How does carbon trading scheme affect carbon emissions and economic performance of electricity cross-subsidy reform: A recursive dynamic CGE model in China

As a pricing strategy, electricity cross-subsidies mainly form the cost and price difference among various market segments under the role of the government's dominant position, so as to achieve the goals of industrial support and welfare transfer. It widely existed on the production side (Shrimali and Tirumalachetty, 2013; Percebois and Pommeret, 2018; and Meya and Neetzow, 2021) and the consumption side (Nahata , 2007; Bhattacharyya and Ganguly, 2017; Dong , 2022) of the electric power industry in various countries. In China, cross-subsidies of electricity on the consumption side are mainly divided into three categories: developed area users, high-voltage users, and industrial and commercial users subsidize less developed area users, low-voltage users, and residential and agricultural users, respectively.

In the early stage of the reform, the cross-subsidy has played a positive role in expanding the power consumption of residents and promoting the power payment capacity of vulnerable groups. Nevertheless, with the further deepening of the market-oriented reform, the drawbacks of cross-subsidies for electric power have gradually emerged. First, it is not beneficial for the optimal allocation of resources, which leads to the low efficiency of resource utilization and the obstruction of economic restructuring, thus affecting the realization of poverty reduction and equity. Second, the price formed by cross-subsidies is inconsistent with the principle of market supply and demand, and the market price signal is wrong, which easily leads to the problem of low consumption efficiency. Therefore, the reform of electricity cross-subsidy has almost become a consensus in the field of energy economics.

At present, China's electricity prices are generally low, and industrial users are heavily subsidized to residential users, which is the major characteristic of the electricity price cross-subsidy at this stage. In the International Comparison of Electricity Prices in 2019, the average sales electricity price in China is only 0.635 yuan/kWh, which is significantly lower than 1.029 and 0.759 yuan/kWh in OECD and newly industrialized countries. Meanwhile, the ratio of electricity prices between residential and industrial users is 0.85 in China, while OECD countries and the United States are up to 1.53 and 1.91, respectively (The State Council, 2021a). As early as 2015, the Opinion on Further Deepening the Reform of the Electric Power System was proposed to implement the reform of power transmission and distribution prices (The State Council, 2015). Then in 2018, the Government Work Report mentioned the following: “cut power grid charges and transmission and distribution prices, and reduce general industrial and commercial electricity prices by 10%” (The State Council, 2018). Later, the Central Economic Work Conference again stated that “we should reduce the cost of electricity for enterprises” in 2019. Subsequently, the Notice on Phasing Down Electricity Costs for Enterprises to Support Enterprises in Resuming Work and Production and other policies were issued successively in 2020 (The National Development and Reform Commission, 2020). Overall, the key to electricity price reform is to eliminate the implicit cross-subsidy that distorts electricity prices and straighten out the pricing mechanism to generate a price level that truly reflects the value of electricity.

With the continuous promotion of the cross-subsidy reform, the efficiency of resource allocation is improved, while the electricity price of the industrial sector is gradually reduced. At this time, due to the high proportion of electricity consumption in the industrial sector dominated by energy-intensive industries, the low electricity price brought about by the cross-subsidy reform could further promote the excessive input of power production factors in relevant industries, increasing carbon emissions. During this period, to alleviate the pressure of carbon emission reduction, the electric power industry implemented a market-oriented environmental policy–carbon emission trading. In 2021, the Measures for the Administration of Carbon Emission Trading was issued, which stipulated the quota allocation scheme and the list of key emitters (The State Council, 2021b). Since then, the first compliance cycle of the power generation industry in the national carbon market was officially launched. There are 2225 key emitters in the power generation industry involved in the market, and the total emission scale is estimated to exceed 4 × 10⁹ tons of carbon dioxide, accounting for about 40% of the total national carbon emissions. Electricity has become an important industry that affects China's green and low-carbon transformation and development. The carbon emission trading internalizes the marginal emission reduction cost through the spot trading of electricity, which promotes the transfer of carbon costs to the electricity prices, and then transmits to the electricity sales prices of different consumer groups, affecting the implementation effect of the cross-subsidy reform.

This paper quantitatively analyses the economic and carbon emissions impact of the cross-subsidy reform and the carbon trading policy, and explores whether the carbon emission trading promotes the realization of carbon emission reduction at the cost of lower economic losses. Firstly, it calculates the electricity prices after the reform through the Ramsey pricing model. Then, a recursive dynamic computable general equilibrium (CGE) model is adopted to explore the economic and CO₂ emissions' impacts of electricity cross-subsidy reform under the different settings of the carbon emission trading scheme (ETS).

