the synergistic use of carbon tax and carbon emissions trading system can promote the optimization of energy consumption structure. Guo et al. [21] constructed a recursive computable general equilibrium model with comprehensive sustainable energy modules to analyze the macroeconomic effects of multiple policies related to sustainable energy technologies advancement. Wang et al. [22] developed an ecological input–output CGE model that integrates a computable general equilibrium model, input–output analysis, and ecological network analysis to explore the evolution of the CO₂ emission metabolism under long-term carbon tax policies.

The above studies have used CGE models to investigate the impact of carbon tax policies or carbon trading systems on energy consumption, carbon emissions, and macroeconomic variables in various sectors from the perspectives of energy sector segmentation, carbon pricing methods, and tax redistribution design, respectively. In order to confirm the reliability of the above studies and make up for the limitations of the current CGE model assessment, this paper proposes to subdivide fossil energy, including coal, oil, and natural gas, into six subsectors, namely, coal mining, coking, petroleum extraction, petroleum utilization, natural gas extraction, and natural gas utilization, and construct a model database based on the data from China’s input–output tables and the Energy Statistical Yearbook to construct a computable general equilibrium model, which is used in different carbon reduction constraints, and is then used as a model for the calculation of the CGE. The CGE model is constructed with data from China’s input–output table and energy statistical yearbook, and a computable general equilibrium model is constructed to impose an ad valorem carbon tax under different scenarios of carbon emission reduction constraints and to study the impacts of the ad valorem carbon tax on the macroeconomic variables, such as energy consumption, social welfare, GDP, and the environmental variables, such as carbon dioxide emissions, and to further explore the differences in the carbon intensity of the eight energy commodities, namely, coal, oil, natural gas, coal-fired power generation, and clean energy, so as to provide the basis for formulating a reasonable carbon tax policy, and to analyze the future development trend of the energy-environment-economy based on the current national energy policy, so as to give the corresponding energy policy recommendations. This paper has four sections: Section 2 describes the modeling methodology, data sources, and carbon tax calculation; Section 3 gives the simulation results, discussion, and corresponding policy measures; and Section 4 gives suggestions for future research.

2. Methods

The CGE model follows the general equilibrium theory, which assumes that all factors and commodities are in a stable equilibrium state and assumes that the behavior of all economic agents is optimal, and that the optimal demand is equal to the optimal supply [23]. The CGE model is a mathematical framework for analyzing the changes in prices, quantities, and market supply and demand of all commodities and factors in the whole economic system, i.e., through the imposition of exogenous variables on the whole economic system of certain policy disturbances to study the impact of the transition of the economic system from one equilibrium state to another on the macroeconomic level [24]. In this paper, we constructed a multi-sectoral static CGE model for China, including a production module, trade module, household module, government module, enterprise module, macro closure module, social welfare module, and carbon tax module, and the main framework of the model is shown in Figure 1. This study involves 21 sectors, two factors of production (capital and labor), and four economic entities (residents, firms, government, and foreign). The CGE model was solved using the piecewise-linear approximation to the triangularization of the Hessian (PATH) solver of the General Algebraic Modelling System (GAMS) software (version 47). This section presents the data processing, production module, trade module, social welfare module, and carbon tax module.

2.1. Data Processing

Two types of data are needed for this study: (1) China’s social accounting matrix (SAM), which is subdivided into a macro social accounting matrix and a micro social accounting matrix; the macro-SAM can provide control figures for the micro-SAM, and the micro-SAM serves as an input to the CGE model. The SAM can describe in detail the economic situation of a country or region in a certain period of time. In this paper, the macro-SAM contains 10 accounts, and the main data come from the input–output table, China statistical yearbook, fiscal yearbook, the balance of payments report, etc. According to the sectoral characteristics and the characteristics of energy use, the 42 sectors in the input–output table are merged and decomposed into 21 sectors (the division rule is shown in Table 1) to construct the micro-SAM, and the required data come from the input–output table, the energy balance table, and the customs statistics, etc. (2). The remaining parameters include socio-economic parameters and fossil energy carbon emission parameters, the socio-economic parameters refer to the elasticity substitution parameters of the production function, the Armington function, and the CET function (see Table 2), which are derived from previous studies [25,26]; the fossil energy carbon emission coefficients are derived from the China Bureau of Statistics, the Intergovernmental Panel on Climate Change (IPCC) emission inventories, etc., as shown in Table 3.

