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Article

Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market

School of Economics and Management, China University of Petroleum (Beijing), Beijing 102249, China
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Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6955; https://doi.org/10.3390/su17156955
Submission received: 19 June 2025 / Revised: 25 July 2025 / Accepted: 26 July 2025 / Published: 31 July 2025
(This article belongs to the Section Air, Climate Change and Sustainability)

Abstract

With the proposal of China’s “dual carbon” goal, the carbon market has become a vital tool for controlling carbon emissions. This study constructs a system dynamics model encompassing carbon trading, the economy, energy, population, and the environment, and conducts simulation analysis against the backdrop of China’s national carbon market’s implementation. The results indicate that the implementation of China’s national carbon market significantly promotes carbon emissions reduction, albeit at the cost of some economic development in the short term. However, the suppressive effect of the carbon market on carbon emissions is stronger than its negative impact on economic growth. The effects of carbon reduction strengthen with increases in carbon price, quota auction, CCER price, penalty severity, and the quota reduction rate and weaken with a higher CCER offset ratio. A moderate reduction in the tightening quota reduction rate is more conducive to achieving coordinated development across the multiple objectives of carbon reduction, economic development, and energy structure. Under the constraints of multiple objectives involving carbon reduction, economic development, and energy structure, the reasonable range for carbon prices is between CNY 77.9 and CNY 118.9 per ton, with the maximum quota auction of 23.4%. Additionally, the reasonable range for the quota reduction rates is between 0.84% and 2.18%, with the penalty severity set at 7.