The innovations of this paper are as follows: (1) In this paper, the price level after the cross-subsidy reform is measured by the Ramsey pricing model and considers the transmission and distribution costs. Compared with the marginal cost pricing or transmission and distribution cost accounting method commonly used by other scholars, the Ramsey pricing model can not only maximize the total social surplus while minimizing the negative impact on resource allocation but also ensure that manufacturers achieve minimum profit demand. (2) The paper explores the impact of cross-subsidy reform on economic benefits and carbon emissions to avoid one-sided conclusions caused by ignoring the policy's contradictory economic and environmental effects. Compared to other relevant literature, the joint effect of cross-subsidy reform and carbon trading policy is explored for reality. (3) When exploring the policy effect of carbon trading policy, this paper comprehensively considered the main elements of the mechanism: quota discount scheme, quota allocation mode, and responding penalty mechanism. Compared to other scholars who only focus on a certain aspect of mechanism design, it can study the differences in emission reduction efficiency of coordinated parameter settings.

The rest of the paper is organized as follows. Section II summarizes the relevant literature, Section III displays the methodology and relevant data required for the research, Sec. IV analyzes the empirical results, and Sec. V puts forward relevant suggestions for the research conclusions.

Properly handling the electricity cross-subsidy is a vital task of the power market-oriented reform and is also the key to reflecting electricity costs and optimizing resource allocation efficiency (Pu , 2020). Accordingly, relevant scholars have explored the issue from three aspects: the calculation of cross-subsidies, the rationality of reform, and the optimization of reform methods. Among them, since the fact that each type of customer belongs to multiple voltage levels and the calculation of transmission and distribution cost is more complicated, the relevant scholars generally adopt the price difference method to calculate the number of cross-subsidies (Lin , 2009; Li , 2011; and Zheng , 2023). Lin (2009) calculated that electricity cross-subsidy of China for residential users is 209.76 × 10⁹ yuan in 2007, accounting for 0.84% of gross domestic product (GDP). Zheng (2023) pointed out that between 2015 and 2019, the supply side of cross-subsidies, mainly for large industrial users, provided nearly 6 × 10⁹ yuan of cross-subsidies to residential users annually.

Furthermore, to clarify whether the high level of cross-subsidies has achieved the goals of industrial support and welfare transfer, relevant scholars have analyzed the benefits of cross-subsidies (Tang and Yang, 2014; Bhattacharyya and Ganguly, 2017; Cardenas and Whittington 2019; and Jia and Lin, 2021). Among them, Tang and Yang (2014) measured the degree of distortion in residential electricity prices and the target group benefit index of the price subsidies in China and concluded that the largest beneficiaries of GSP low electricity price subsidies are not low-income groups. Cardenas and Whittington (2019) obtained the same conclusion, and the results showed that only 7% of the total subsidies distributes to the poorest quintile households, while the richest quintile received 37%. On this basis, if the subsidies are completely removed, it will be unaffordable for the vulnerable groups to consume electricity and will also have a negative impact on the industrial structure, CO₂ emission, and social welfare (Bhattacharyya and Ganguly, 2017; Jia and Lin, 2021).

To this end, it has become a consensus to reform the existing cross-subsidy policy. Khalid and Salman (2020) proposed a targeted subsidy approach, which can save financial expenditure and improve the welfare of vulnerable social groups compared to the increased deadweight losses due to the huge subsidies. Pu (2021) proposed the optimal allocation mode of cross-subsidy among industrial and commercial users based on calculating the scale of cross-subsidy of China's transmission and distribution electricity prices. Dong (2022) explored the optimal sectoral pricing of electricity consumption in China from multiple dimensions, such as efficiency, equity, environmental protection, and supply constraints. Meanwhile, effective energy subsidies also provide new ideas for the electricity cross-subsidy reform to balance the developmental relationship between efficiency and welfare (Lin and Liu, 2016).