2.2. Production Module

The production module describes the relationship between factor inputs and outputs in the domestic production sector. In this model, we assume that the market is perfectly competitive on the cost minimization principle, the market equilibrium conditions determine that output, and that the production module adopts a multilevel nested structure reflecting the complex substitution relationships between multiple inputs. As shown in Figure 1, this paper establishes a six-level nested production module with a constant elasticity of substitution (CES) function describing the substitution relationship between production factors at each level. The production inputs in the top layer are capital–labor–energy composite products and intermediate inputs without energy; the second layer consists of labor and capital–energy composite products; the third layer consists of capital and energy; the fourth layer consists of fossil and clean energy; the fifth layer consists of coal, oil, natural gas, and thermal power generation; and the sixth layer further subdivides coal, oil, natural gas, with coal consisting of coal mining and coking, oil consists of oil extraction and oil processing, natural gas consists of natural gas extraction and natural gas processing. As an example of the production function for the first tier, the main formulae for the production module are as follows:

(1)$\mathrm{Q}{\mathrm{X}}_i = {\alpha }_i^{\mathrm{qx}}{\left[{\delta }_i^{\mathrm{k}\mathrm{e}\mathrm{l}}{{\mathrm{KEL}}_i}^{{\rho }_i^{\mathrm{qx}}} + \left(1 - {\delta }_i^{\mathrm{k}\mathrm{e}\mathrm{l}}\right){{\mathrm{ND}}_i}^{{\rho }_i^{\mathrm{qx}}}\right]}^{1/{\rho }_i^{\mathrm{qx}}}$

where $\mathrm{Q}{\mathrm{X}}_i$, $\mathrm{K}\mathrm{E}{\mathrm{L}}_i$, and ${\mathrm{ND}}_i$ are total output, capital–labor–energy composite inputs, and non-energy intermediate inputs in sector i, respectively; ${\alpha }_i^{\mathrm{qx}}$ is a scale factor obtained by calculating the calibration; ${\delta }_i^{\mathrm{k}\mathrm{e}\mathrm{l}}$ is a share parameter; and ${\rho }_i^{\mathrm{qx}}$ is related to the elasticity of substitution ${\epsilon }_i^{\mathrm{qx}}$, ${\epsilon }_i^{\mathrm{qx}}={\displaystyle \frac{1}{1 - {\rho }_i^{\mathrm{qx}}}}$, the coefficients of elasticity of substitution occurring in the production module are all presented in Table 2. Therefore, the cost of production in sector i can be calculated by Equation (2).

(2)$\mathrm{P}{\mathrm{X}}_i\cdot {\mathrm{QX}}_i = \mathrm{K}\mathrm{E}{\mathrm{L}}_i\cdot \mathrm{P}\mathrm{K}\mathrm{E}{\mathrm{L}}_i + {\mathrm{ND}}_i\cdot {\mathrm{PND}}_i$

where $\mathrm{P}{\mathrm{X}}_i$, $\mathrm{P}\mathrm{K}\mathrm{E}{\mathrm{L}}_i$ and ${\mathrm{PND}}_i$ are aggregate output prices, synthetic prices of capital–energy–labor inputs, and synthetic prices of intermediate inputs for sector i, respectively. Then, the first-order optimization condition satisfying cost minimization is given by Equation (3).

(3)${\displaystyle \frac{\mathrm{P}\mathrm{K}\mathrm{E}{\mathrm{L}}_i}{\mathrm{P}\mathrm{N}{\mathrm{D}}_i}} = {\displaystyle \frac{{\delta }_i^{\mathrm{k}\mathrm{e}\mathrm{l}}}{1 - {\delta }_i^{\mathrm{k}\mathrm{e}\mathrm{l}}}}{\left({\displaystyle \frac{{\mathrm{ND}}_i}{\mathrm{K}\mathrm{E}{\mathrm{L}}_i}}\right)}^{1-{\rho }_i^{\mathrm{qx}}}$

The relationship between sectoral outputs, inputs, and prices can be calculated by associating the above production functions and optimization conditions.