1. Introduction

Starting from the Industrial Revolution, the use of fossil fuels has provided significant benefits to people while also triggering a series of environmental and climate issues. In response to climate change challenges, notable international agreements, including the Kyoto Protocol, have facilitated the establishment of the carbon market [1]. Carbon emissions trading commodifies carbon dioxide emissions and achieves greenhouse gas reductions through market mechanisms [2], contributing significantly to the advancement of the global carbon neutrality goal [3]. Nowadays, an increasing number of countries and regions are choosing to link their macro reduction targets with those of the carbon market, and the influence of the carbon market is gradually growing [4].
The Chinese government also places great emphasis on the construction and development of carbon markets, having launched local pilot carbon markets successively in 2013 and having initiated a national carbon market in 2021. A large number of important government documents clearly outline the strategic direction of achieving the dual carbon goals through market mechanisms and highlight the significance of a carbon market in coordinating economic development and environmental protection [5,6]. The carbon market is significant in promoting China’s fulfillment of international emissions reduction commitments and achieving an ecological civilization [7,8]. China’s national carbon market represents not only an institutional innovation but also a key policy tool for realizing the vision of carbon neutrality [9].
The implementation of various carbon market policies will significantly impact the economy, carbon emissions reduction, and the energy structure. Therefore, comprehensively assessing the emissions reduction effects of China’s carbon market and optimizing regulatory strategies for its key elements are essential for improving the carbon emissions trading system in China. Although progress has been made in the construction of China’s carbon market, it still faces a series of practical challenges, such as weak market mechanisms. Questions regarding the operational effectiveness of the carbon market in China and how to achieve multi-objective coordination among carbon reduction, economic development, and energy structure, as well as defining the reasonable scope of carbon market elements, require in-depth exploration. To this end, this study conducts a simulation analysis based on the implementation of China’s national carbon market. By setting different carbon trading scenarios, it examines the impacts of factors such as carbon prices, allocation methods, and voluntary emissions reduction credit trading (CCER) on carbon emissions and economic development. Based on multi-objective constraints related to economic effects, carbon reduction effects, and energy structure effects, the study further optimizes the key elements of the carbon market, providing a reference basis for regulating these elements. This research will provide important reference for the Chinese government in achieving macro-control of the carbon market while pursuing diverse development objectives.
Following the rollout of pilot carbon markets in China and the subsequent launch of China’s national carbon market, public attention has been steadily increasing regarding the carbon trading policy implemented by China. Regarding the study on factor regulation and multi-objective coordinated development of China’s carbon market, the existing literature mainly includes qualitative analyses and policy simulations. Qualitative analyses emphasize aspects such as legal frameworks, quota allocation, regulatory mechanisms, coverage, and intertemporal trading. For example, Munnings et al. [10] assessed the adaptability of carbon markets in Shanghai, Guangdong, and Shenzhen through interviews and data analysis; Chen [11] emphasized the challenges faced by the China’s carbon market, analyzing them from the perspectives of industry coverage and quota allocation. In addition, a few scholars have combined quantitative analysis results to propose pathways for the multi-objective coordinated development of the carbon market. For instance, Feng et al. [12] suggested improvements based on efficiency comparisons, focusing on participation, security, constraints, and incentives; Ye et al. [13], in the context of big data, explored a blockchain-based carbon market system in China, integrated blockchain technology with carbon market development, proposed a construction plan for the carbon market system, and validated its social benefits through modeling analysis. Policy simulations primarily utilize CGE and multi-agent models. However, due to challenges in depicting feedback relationships between systems, some scholars have shifted to system dynamics (SD) simulations, mainly focusing on provincial and regional levels. For instance, Ye et al. [14] simulated the economic and carbon emissions impact of the carbon market, using Guangdong Province as a representative case study; Zhang et al. [15] examined how the carbon market impacted the Beijing–Tianjin–Hebei region. SD has been widely applied in environmental and energy policy research due to its strength in capturing complex feedback loops, time delays, and non-linear behaviors within socio-technical systems [16]. In the environmental domain, SD has been used to assess the effectiveness of pollution control policies, model emissions trajectories, and analyze sustainability trade-offs under dynamic conditions [17]. In the energy sector, researchers have applied SD to simulate energy transitions [18], evaluate the impacts of renewable energy policies, and explore interactions between technological innovation, market behavior, and policy regulation [19,20]. These studies demonstrate the method’s capacity to provide long-term insights into policy outcomes under uncertainty and interdependence.
In recent years, a growing number of studies have focused on simulating the operation and reform of carbon markets in the EU ETS and OECD countries. These studies typically employ system dynamics models, computable general equilibrium models, or agent-based approaches to analyze the interaction between carbon pricing mechanisms and economic or energy system performance. For example, several works have examined the role of carbon price floors and ceilings in stabilizing allowance prices and guiding long-term decarbonization investments under the EU ETS [21]. Others have focused on firm competitiveness, emissions outcomes, or on the effects of different quota allocation schemes, such as benchmarking versus auctioning [22]. Moreover, recent models have simulated the market impacts of integrating carbon border adjustment mechanisms (CBAM), revealing their potential to reduce carbon leakage and redistribute emissions burdens [23]. In OECD countries, simulation research also emphasizes the behavioral response of firms to policy uncertainty and the implications of linking multiple regional carbon markets [24]. These international experiences provide useful insights into the policy design and dynamic response of carbon trading systems and offer valuable references for China’s carbon market development.
Currently, China’s carbon market is in its developmental infancy, and its effects on reducing carbon emissions, economic growth, and energy structure remain controversial [25]. Regarding economic impacts, research mainly covers macroeconomic, regional economic, industrial, and corporate levels. At the macro level, most scholars support the “Porter Hypothesis,” arguing that the carbon market facilitates economic development [26]. However, certain studies indicate that the carbon market might suppress economic growth, with negative effects depending on the setting of emissions reduction targets [27]. At the regional level, studies often concentrate on the heterogeneous effects of the carbon market across different areas and the associated spillover effects [28]. At the industrial and corporate levels, scholars have separately examined the role played by the carbon market in impacting industrial structure and corporate decision-making processes [29]. In terms of carbon emissions reduction, previous research has primarily centered on the reduction effects in pilot areas and energy-intensive industries. Existing research has indicated that carbon trading plays a significant role in cutting carbon emissions in pilot and adjacent areas while also achieving co-reductions of other pollutants [30]. In energy-intensive industries, the carbon trading mechanism has improved reduction efficiency [31]. At the corporate level, Liu et al. [32] conducted system simulation analyses and observed that the carbon trading system enhances corporate emissions reduction performance in most scenarios; however, overly stringent policies may have counterproductive effects. In contrast, existing research regarding the carbon market’s effect on energy structure remains relatively limited [33].
The design of the carbon market is based on the Coase Theorem, aiming to achieve the internalization of externalities through market mechanisms under well-defined property rights. Theoretically, the carbon market imposes constraints on regulated enterprises by setting a total emissions cap, thereby establishing the scarcity of carbon emissions permits. As the total quota is gradually reduced, carbon reduction targets are incrementally met. Since carbon emissions primarily stem from fossil energy consumption, an escalation in the price of carbon raises the cost of fossil fuels, compelling enterprises to adjust their energy structures in response to market incentives and cost pressures in order to reduce carbon emissions. However, this process is simultaneously constrained by both carbon emissions and carbon prices, which subsequently impact economic output.
To fully leverage the benefits of the carbon trading mechanism, it is essential to comprehensively understand the implementation effects of China’s carbon market and optimize its design according to various objectives. However, the existing literature has several shortcomings: (1) Research primarily based on qualitative analysis lacks empirical support, which fails to adequately validate the proposed improvement recommendations for the carbon market. (2) There is insufficient multi-dimensional analysis regarding the effects of carbon market implementation; existing studies often emphasize a single impact—either economic or environmental—within the same analytical framework. (3) There is a lack of exploration regarding improvement proposals and regulatory measures under multi-objective constraints involving carbon reduction effects, economic development, and energy structure effects.
This study makes the following theoretical, methodological, and empirical contributions to the existing literature on carbon market simulations and climate policy evaluation: (1) Theoretically, this study extends the understanding of carbon market implementation by systematically examining its multi-dimensional impacts on carbon emissions, economic growth, and energy structure under the context of China’s national ETS. (2) Methodologically, it applies a system dynamics framework to simulate the feedback mechanisms and dynamic interactions between the carbon market and other subsystems. The model captures causal loops involving carbon prices, CCER offset ratios, and penalty structures, offering a flexible tool for policy experimentation under uncertainty. (3) Empirically, this study constructs and evaluates a series of carbon market scenarios and identifies preferable policy schemes through a multi-objective scheme comparison, considering trade-offs among emissions reduction, economic performance, and energy structure transition. This approach avoids reliance on formal optimization algorithms but still provides decision-relevant insights by screening for robust policy packages under multiple constraints.