In addition, the implementation of electricity cross-subsidy reform also has to consider other relevant policies to ensure policy coordination. At present, electricity, as the main source of carbon emissions, has become a key sector included in the carbon trading market, which is bound to have an impact on the effect of the reform (Liu , 2020). Therefore, relevant scholars have conducted extensive research on carbon trading from the aspects of mechanism design and policy effects. In the design of carbon emission trading scheme, the setting of the total quota, the method of initial quota allocation, and the penalty mechanism are the main components (Gao and Wang, 2021). Total quota setting mainly involves two types of methods, the baseline method and the historical method (also known as the grandfather method). The baseline method is based on the industry benchmark value of carbon emission intensity and emphasizes fairness, while the historical emission method is based on the carbon emission data in the past period and emphasizes data availability (Li and Jia, 2016). The method of initial quota allocation mainly analyzes the proportion of free quotas and auction quotas, while the key to the penalty mechanism is to set the punishment coefficient reasonably (Tang , 2016; Lin and Jia, 2018). Otherwise, there are also differences in the policy effectiveness of different carbon trading revenue return methods (Cai , 2017).

In order to design a scientific mechanism, relevant scholars have explored the effects of carbon trading policy under different design approaches and parameter settings on economic effects, emission reduction, energy efficiency, green innovation, and social equity (Lopez , 2018; Zhang , 2020; Wang , 2021; Kiss and Popovics, 2021; Huang , 2019; Chen , 2021; and Liu , 2022). From the perspective of emission reduction effectiveness, which is the most critical factor, the efficiency of carbon trading in providing emission reduction incentives to energy producers and final consumers depends on whether the emission reduction cost can be transferred to the price of industrial products (Jouvet and Solier, 2013). Cost transfer is influenced by various factors, such as market structure, market strategy, carbon quota allocation, and carbon emission intensity (Sijm, 2012; Bushnell and Chen, 2012; and Nazifi , 2021). Since electricity is the largest carbon emission source sector, the impact of carbon emission trading on electricity prices is more obvious than that of other commodities (Li , 2018; Teng , 2014; Liu , 2016; Lin and Jia, 2019; and Cludius , 2020). Kara (2008) indicated that for every ton of CO₂ emitted in the Nordic region, the annual average electricity price would rise by 0.74 EUR/MWh.

To sum up, the studies on electricity cross-subsidy in the relevant literature mainly focus on the scale measurement, effectiveness analysis, and reform methods, while there are few empirical analyses on the post-reform policy effects and the joint effects of other related policies. At the same time, relevant scholars have conducted extensive research on the carbon trading policy mainly from the perspective of the mechanism design and policy effects, and few studies have conducted comparative analysis on different mechanism designs. Consequently, this paper measures the electricity prices' level of different users through the Ramsey pricing mechanism and examines the differences in the economic and CO₂ emission effects of the electricity cross-subsidy reform under different scenario settings of the ETS. Based on this, we can ensure the realization of the dual control targets of industrial electricity tariff reduction and energy conservation and emission reduction through coordinating the cross-subsidy reform and carbon trading.

The reform of electricity cross-subsidy is essentially the transformation in the pricing method of electricity sales price, gradually shifting from government pricing to market competitive pricing. As an essential industry of national economic and social development, electricity pricing should conform to the basic market discipline and ensure the inherent characteristics of public service. Marginal cost pricing has the highest resource allocation efficiency and social welfare, but the fixed investment costs cannot be compensated, which is not conducive to the continuous operation of power enterprises. On this basis, the Ramsey pricing model aims to maximize the total social welfare under the constraint of covering the fixed investment costs, which can achieve suboptimal pricing based on marginal cost pricing. In this process, it considers the demand elasticity of users, minimizing the negative impact on resource allocation. It is more applicable to regulated industries (such as utilities) and nonprofit industries (expected to compensate for costs), just like electricity. Hence, this paper adopts the Ramsey pricing model to measure the electricity prices after implementing the electricity cross-subsidy reform.

In the study of policy effect, the DSGE model and the dynamic CGE model have both been widely applied, but their main application directions are different. The DSGE model is based on theoretical modeling and numerical simulation to analyze the interaction between macroeconomic operation characteristics and microeconomic subject behavior decisions. It takes the inter-temporal optimal choice and the uncertainty factors (such as technology shock, price shock, and preferences shock) into consideration to discuss economic growth, economic cycles, and the effects of monetary and fiscal policy (Chan, 2020; Xin and Jiang, 2023; and Sun , 2023). However, the introduction of virtual exogenous shocks could significantly impact the results. The dynamic CGE model mainly applies actual economic development data to describe the relationship among markets and sectors. It explored the dynamic change of equilibrium in multiple periods with the endogenous mechanism of production factors supply and is widely used to analyze the impact of policies on national or regional welfare, industrial structure, environmental conditions, etc. (Lu , 2010; Li and Jia, 2016; and Mayer , 2021). This paper mainly explores the effect of cross-subsidy reform and carbon trading policy on carbon emissions and economic performance, which focuses on analyzing the details of economic structure and the relationship between sectors. Therefore, the dynamic CGE model is more suitable.