2.3. Trade Module

The trade module describes the flow of consumption of goods in domestic and foreign markets. The import demand is described using the Armington assumption that imported goods and domestically produced goods are imperfect substitutes; the domestic product allocation is represented by the constant elasticity of transformation (CET) function. The formulae are shown below:

(4)${\mathrm{QQ}}_j = {\alpha }_j^{\mathrm{QQ}}\cdot {\left[{\delta }_j^{\mathrm{QDA}}\cdot {{\mathrm{QD}}_j}^{{\rho }_j^{\mathrm{QQ}}} + \left(1 - {\delta }_j^{\mathrm{QDA}}\right)\cdot {{\mathrm{QM}}_j}^{{\rho }_j^{\mathrm{QQ}}}\right]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\rho }_j^{\mathrm{QQ}}$}\right.}}$

where ${\mathrm{QQ}}_j$ represents the total demand of the domestic market, and ${\mathrm{QM}}_j$ represents the quantity of import goods. ${\alpha }_j^{\mathrm{QQ}}$ and ${\delta }_j^{\mathrm{QDA}}$ denote the scale and share parameter of the Armington function, ${\rho }_j^{\mathrm{QQ}}$ which is the elasticity of substitution between domestic sales and imports. The coefficients of elasticity of substitution appearing in the trade module are all presented in Table 2.

(5)${\mathrm{QX}}_j = {\alpha }_j^{\mathrm{CET}}\cdot {\left[{\delta }_j^{\mathrm{QD}}\cdot {{\mathrm{QD}}_j}^{{\rho }_j^{\mathrm{CET}}} + \left(1 - {\delta }_j^{\mathrm{QD}}\right)\cdot {{\mathrm{QE}}_j}^{{\rho }_j^{\mathrm{CET}}}\right]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\rho }_j^{\mathrm{CET}}$}\right.}}$

where ${\mathrm{QX}}_j$ represents the quantity of domestically produced goods, ${\mathrm{QE}}_j$ represents the quantity of export goods, ${\mathrm{QD}}_j$ represents the quantity of goods supplied in the domestic market. ${\alpha }_j^{\mathrm{CET}}$ and ${\delta }_j^{\mathrm{QD}}$ denote the scale and share parameter of the CET function, ${\rho }_j^{\mathrm{CET}}$ is the elasticity of substitution between domestic sales and exports, and ${\epsilon }_j^{\mathrm{CET}} = {\displaystyle \frac{1}{{\rho }_j^{\mathrm{CET}} - 1}}$.

2.4. Social Welfare Module

This study constructs the social welfare module of the population with the help of Hicks equivalent changes so as to measure the impact of exposure to external shocks on the social welfare of the population. The social welfare function is shown below:

(6)$\mathrm{EV} = {\displaystyle \sum _i{\mathrm{PQ}}_i^0\cdot {\mathrm{HC}}_i^1-}{\displaystyle \sum _i{\mathrm{PQ}}_i^0\cdot {\mathrm{HC}}_i^0}$

where $\mathrm{EV}$ represents social welfare valuation, ${\mathrm{PQ}}_i^0$ is the price of the goods before the policy was implemented, and ${\mathrm{HC}}_i^0$ and ${\mathrm{HC}}_i^1$ are the commodities demand of the households before and after the policy implementation.

2.5. Carbon Tax Module

The model assumes that carbon emissions come from the final consumption of fossil energy and, therefore, calculates CO₂ emissions by multiplying fossil fuel consumption with its corresponding potential carbon emission factor, which is calculated as follows:

(7)${\mathrm{CO}}_{2j} = {\displaystyle \sum _i{E}_{i,j}\cdot {\theta }_j} (j=coal, oil, gas)$

where ${\mathrm{CO}}_{2j}$ represents the CO₂ emissions of fossil energy j, ${\mathrm{E}}_{i,j}$ is inputs of fossil energy j in sector i, and ${\theta }_j$ is the corresponding emission factor. The relevant carbon emission factors for each fossil energy source are presented in Table 3.

In this paper, a carbon tax is levied on carbon emissions from the production sector, calculated using an ad valorem tax rate, which is the ratio of the tax revenue from the carbon tax levied on each type of fossil energy source to the value of the domestic demand for that fossil energy source, and the tax revenue levied is used for the government’s general budgetary management, assuming that there are no carbon tax incentives or rebates in place. The formula for the carbon tax module is shown below:

(8)${\mathrm{TCO}}_{2j}=tax\cdot {\mathrm{CO}}_{2j}$

where ${\mathrm{TCO}}_{2j}$ represents a carbon tax levied on fossil energy j, $tax$ is the specific duty rate of the carbon tax.