2. Methodology

The system, as an organic whole, achieves specific functions through the interactions among its components [34]. Since its introduction, systems theory has been widely applied across various fields. This study devotes attention to the operation of China’s national carbon market and explores the interrelationships between the economy, society, energy, the environment, and the carbon market through system dynamics approaches grounded in the principles of general systems theory.

2.1. Causality

The system dynamics method is used to establish causality through interconnected feedback loops to explain complex problems in the real world. The carbon market, as a policy-oriented tool for reducing greenhouse gas emissions through market mechanisms, involves multiple factors such as the economy, energy, and the environment. The causality diagram shown in Figure 1, based on the feedback relationships among the main variables of each system, is presented in this paper.
The main cause–effect feedback loops in Figure 1 are as follows:
(1) GDP → (+) Value of the three major industries → (+) Total energy consumption → (+) Carbon emissions → (+) Excess carbon emissions → (+) The cost of carbon emissions → (−) GDP
Economic growth contributes to an increase in the value of the three major industries, which contributes to an increase in energy consumption and, consequently, greater carbon emissions. When carbon emissions exceed set limits, the resulting increase in the cost of carbon emissions places constraints on economic growth.
(2) GDP → (+) Value of the three major industries → (+) Total energy consumption → (+) Carbon emissions → (+) The cost of carbon emissions → (+) Technological-driven coefficient → (+) GDP
Economic growth promotes an increase in the value of the three major industries and in energy consumption, which in turn raises carbon emissions and the cost of carbon emissions. However, according to the “Porter Hypothesis,” the increase in the cost of carbon emissions incentivizes technological innovation, further driving economic growth.
(3) Carbon emissions → (+) Excess carbon emissions → (+) Fines → (+) The cost of carbon emissions → (+) Emissions reduction driving factor → (−) Comprehensive energy emissions factor → (+) Carbon emissions
Under the constraint of carbon quotas, enterprises that emit more than their allocated allowances are subject to penalties, resulting in increased costs of carbon emissions. This mechanism effectively raises the marginal cost of emitting CO2, thereby acting as a price signal. Higher emissions costs incentivize firms to adjust their behavior through two primary channels: short-term abatement measures, such as optimizing production processes, improving energy efficiency, and reducing energy intensity; and long-term investment in low-carbon technologies, such as upgrading equipment or shifting toward cleaner energy sources. These actions collectively reduce the comprehensive energy emissions factor, ultimately leading to a net reduction in total emissions.
A stock and flow diagram, based on the causality diagram, is shown in Figure 2 to quantify the relationships among the various variables.

2.2. Main Equations and Parameter Settings

The data used in this study are primarily derived from authoritative national sources, including the Wind Database, China Statistical Yearbook, China Energy Statistical Yearbook, and China Environmental Statistical Yearbook, as well as the relevant existing literature. With respect to the system dynamics model constructed in this paper, the model variables include both endogenous and exogenous components. Endogenous variables are determined by the internal structure of the model and mainly include GDP, carbon dioxide emissions, total population, carbon emissions costs, total energy consumption, and non-fossil energy consumption. Exogenous variables, such as the quota reduction rate, carbon price, offset ratio, and CCER price, are set based on existing policy frameworks and the relevant literature. Several variable equations are fitted using OLS regression in Stata 16 based on real data. The key equations and the sources of the main parameters used in the model are summarized in Table 1.