Figure 1 displays the overall structure of the CGE model under market equilibrium. The model includes four economic agents: government, residents, enterprises, and foreign countries, and the whole economy is divided into 21 production sectors, including seven energy sectors: electricity, coal, oil, gas, coke, refined oil, and natural gas production. In addition, the model is composed of six modules: production, trade, income–expenditure, macro closure and market clearing, ETS, and dynamic mechanism. All modules cooperate and interact with each other to shape the overall framework of macroeconomic operation.

As the basic module, the production module in the model displays the relationship between the input and output of domestic products, involving intermediate products and production factors. Except that the Leontief production function with substitution elasticity of 0 is applied in the first level and the nested level of intermediate input, production behavior in other nested combinations is described by the constant elasticity of substitution (CES) function. In the trade module, this paper does not set up production and consumption modules for foreign countries, but only considers commodity and services trade between domestic and foreign countries, foreign direct investment, and foreign transfer income to the government. Among them, the import and export of domestic goods adopt the “small country hypothesis,” which means that under given price conditions, the import and export volume of goods are set as endogenous variables and are not restricted. For the domestic, the constant elasticity of transformation (CET) function is used to describe the domestic product distribution, which is the optimal strategy of product distribution among different markets under certain production technology constraints. Domestic product demand adopts the function form of the Armington hypothesis, which minimizes the consumption cost of imported and domestic products.

The income and expenditure functions of residents, firms, and governments constitute the income and expenditure modules. Among them, the consumption function of residents adopts LES linear function, and the others are proportional linear functions. The macro closure and the market clearing module consist of factor market equilibrium, commodity market equilibrium, savings and investment balance (capital market), and international revenue and expenditure balance (foreign exchange market). It is worth mentioning that there are differences in the ownership of capital factors in the factor market, the supply of which is mainly derived from the capital income of domestic residents and enterprises, as well as from the investment income of foreign capital, which in turn is used in the production activities of goods.

In addition, the income generated from the paid allocation of carbon emission allowances will be incorporated into the national carbon emission trading fund to support the construction of a national carbon emission trading market and key projects of greenhouse gas reduction (Ministry of Ecology and Environment, 2021). However, there is no clear policy regulation on the specific distribution form of carbon trading income, and the customized subsidy object and subsidy ratio have different policy effects (Cai , 2017; Lin and Jia, 2020; and Zhao , 2022), affecting the research conclusion of this paper. Therefore, the display of subsidy in Fig. 1 only illustrates its possible flow in the ETS module, and the value is set to 0.

The scenario design of this paper includes two components: historical scenario simulation and policy scenario simulation. For the former, this paper ensures the reliability of its prediction when calibrating the model by simulating the historical scenario between 2018 and 2022, which is referred to in the research of Cui (2019) and Guo (2021). The SAM table serves as the initial database, and key parameters are obtained by setting macroeconomic variables exogenously, such as GDP, the GDP deflation index, import and export trade, and carbon emissions. While shift variables are assumed to be endogenous in the historical calibration, they are set to be exogenous in policy simulation. Specifically, GDP, the GDP deflation index, and import and export trade data are obtained from China Statistical Yearbook (National Bureau of Statistics of China, 2019), and carbon emissions data are obtained from Statistical Review of World Energy (The Energy Institute, 2023). In addition, this paper intends to set up varied policy implementation scenarios for the forecast period of 2023–2030 using the control variable method in order to investigate the effect of policy implementation.

The specific scenarios are shown in Table I. The BAU scenario assumes the absence of any implementation of electricity cross-subsidy reform or carbon trading policies, with other macroeconomic variables extrapolated based on development trends, such as GDP and energy efficiency. To account for China's process of national economic and social development, the paper relied on “The Economist's Interpretation of the Outline of the 14th Five-Year Plan and Vision 2035” (the Central People's Government, 2021) to establish GDP development trends between 2023 and 2030, with the real GDP growth rate set at 4.7%. To reflect the reduction of energy consumption intensity over time, the paper sets an average annual increase in energy efficiency of 1.5% for 2023–2030, consulting the research of Qi (2023). Scen1–5 introduce the implementation of two specific policies to the BAU scenario, with various carbon trading policy settings. Scen1 includes the implementation of electricity cross-subsidy reform to the BAU scenario. Scen2–5 differ in the key parameters of the quota decline scheme, quota allocation method, and penalty mechanism, which constitute the main components of the carbon trading mechanism, based on Scen1. Since the national carbon market is still under development, the parameter settings of Scen2 refer to the carbon trading mechanism in Guangdong Province, given its similarity to the National Carbon Trading Plan of the National Development and Reform Commission of China (The National Development and Reform Commission, 2018), and its relatively advanced stage of development. Then, Scen3–5 are established by modifying some parameters of Scen2.