(9)$ta{x}_j^{\mathrm{ad}}={\displaystyle \frac{{\mathrm{TCO}}_{2j}}{{\mathrm{PQ}}_j\cdot {\mathrm{QQ}}_j}}$

where $ta{x}_j^{\mathrm{ad}}$ represents the ad valorem duty rate of fossil energy j, ${\mathrm{PQ}}_j$ and ${\mathrm{QQ}}_j$ denote the market price and domestic demand for fossil energy j, respectively.

3. Analysis and Discussion

3.1. Energy Contribution and Carbon Tax Under Carbon Emission Reduction

The higher the carbon emission reduction target, the larger the equilibrium carbon tax rate, but the slower the growth rate. As shown in Table 4, when the carbon emission reduction rate changes from 5% to 25%, the equilibrium carbon tax rate grows from 16.9225 to 90.4882, which is an amazing increase, and the equilibrium carbon tax rate only increases to 93.5786 in the interval of the carbon emission reduction rate from 25% to 35%, indicating that the marginal cost of carbon emission reduction is on a downward trend. This table also reflects the changes in ad valorem tax rates for fossil fuels with different carbon emission intensities and their contribution to carbon emission reductions. The ad valorem carbon tax internalizes the cost of environmental abatement into the cost of using each fossil energy source through the price mechanism, so the ad valorem carbon tax rate can reflect the difference in the carbon intensity of each fossil energy source. As the carbon tax rate increases, the ad valorem tax rates of all six fossil fuels show an upward trend, but with different rates of increase. Among them, coal mining and coking has the highest absolute value of ad valorem tax rate, which means that it contains high carbon intensity and corresponds to the highest environmental abatement cost, so that as its use cost increases, its energy demand decreases drastically, and therefore its contribution to the abatement of emissions is also the largest.

Under the most stringent carbon emission reduction scenario, the ad valorem tax rates for coal mining and coking increase to 0.4906 and 0.4510, respectively, which is more than ten times higher than the ad valorem tax rates for the other four fossil fuels, and this price trend also implies a significant increase in the cost of fuels for most industrial sectors. Unlike the ad valorem tax rate, as the carbon emission reduction rate increases, the emission reduction contribution of coal mining is on a downward trend, while coking, petroleum processing, and natural gas extraction are on an upward trend, which indicates that the substitution effect between different fossil energy sources is helping the country’s transition towards green energy.

3.2. Impact on Macroeconomic Variables

Various macroeconomic indicators under different carbon emission reduction scenarios can reflect the socio-economic impact of the imposition of a carbon tax in order to strike a balance between socio-economic development and carbon emission reduction. Table 5 demonstrates various macroeconomic indicators, including residential consumption, government consumption, government revenue, investment, import and export, and real GDP. The decreasing trend of real GDP with the growth of carbon emission reduction targets shows that the CGE model used in this paper can reflect the complexity of the nonlinear structure of the socio-economic system. As the emission reduction target becomes stricter and stricter, the production process of the whole system may decline sharply. When the carbon emission reduction standard is between 5% and 15%, the impact of levying a carbon tax on economic development is relatively light. In comparison, once the 15% target is exceeded, its impact on consumption, investment, and social welfare will be significant, which is difficult for developing countries such as China to accept, especially the decline in the total amount of investment, which means that the amount of capital available to the entire market is reduced significantly. The related capital outflow will have an irreversible impact. In addition, the change in government consumption can be seen from the fact that when the carbon emission reduction target is 5%, the collection of the carbon tax increases the government’s fiscal revenue, which the government uses to stimulate the national economy through consumption, but this stimulus is obviously far from enough compared to the vicious circle of the economy caused by the high carbon tax. The high carbon tax will also trigger a series of problems, such as inflation and rising unemployment, and it is incumbent upon the government to formulate a reasonable carbon tax policy. Considering the impact of changes in macroeconomic variables, it is a relatively reasonable choice for the ad valorem carbon tax to set the carbon reduction target at 5–15%.