3. Empirical Simulation Analysis

Vensim PLE version 9.3.5 is utilized in this study for simulation analysis, with the simulation period set from 2008 to 2030 and a simulation step of one year. The actual operational data cover the years 2008 to 2022, and simulations are conducted to estimate relevant indicators after China’s national carbon market was implemented in 2021.

3.1. Model Validation

To validate the accuracy of the model, historical data for GDP, carbon emissions, and total energy consumption are compared with the model’s initial output results. Generally, a relative error of less than 15% is considered acceptable for validating the model. The fitted values and relative errors for GDP, carbon emissions, and total energy consumption are presented in Table 2. The results indicate that the errors for all years are within ±10%, with an average error not exceeding 5%, which confirms the model’s high fitting degree and strong accuracy.

3.2. Scenario Setting

The impact of carbon prices, CCER prices, CCER offset ratios, quota auctions, quota reduction rates, and penalty severities on the economy, carbon emissions, and energy structure is analyzed in this study based on the carbon trading mechanism represented by carbon prices and the offset mechanism represented by CCER prices. To achieve this, the baseline scenario (BAU), the classic scenario (S0), and the simulation scenarios A1–F4 are established to examine the effects of variations in different carbon market factors on China’s economy, carbon emissions, and energy consumption. The specific scenario settings are detailed in Table 3. The BAU represents a scenario without a carbon market, while the S0 reflects the current carbon market operating policies. A1–F4 scenarios simulate the carbon trading mechanisms under different carbon prices, quota auctions, CCER prices, CCER offset ratios, quota reduction rates, and penalty severities.
This scenario design enables a systematic exploration of how variations in individual parameters influence model outcomes. By holding other variables constant while adjusting one key factor within a plausible policy range, the scenarios effectively capture the dynamic responses of the system to policy changes.

3.2.1. Impacts on the Economy, Carbon Emissions, and Energy Consumption

The simulated GDP results under the BAU and S0 scenarios are illustrated in Figure 3a. The GDP under the S0 scenario is lower compared with the BAU scenario, indicating that the implementation of China’s national carbon market has had a certain impact on the economy. By 2030, the GDP in the BAU scenario is projected to be CNY 182.95 trillion, while in the S0 scenario, it is projected to be CNY 164.17 trillion. From 2021 to 2030, following the implementation of the carbon market, the total GDP for the S0 scenario shows a 10.26% decrease compared with the BAU scenario, with a total reduction of CNY 18.78 trillion. This is primarily due to the increased carbon emissions costs for enterprises, which reduce profits in the short term and consequently affect GDP.
The simulation results in Figure 3b,c indicate that the implementation of China’s national carbon market effectively curbs the rapid growth of carbon emissions and total energy consumption. In the S0 scenario, although energy consumption and carbon emissions continue to rise, their growth rates have significantly slowed. By 2030, the carbon emissions in the BAU and S0 scenarios are projected to be 13.362 billion tons and 11.778 billion tons, respectively, while the total energy consumption is expected to be 7.101 billion tons and 6.434 billion tons of standard coal. From the initiation of the national carbon market in 2021 through to 2030, the carbon emissions in the S0 scenario are reduced by 1.584 billion tons, an 11.86% decrease, while the total energy consumption decreases by 0.666 billion tons of standard coal—a reduction of 9.39%.
The implementation of China’s national carbon market has varying impacts on the economy, energy consumption, and carbon emissions, with the most significant effect being on carbon reduction, followed by GDP, while the impact on energy consumption is relatively minor. This result aligns with the studies of Liu and Zhou [35] and Zhang et al. [15]. The simulation results indicate that from 2021 to 2030, China’s national carbon market leads to a decrease of 10.26% in China’s GDP and a 9.39% reduction in total energy consumption, while carbon emissions are reduced by 11.86%. This illustrates the notable effectiveness of China’s national carbon market in terms of curbing energy consumption and decreasing carbon emissions, although it also negatively impacts the economy. The suppressive effect that the carbon market has on carbon emissions is demonstrated to increase progressively over time, as illustrated in Figure 4, while the shock to GDP gradually diminishes, indicating that the economic “cost” of emissions reduction is decreasing.