A recursive dynamic CGE model is constructed based on the Social Accounting Matrix (SAM) compiled from input–output table (National Bureau of Statistics, 2019b). Among them, 42 production departments are divided into 21 sectors according to their product characteristics through merger and split. See Table II for the detailed division of departments and their abbreviations. Furthermore, the rest of the relevant data in the SAM comes from the China Electricity Yearbook (National Bureau of Statistics, 2019a), the China Financial Yearbook (Ministry of Finance of the People’s Republic of China, 2019), and the China Statistical Yearbook (National Bureau of Statistics, 2019c). Moreover, the parameters within the CGE model chiefly pertain to share and substitution elasticity parameters. The production share, consumption share, average savings rate, and average tax rate of consumers and government are derived from data within the SAM and are, therefore, considered to be share parameters. However, the substitution elasticity parameters in the production block and trading block require external guidance from previous research conducted by other scholars (Liang, 2013; Li , 2019).

In the process of electricity cross-subsidy reform, the determination of the price elasticity of electricity demand and marginal electricity cost of various users are the two critical factors to realize Ramsey pricing. Most literature has explored the price elasticity of electricity demand in various groups of users and reached similar conclusions (Chen and Zuo, 2018; Yu and Xin, 2020; and Jia , 2021). Consequently, this paper applies the research results of Chen and Zuo (2018) for reference, that the price elasticity of residential electricity demand is determined as −0.556, and that of the industrial sector is determined as −0.431. Simultaneously, this paper defines the marginal cost of electricity price as the end-user price excluding government funds and surcharges and fuel costs (Erdogdu, 2011), which is calculated as the ratio of the sum of market transaction electricity prices and transmission and distribution prices to the fuel commodity price index drawing on the study of Hou (2021).

On the basis of relevant data, the corresponding constant terms $k i$ for residential and industrial users are calculated to be 13 740.08 and 71 493.52, respectively, and the Ramsey index R is 0.0928. Then, the Ramsey electricity prices for residential and industrial users in China are measured separately, as shown in Table III. It can be seen that the Ramsey industrial prices are lower than that of residential prices, which is consistent with the research result of Qi (2010) and Yu and Xin (2020). The reason is that the price not only depends on the setting of the objective function but also is related to the marginal cost and the price elasticity of electricity demand of each user. Due to the high marginal cost of electricity for residential customers, its Ramsey price is still higher than that of the industrial and commercial users even under the higher elasticity of electricity consumption demand and the lower markup. Furthermore, according to the National Electricity Prices Regulation Notice (National Energy Administration, 2019), we can also obtain the actual electricity prices for different users. Accordingly, we can get the price change rates to simulate the electricity prices' level after the cross-subsidy reform.

GDP is a crucial economic indicator for measuring national economic and social development and is significantly impacted by inflation. Therefore, to account for this factor, the annual data are processed at constant prices using the GDP deflator based on 2018. Figure 2 displays the changes in real GDP under the BAU scenario from 2018 to 2030. Specifically, this paper treats 2018–2022 as a historical simulation period, during which real GDP is determined exogenously for rigorous model calibration. Meanwhile, 2023–2030 is the policy simulation period in which the growth rate of real GDP is fixed at 4.7%, considering national economic development goals and trends derived from the Outline of the 14th Five-Year Plan and Vision 2035. Based on this, it is estimated that the real GDP will increase to 116.09 × 10¹² and 145.32 × 10¹² yuan in 2025 and 2030, respectively.

Figure 3 presents the real GDP under all scenarios in 2030. In comparison to the BAU scenario, Scen1 demonstrates that the implementation of cross-subsidy reform leads to a 0.17% increase in GDP. This is due to the fact that the reform facilitates the accurate reflection of electricity costs, allowing companies to benefit from lower electricity prices and obtain cost advantages for their production and operational activities. Thus, it can be inferred that electricity cross-subsidy reform is a significant contributor to enhancing resource allocation efficiency and enabling the national economy to develop rapidly. Scen2–5, on the other hand, explore the impact of ETS in comparison to Scen1. Notably, GDP exhibits a noticeable downward trend under ETS implementation. Given that coal is China's primary source of carbon emissions, the implementation of carbon emission trading policy results in passing high carbon costs to the power sector, leading to an increase in electricity prices. As Laing (2014) stated, the pass-through rate of carbon cost to the electricity sector in the EU carbon market varies between 5% and 100% depending on the country. Li (2022) found that the pass-through rate for industrial electricity price in China is 6.27%. Consequently, the rise in electricity costs will partially attenuate the cost advantage that industrial users gain from cross-subsidies. Nevertheless, the GDP in Scen2–5 remains higher than that of BAU, ranging from 0.43% to 1.12%.