3.3. Impact on Energy Consumption by Sector

According to the nature of the industry, the manufacturing and processing of food and tobacco, the manufacturing and processing of textiles and related products, and the processing and manufacturing of wood, paper, printing, and cultural, educational, and sporting goods can be categorized as light industry, and the chemical industry, the manufacturing of non-metallic mineral products, the smelting, pressing and manufacturing of metals and related products, the manufacturing of machinery and equipment, and the manufacturing of communication equipment, measuring instruments and other manufacturing industries can be categorized as heavy industry. The thirteen non-energy sectors are grouped into six categories: agriculture, light industry, heavy industry, construction, transport, and services. Figure 2, Figure 3 and Figure 4 show the energy input changes of the above six industries under different carbon emission reduction scenarios. The energy input when no carbon tax is imposed is taken as the benchmark data, and the energy input change percentage after the carbon tax is imposed is calculated. The column height represents the size; a positive number indicates an increase in energy input, and a negative number indicates a decrease in energy input. Orange and light green represent changes in coal mining and coking, purple and yellow represent changes in oil mining and processing, dark blue and pink represent changes in natural gas mining and processing, and dark green and light blue represent changes in thermal power and clean power.

As shown in Figure 2, with the gradual increase of carbon emission reduction targets, the input of coal mining and coking in agriculture, construction, transportation, and service industries decreases significantly, which is consistent with the trend reflected by the ad valorem carbon tax and the contribution rate of carbon emission reduction in Table 4, and reflects the price effect brought by the imposition of carbon tax, that is, the input price of coal mining in the production process increases significantly. Forcing the production sector to reduce its use of coal mining in favor of alternatives, such as clean energy, the amount of clean energy input in each sector in Figure 2 increases significantly with the increase in carbon reduction targets. In addition, thermal power generation can indirectly reflect the impact of carbon taxes on coal, so the input of thermal power generation also decreases with the increase of carbon reduction targets. However, the change is smaller than that of coal mining and coking. Also affected by the emission coefficient, the impact of carbon tax on petroleum processing is relatively significant, and the input of petroleum processing in agriculture, construction, transportation, and service industries all decreases with the deepening of carbon emission reduction. Interestingly, natural gas extraction and processing in transportation and services showed an energy substitution effect, but to a lesser extent than clean energy.

Figure 3 shows the change in energy input of the light industry sector after the imposition of a carbon tax, and its trend is roughly the same as that of agriculture, construction, transportation, and other sectors in Figure 2. However, the energy substitution effect of natural gas processing and clean energy is relatively stronger in all light industry sectors, and the textile industry even shows the triple substitution effect of oil processing, natural gas processing, and clean energy. Figure 4 shows the changes in energy input in heavy industry after the imposition of a carbon tax. Compared with various heavy industry sectors, the changes in energy input in machinery manufacturing are special, all of which are income effects; that is, the demand for energy input decreases significantly with the rise of energy prices. In the manufacturing of non-metallic mineral products, smelting, pressing, and manufacturing of metal and related products, as well as communication equipment, measuring instruments, and other manufacturing sectors, with the rise in the price of coal mining, coking, and thermal power generation, the energy substitution effect has gradually emerged, and the demand for oil and gas mining, processing and clean energy has increased significantly, of which clean energy has experienced the largest change. This shows that clean energy has a broad prospect and can effectively alleviate the consumption demand for high-emission fossil energy in various sectors.

3.4. Changes in Energy Sector Output

In addition to energy input in non-energy sectors, changes in the share of output in each energy sector can further reflect the impact of carbon taxes on the energy transition. As shown in Figure 5, with the increase of carbon emission reduction targets, the proportion of coal mining and coking in total energy output has gradually decreased, especially coal mining, with a very significant decline. In contrast, the output of oil and natural gas has slightly increased, indicating that there is an energy substitution effect between fossil energy sources. Rising coal prices can promote the production of oil and natural gas. Thermal power generation, as the cornerstone of national energy, has little change in the proportion of total energy output, and clean energy has shown a relatively strong proportion growth, which shows that the carbon tax can gradually increase the proportion of clean energy and promote China’s transition to clean power production.

3.5. Potential Measures to Reduce Carbon Emissions: The Future of Emerging Clean Energy Sources Such as Hydrogen

China, as a large power producer and consumer of electricity, has seen its energy demand continue to rise. A carbon tax is imperative to meet the environmental demand for carbon emission reduction. The above comprehensive environmental-economic-energy analysis of the implementation of carbon tax shows that the energy substitution effect of clean energy helps all sectors of society to deeply decarbonize and realize an all-round energy clean transition and that the vigorous development of clean energy is the top priority for China to realize carbon peaking and carbon neutrality. In recent years, China has enacted a series of energy policies, including increasing the share of non-fossil energy in total energy consumption, strengthening key core technologies, deepening the reform of the electric power system, and providing government subsidies to incentivize the development of new energy sources, in order to ensure energy security and promote the green, low-carbon transformation and high-quality development of the energy sector. Among them, hydrogen energy, as an emerging energy source, is clean and low-carbon, flexible and efficient, and has abundant application scenarios, and thus has received widespread attention. The 2023 International Hydrogen Energy Technology and Industry Development Research Report predicts that by 2030, the global demand for hydrogen energy will be more than 150 million tons, and the scale of the hydrogen energy industry will double to United States Dollar (USD) 500 billion. By 2050, global hydrogen energy demand will increase 10-fold from 2022, and the size of the hydrogen energy industry will exceed USD 2.5 trillion. According to a joint report by the International Hydrogen Energy Council and management consulting firm McKinsey & Company, by 2050, hydrogen energy will contribute more than 20% to global carbon emissions reduction, which could cumulatively help reduce 80 billion tons of CO₂ emissions.

In terms of environmental protection, whether hydrogen combustion or through the electrochemical reaction of the fuel cell, its products are free of pollutants and carbon emissions. The development of green hydrogen also helps to reduce the energy dependence on coal and natural gas, to help the industry in-depth decarbonization, and is expected to achieve true zero carbon emissions. In terms of energy development, the energy density of hydrogen is extremely high, and each kilogram of hydrogen can release 33.3 kilowatt-hours of energy, which is much higher than that of traditional fossil energy sources, so the use of hydrogen energy can improve the efficiency of energy use. At the same time, hydrogen energy production methods are diversified, both through the production of fossil fuels but also as a bridge to renewable energy sources, the use of wind and solar excess power for water electrolysis hydrogen production, to solve the problem of intermittent renewable energy sources, to achieve energy recycling, combined with energy storage systems to improve the stability of the power system, and to further improve the efficiency of energy use. Hydrogen energy utilization flexibility provides new decarbonization ideas for industrial sectors such as steel, chemical, and other hard-to-electrify industries [27,28]. Hydrogen energy in the industrial sector can provide raw materials, reductants, and high-grade heat sources for refining, steel, metallurgy, and other industries, effectively reducing carbon emissions and promoting the energy structure’s diversification and cleanliness. In terms of economic development, the development of hydrogen energy can bring huge investment opportunities, promote innovation in manufacturing, infrastructure, energy technology, and other fields, drive the development of the energy industry chain, expand energy domestic demand, promote the popularization of renewable energy, create new jobs, reduce costs and increase efficiency, enter the international trade market, and bring more economic benefits to the country. Taken together, hydrogen energy is an ideal energy storage medium for realizing the low-carbon and clean transition of the energy system and occupies an indispensable position in the field of clean energy.

4. Conclusions

In summary, this paper subdivided fossil energy into six categories and levied an ad valorem carbon tax on the production process of each sector. The trend of the equilibrium carbon tax rate under different carbon abatement scenarios is described, which increases with the CO₂ abatement rate, but its increase slows down from a steep increase. There is an extreme point of marginal abatement cost, in addition to the difference in the carbon intensity of each fossil energy source, and its carbon dioxide emission coefficient is strongly correlated, in which coal mining and use and petroleum processing occupy a dominant position in the process of carbon dioxide abatement.

Analyzing the impact of levying carbon tax on macro variables, it can be found that when the carbon dioxide emission reduction rate is increased from 0 to 35%, the substantial reduction of residents’ consumption, government consumption, import and export, investment and social welfare highlights the adverse impact of the carbon tax policy on the socio-economy. However, when the carbon reduction target is set at below 15%, the impact on social welfare is relatively small, and if the total factor productivity or energy efficiency can be further improved, the ‘double dividend’ of carbon dioxide reduction and positive economic growth can be achieved, which is in line with the expected goal of the dual carbon emission reduction policy. Therefore, a balanced carbon tax price of 54 yuan/ton or less is a relatively reasonable range.

Summarizing the energy inputs of various sectors under different carbon tax scenarios, it is possible to reflect the energy substitution effect of natural gas and clean energy on coal, oil, and thermal power generation under the socio-economic conditions, and clean energy has a greater development potential, which is a key initiative for China to achieve carbon peak and carbon neutrality, among which hydrogen energy, as a clean and pollution-free secondary energy, can be combined with renewable energy. Improving the stability of the power system and providing a new decarbonization path for industrial sectors that are difficult to electrify, such as steel and metallurgy, are indispensable and important components of clean energy.