In addition, This paper also explores the sensitivity of GDP to key parameters of the carbon trading mechanism design by comparing Scen3–5 with Scen2. The results show that GDP grows by 1.1%, 0.4%, 1.1%, and 0.8% under the four scenarios relative to the BAU scenario. The paper draws roughly the same conclusions as Tang (2016) and Wei (2022). It is the most sensitive to the change of annual decline factor, and it is less affected by the free quota rate and the penalty coefficient. As electricity is a critical element affecting the development of the national economy and society, the carbon market mainly plays a role through the transmission of carbon costs to the power sector. This paper assumes that the electricity market is fully competitive, so the effectiveness of ETS policy only needs to consider carbon costs.

For the annual decline factor, it elevates carbon price though cutting supply of carbon emission allowances, and the marginal cost of carbon emission reduction technology expands accordingly, while in a perfectly competitive market, the emission reduction costs will be the same as ETS cost. Additionally, it has been demonstrated by Lin and Jia (2019) that the ETS price exhibits an elasticity of less than 1 in response to the annual decline factor. This suggests that an increase in carbon price will result in a corresponding increase in carbon cost. For the free quota ratio, it cannot affect the total emission reductions under the same annual decline factor in the Scen2 and Scen4, which means that the total amount of carbon emissions will remain unchanged. In the same technical level, the same emission reductions have the same emission reduction costs. So the ETS cost will not be affected by the free quota ratio. Moreover, Lin and Jia (2019) calculated that the elasticity of the ETS price with respect to free quota rate is always 1, further supporting this conclusion. In addition, the penalty coefficient increases the cost of the carbon market players who fail to reach the emission reduction target through the payment of fines, which prompts the cost of electricity consumption and hinders the production and operating activities.

Figure 4 displays the change rate of the industry output in all scenarios in 2030 compared to the BAU scenario. It can be seen that the output of the electricity sector exhibits a significant upward trend of nearly 2.5% under the implementation of cross-subsidy reform. The main reason is that the decrease in industrial electricity price during the post-reform period has greatly promoted the growth of industrial electricity demand, which accounts for a relatively high share of the total electricity consumption. Although residential users with greater elasticity of electricity demand have reduced their demand under non-subsidized electricity prices, there is still a significant increase in electricity demand under the synergistic effect. In addition, the reasonable reflection of electricity prices on the cost of electricity consumption promotes the optimal allocation of resources in the power production process, further improving industry output. Based on this, the structure of China's electricity production, which is dominated by thermal power, has also led to a significant increase in the output of fossil energy industry, such as coal, oil, and natural gas. As an important input factor in production activities, the lower electricity price also leads to varying degrees of growth in the output of energy-intensive industries. Among them, the output growth of metal mining and smelting products industry, nonmetallic mineral products industry, equipment manufacturing industry, and construction industry is particularly significant.

On this basis, Scen2–5 simulate the scenarios where the carbon trading and cross-subsidy reforms are implemented together. It can be seen that outputs in all sectors are limited, especially for the energy industries and energy-intensive industries. In addition, for the same reasons, the sensitivity of the output to the quota decline scheme, quota allocation mode, and penalty mechanism of ETS is similar to GDP.

Figure 5 displays the change rate of residential consumption in all scenarios in 2030 compared to the BAU scenario. With the significant rise in residential electricity cost under the electricity cross-subsidy reform, the consumption of electricity is significantly reduced by 0.88%, revealing a greater demand rigidity. Meanwhile, the consumption demand for other energy and energy-intensive products rises due to the substitution effects and lower production costs. Among them, the energy product consumption, such as coal, coke, and refined oil, grows 0.067%–0.166%, and consumption demand for energy-intensive products, such as chemicals, metal products, and nonmetal products reaches 0.272%–0.544%. In addition, the expenditure on services that often accompany electricity consumption is also diminished to a certain extent, which is affected by a decline of 0.49%. Moreover, the comparison between Scen1 and Scen2 shows that the implementation of the carbon trading leads to a downward trend in the consumption demand of all products after the cross-subsidy reform by increasing the cost of electricity.