The shortcomings of the CGE model are mainly reflected in that although it can carry out dynamic analysis (through recursive dynamic or endogenous dynamic mechanism), it still relies on static assumptions in some aspects, such as production technology, consumer preferences, factor supply, etc. These static assumptions may not adequately capture the dynamic effects of carbon tax policies on technological progress, long-term investment, or changes in consumer behavior. Secondly, the simulation of energy substitution elasticity and technological innovation in the CGE model is ideal. In the actual production process, energy substitution may be limited by technology, cost, and policy environment, and it is difficult for the CGE model to capture the complexity of these factors.

In the future, we will combine dynamic models (such as dynamic stochastic general equilibrium models, DSGE) or the multi-phase dynamic analysis framework and improve the existing CGE model to simulate technological progress, capital accumulation, policy changes, and the evolution of consumer behavior over time to capture long-term technological changes and behavioral adjustments better. Second, we will set the elasticity of energy substitution as a non-uniform parameter to characterize differentiated substitution capabilities across technologies, regions, and sectors, and further segment the clean energy industry. In addition, policy uncertainty and market volatility will be introduced into the CGE model to reflect the complexity of the actual economic environment and empirical data will be used to correct key variables in the model, such as elastic parameters, energy substitution costs, and technology diffusion speed, to improve the prediction accuracy of the model. Finally, we will distinguish between-group differences and regional differences in the model so as to more accurately reflect the distributional effects of different groups and regions in the process of energy substitution and better analyze the unbalanced impact of carbon tax on regional economic development.

Author Contributions

Conceptualization, K.F.; Methodology, K.F., Y.Z., L.J. and B.W.; Software, Y.Z.; Validation, Y.Z.; Formal analysis, Z.Y.; Investigation, B.W.; Resources, Z.L.; Data curation, L.J.; Writing—original draft, K.F. and L.J.; Writing—review & editing, Z.Y., B.W. and Z.L.; Visualization, B.W. and Z.L.; Supervision, Z.Y. and B.W.; Project administration, Z.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

Authors Keying Feng, Zeyu Yang and Yu Zhuo were employed by the company Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

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Figures and Tables

Figure 1. The main framework of CGE model.

Figure 2. Changes in energy inputs in agriculture, buildings, transport, and service under different carbon reduction constraints.

Figure 3. Changes in energy inputs in the light industry under different carbon reduction constraints.

Figure 4. Changes in energy inputs in the heavy industry under different carbon reduction constraints.

Figure 4. Changes in energy inputs in the heavy industry under different carbon reduction constraints.

Figure 5. Changes in energy inputs by sector under different carbon reduction constraints.

Table 1

The division rule of sectors in the I/O Table of China.

Sector Number	Sector Name	Initial Sectors in the I/O Table of China
1	Agriculture, Forestry, Farming of Animals and Fishing	Agriculture, Forestry, Farming of Animals and Fishing
2	Mining and Processing of Other Ores	Metal Ore Mining Non-metal Minerals and Other Mining
3	Manufacturing and Processing of Food and Tobacco	Manufacture and Tobacco
4	Manufacture and Processing of Textiles and Related Products	Textile and Knitting ProductsTextile, Clothing, Shoes, Hats, Leather, Down, and Related Products
5	Processing and Manufacture of Timber, Paper, Printing, and Articles for Culture, Education, and Sport Activities	Processing and Manufacture of Timber and FurnitureManufacture of Paper, Printing, and Cultural, Sporting, Athletic, and Recreation Products
6	Chemical Products	Chemical Products
7	Manufacture of Non-Metallic Mineral Products	Manufacture of Non-metallic Mineral Products
8	Smelting and Pressing and the Manufacture of Metals and Related Products	Smelting and Pressing of MetalsMetal ProductsServicing of Metal Products, Machinery and Equipment
9	Machinery and Equipment	General Purpose MachinerySpecial Purpose MachineryTransport EquipmentElectrical Machinery and Equipment
10	Communication Equipment, Measuring Instruments, and Other Manufacturing	Manufacture of Communications Equipment, Computers and Other Electronic EquipmentInstruments, Meters, and Other Measuring EquipmentOther Manufacturing Products and Scrap Waste
11	Construction	Construction
12	Transport, Storage, and Post	Transport, Storage and Post
13	Service	Information Transfer, Computer Services and SoftwareWholesale and Retail TradeProduction and Distribution of WaterAccommodation and CateringFinanceReal EstateTenancy and Business ServicesResearch and Experimental DevelopmentProfessional Technique ServicesManagement of Water Conservancy, Environment, and Public EstablishmentResident Services and Other ServicesEducationSanitation, Social Security, and Social WelfareCulture, Sports, and EntertainmentPublic Management and Social Organization
14	Mining and Washing of Coal	Mining and Washing of Coal
15	Coking	Petroleum Processing, Coking, and Nuclear Fuel Processing
16	Petroleum Processing
17	Extraction of Petroleum	Extraction of Petroleum and Natural Gas
18	Extraction of Natural Gas
19	Production and Distribution of Gas	Production and Distribution of Gas
20	Production and Distribution of Thermal Power	Production and Distribution of Electric Power and Heat Power
21	Production and Distribution of Clean Power

Table 2

The coefficients of the elasticity of substitution.

	Agri	Meta	Food	Texi	Manf	Chem	Nonm	Equi	Othm	Cons	Whol	Tran	Teri	Coalm	Coking	Petre	Petrp	Gase	Gasp	Therp	Clep
rhoQx	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3
rhoKEL	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8
rhoKE	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6
rhoE	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2
rhoFE	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2
rhoCoal	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25
rhoGas	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6	1.6
rhoOetr	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25	1.25
rhoEle	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3
rhoQq	3	2.5	2.5	2.5	2.5	2.5	2.5	2.5	2.5	2	2	2	2	2	2.5	2.5	2.5	2.5	2.5	1.1	1.1
rhoCET	4	3.5	3.5	3.5	3.5	3.5	3.5	3.5	3.5	3	3	3	2.5	2.5	3.5	3.5	3.5	3.5	3.5	0.5	0.5

Table 3

The carbon emission coefficients.

Fuel Types	Ton (CO2)/104 Ton
coalm	28.1026
coking	24.9222
petre	1.8768
petrp	4.2889
gase	0.649
gasp	1.1661

Table 4

Impact of different CO₂ reduction constraints on energy consumption.

		Carbon Emission Reduction
		5%	15%	25%	35%
Equilibrium carbon tax rate (Yuan/t)		16.9225	54.1569	90.4882	93.5786
Ad valorem duty rates	Mining and Washing of Coal	0.0476	0.1599	0.3028	0.4906
	Coking	0.0424	0.1437	0.2749	0.4510
	Extraction of Petroleum	0.0032	0.0110	0.0214	0.0358
	Petroleum Processing	0.0073	0.0251	0.0486	0.0810
	Extraction of Natural Gas	0.0011	0.0038	0.0074	0.0123
	Production and Distribution of Gas	0.0020	0.0068	0.0132	0.0219
Contribution to carbon emission reduction	Mining and Washing of Coal	85.76%	85.23%	84.58%	83.85%
	Coking	12.32%	12.62%	12.91%	13.20%
	Extraction of Petroleum	0.01%	0.01%	0.01%	0.01%
	Petroleum Processing	1.86%	2.13%	2.47%	2.90%
	Extraction of Natural Gas	0.00%	0.01%	0.01%	0.01%
	Production and Distribution of Gas	0.01%	0.01%	0.01%	0.01%

Table 5

Impact of different CO₂ reduction constraints on macroeconomic variables.

	Carbon Emission Reduction
	5%	15%	25%	35%
Equilibrium carbon tax rate (Yuan/t)	16.9225	54.1569	90.4882	93.5786
Real GDP	−0.1481%	−0.5196%	−1.0170%	−1.6847%
Household consumption	−1.3145%	−8.5394%	−21.9229%	−52.1563%
Government consumption	−0.0046%	−4.7721%	−16.3402%	−47.1222%
Government total income	1.5668%	−0.2726%	−9.7066%	−41.1768%
Total investment	−1.6520%	−9.5164%	−23.3799%	−53.4783%
Export	−1.4234%	−8.8383%	−22.3438%	−52.5157%
Import	−1.4255%	−8.8272%	−22.2954%	−52.4351%
Social welfare	−0.0924%	−0.3364%	−0.6815%	−1.1668%

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