Figure 6 exhibits the CO₂ emissions in the BAU scenario during 2018–2030. The total carbon emissions from fossil fuel increase from 9.61 × 10⁹ tons in 2018 to 12.64 × 10⁹ tons in 2030, which is consistent with the results of relevant research (Lin and Jia, 2019; Cui , 2019; Zhang , 2022). And in terms of carbon emission contribution, coal accounts for the highest share. In 2018, the CO₂ emissions caused by coal consumption reached 7.47 × 10⁹tons, accounting for 77.9%, while oil and natural gas accounted for only 15.98% and 6.13%, respectively. With the continuous improvement of energy utilization efficiency and adjustment of energy structure, the contribution of coal consumption to carbon emissions will decrease to 71.4% by 2030, and the consumption of oil and natural gas will significantly increase.

Figure 7 displays the national CO₂ emission level and variation in all scenarios in 2030. From the results of the comparison, the cross-subsidy reform promotes national economic and social development while facilitating the national CO₂ emission in 2030 from 12.64  × 10⁹ to 12.78 × 10⁹ tons, with an increase of 1.14%. The main reason is that, for one thing, the low electricity prices of industry greatly pull the industrial electricity demand, which accounts for 49.8% of the total social electricity consumption; for another, the decline in the price of electricity, a factor of production, can favorably boost the industrial production activities, which leads to an uplift in total energy consumption demand and CO₂ emissions. On this basis, the carbon emission trading can effectively mitigate the adverse impact of the cross-subsidy reform on CO₂ emissions at the cost of limited economic losses. The evidence is that the CO₂ emission in Scen2–5 drops by 0.50%, 0.90%, 0.50%, and 0.72%, respectively, compared to Scen1, which is significantly higher than the variation range of GDP in Sec. IV B 1. In addition, the setting of ETS also has a remarkable impact on CO₂ emission, and it can be seen that the emission reduction effect of the carbon trading is mainly owing to the reduction of carbon allowance year by year. This is consistent with the analysis of GDP in Sec. IV A.

Figure 8 displays the CO₂ emission in the electricity sector in 2030, and the fluctuating trend under various scenarios is the same as the national total carbon emission. However, since the carbon emission trading is only implemented in electricity industry in this paper, the ratio of carbon emission reduction is larger, and the impact effect is more significant. For example, the comparison of Scen1 and Scen3 shows that the CO₂ emission of the power sector drops by 1.39%, which is higher than the national carbon reduction rate of 0.90%.

In order to ensure the stability of the simulation, a sensitivity analysis of the BAU scenario is conducted in this paper. In the process of model construction, the production module portrays the production activities of each industry sector, which affects the final outcome of general equilibrium. The dynamic mechanism determines the development and change of general equilibrium in multiple periods through technological progress, capital, and labor growth. Therefore, the paper selects the elasticity coefficient of the production function in the second-level nested format of the production module and the depreciation rate, population growth rate, and AEEI in the dynamic mechanism as the key parameters for sensitivity analysis.

Table IV displays the changes in variables caused by the variation of the key parameters in the BAU scenario, the magnitude of which increases gradually over time. From the empirical results in 2030,the largest change in CO₂ emissions is between −2.5736% and 2.2608% when the elasticity coefficient of the production function is varied up or down by 10%, and when AEEI fluctuates by 10%, GDP faces the largest change range, which is between −1.2744% and 1.2410%. Hence, the fluctuation of the parameters makes the variables less affected, and the simulation of the recursive dynamic CGE model is stable to some extent. On the whole, the scientificity and validity of the research are verified.

In this paper, we focused on the reasonable price level of electricity users on the sales side by applying the Ramsey pricing mechanism and explored the carbon emissions and economic performance of the reform and the impact of carbon emission trading policies under different mechanism designs on the reform effect. In this process, the recursive dynamic CGE model is adopted to explore the policy effect through six policy scenarios. Among them, the introduction of ETS in CGE takes the quota decline scheme, quota allocation mode, and corresponding penalty mechanism into account and makes different settings to exclude the randomness of the results through comparative studies. The conclusions are as follows:

1. The Ramsey electricity price after the cross-subsidy reform for the industrial users is 0.541 yuan/kWh, and the residential Ramsey price is 0.792 yuan/kWh. It is more of a true value of electricity and can maximize the total social surplus while minimizing the negative impact on resource allocation efficiency.

2. The electricity cross-subsidy reform can enhance the cost-competitive advantage of the industry, effectively promote the growth of GDP, industrial output, and simultaneously elevate CO₂ emissions significantly. A simple geographic alignment of electricity prices can make it difficult to achieve concurrent carbon reduction targets. According to the data of 2030, the GDP will grow by 0.17% after the reform. And for the changes in industrial output, energy industries and energy-intensive industries showed the most pronounced rise, which can reach the highest 2.62% and 1.84%, respectively. In addition, the cross-subsidy reform contributes to a 1.14% rise in carbon emissions.

3. The carbon trading policy can effectively reduce the incremental carbon emissions of the cross-subsidy reform at a lower cost of economic losses. Furthermore, the degree of impact is most sensitive to annual decline factor than the free quota rate and penalty coefficient. According to the data in 2030, compared to the BAU scenario, the introduction of ETS reduces incremental GDP from 0.17% to 0.11%, and the change rate in CO₂ emissions is from 1.14% to 0.64%. And under the scenario that the carbon emission intensity decline coefficient rises by 1%, the changes of the change rate in GDP and CO₂ emission are 0.13% and 0.9%, respectively.

First, making timely adjustments to residential electricity prices based on the Ramsey pricing rule is crucial in effectively addressing policy cross-subsidies. During the implementation process, it is essential to consider the affordability of users and power grid enterprises to ensure a smooth transition. The tiered pricing system for residents should be gradually improved to adequately release the space for adjustment of residential electricity prices, and a reasonable range for escalating “non-basic” electricity prices should be formulated alongside defining “basic” electricity demand. This would allow for level setting of electricity prices for large industries and general industries and commerce according to the extent of the rising space.

Second, while the electricity cross-subsidy reform can increase the efficiency of the electricity market, it could potentially limit carbon emission reduction capabilities, creating a trade-off between electricity price reduction and carbon reduction targets. Thus, the government needs to integrate carbon emission targets into the cross-subsidy reform through a unified framework. To achieve this, it is crucial to establish a coupling mechanism between the carbon market and the electricity market and clearly define the boundary between government regulation and market mechanisms for both markets. Such efforts can effectively enhance the intervention and promotion of both markets, including deeper market-oriented reforms in electricity, leading to more significant direct effects of carbon prices on the electricity price for residential users.

Third, to expedite the national carbon market development in line with the “dual carbon” target requirements, a mechanism design for the national carbon market should be implemented to meet both the goals of electricity market reform and carbon emission reduction. Specifically, drawing on the valuable experience of established carbon trading pilots in provincial and municipal areas, it is crucial to establish a justifiable path for gradually raising carbon constraints, while keeping carbon prices stable and maintaining the dynamism of the carbon market. In addition, the proportion of free carbon emission allowances should decrease gradually with the maturity of the carbon trading market. Furthermore, in order to prevent carbon leakage stemming from a low penalty coefficient and reduced market compliance rate resulting from a stringent penalty coefficient, it is imperative to enhance the efficiency of the penalty mechanism.

There exist certain limitations within the scope of this research. One crucial point to note is that the electricity cross-subsidy reform is solely based on marketization and has not taken into account the potential impact of the reform under stricter government regulation. Additionally, due to unavailability of pertinent data, the cross-subsidies of general industry and commerce as well as agriculture and the means to refund government revenue from the carbon market have not been incorporated within the model. Moving forward, our future work will consider the effects of electricity cross-subsidy reform under government regulation and also aim to design an improved ETS while analyzing the repercussions of various methods through which carbon emission reduction revenue of the government can be refunded on the impact of cross-subsidy reform.

This paper is supported by the Beijing Municipal Social Science Foundation (No. 16JDYJB031), the Fundamental Research Funds for the Central Universities (No. 2020YJ008), and the Fundamental Research Funds for the Central Universities (No. 2018ZD14).

The authors have no conflicts to disclose.

Zhao Xin-gang: Conceptualization (equal); Funding acquisition (equal); Resources (equal); Supervision (equal); Writing – review & editing (equal). Hu Shuran: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Writing – original draft (equal). Zhang Wenbin: Data curation (equal); Funding acquisition (equal); Investigation (equal); Software (equal); Validation (equal). Wang Wei: Data curation (equal); Project administration (equal); Resources (equal). Lu Wenjie: Resources (equal); Supervision (equal); Validation (equal); Visualization (equal).

The data that support the findings of this study are available within the article.