3.2.2. Impact of Different Scenarios

(1)
Impact of Carbon Prices
As the core element of the market mechanism, the carbon price directly affects the costs associated with carbon emissions for enterprises and, to some extent, determines the effectiveness of emissions reduction in the carbon market. The outcomes of the simulation for various carbon prices scenarios are presented in Figure 5a. By 2030, as the carbon price increases from CNY 35 per ton to CNY 135, the GDP is projected to decline by 4.93% to 14.83% relative to the BAU scenario. Over the same range, carbon emissions are reduced by 6.92% to 16.00%, and total energy consumption is reduced by 4.51% to 13.56%. Increasing the carbon price intensifies the negative impact on the economy and significantly enhances the effectiveness of emissions reduction and the magnitude of the decline in energy consumption. To ensure the effectiveness of the carbon market in reducing emissions, the carbon price should not be set too low, as this could limit the full potential of the market mechanism.
(2)
Impact of Quota Auctions
The effect that China’s national carbon market has on GDP, carbon emissions, and total energy consumption, considering various quota auction schemes, is shown in Figure 5b. By 2030, in comparison with the BAU scenario, increasing the quota auction ratio from 1% to 20% results in a GDP decline that changes from 10.13% to 10.76%, a carbon emissions reduction that shifts from 11.73% to 12.31%, and a total energy consumption reduction that moves from 9.26% to 9.84%. This indicates that as the quota auction increases and the free quota decreases, the adverse effects caused by the carbon market on the economy intensifies, while the suppressive effect of the carbon market on carbon emissions and energy consumption also strengthens. This is because a higher quota auction means enterprises need to bear higher auction costs, which leads to increased carbon costs, thereby suppressing GDP growth and reducing carbon emissions and energy consumption.
(3)
Impact of CCER Prices and Offset Ratios
The influence of various CCER prices and offset ratios on GDP, carbon emissions, and energy consumption in 2030 is depicted in Figure 5c,d. The simulation results indicate that increasing in the CCER price enhances the suppression of GDP while further lowering energy consumption and cutting carbon emissions. As the CCER price increases from CNY 35 per ton to CNY 95, the GDP decline changes from 10.09% to 10.44%, the carbon emissions reduction changes from 11.69% to 12.02%, and the total energy consumption reduction moves from 9.22% to 9.55%. A significant increase in the CCER price does not notably affect GDP, carbon emissions, or total energy consumption. Therefore, the CCER price should not be set too high, as the CCER policy aims to encourage enterprises to actively engage in emissions reduction efforts and mitigate the economic impact caused by the carbon trading market mechanism. When the CCER offset ratio increases from 5% to 25%, the negative impact on GDP decreases from 11.82% to 5.48%, but the suppression effect on carbon emissions and energy consumption also weakens, with the suppression of carbon emissions falling from 13.27% to 7.38% and the suppression of energy consumption decreasing from 10.81% to 5.01%. As the CCER price is typically below the level of the carbon price, increasing the offset ratio effectively lowers carbon costs for enterprises. To ensure the effectiveness of carbon emissions reduction, the CCER offset ratio should not be too high.
(4)
Impact of Quota Reduction Rates
The impact of different quota reduction rates on GDP, carbon emissions, and energy consumption is shown in Figure 5e. A higher quota reduction rate means a faster tightening of carbon quotas in the future. By 2030, compared with the BAU scenario, increasing the quota reduction rate from 0.5% to 4.5% leads to a GDP decline ranging from 8.38% to 11.98%, a carbon emissions reduction from 10.13% to 13.42%, and an energy consumption reduction from 7.67% to 10.95%. Although accelerated tightening of quotas increases the economic impact, it also significantly enhances the decrease in carbon emissions and energy consumption.
(5)
Impact of Penalty Severities
The impact of the carbon market on GDP, carbon emissions, and energy consumption under different severities of penalties is depicted in Figure 5f. As the severity of the penalty increases, the suppressive effect of the carbon market on economic growth intensifies, while the reduction in carbon emissions and energy consumption also becomes greater. When the severity of the penalty increases from 5 to 9, compared with the BAU scenario, the GDP decline by 2030 ranges from 7.78% to 12.57%, the carbon emissions reduction ranges from 9.57% to 13.96%, and the total energy consumption reduction ranges from 7.11% to 11.49%. Although a lower penalty severity has a smaller economic impact, its effect on emissions reduction is limited. The inadequate severity of penalties in previous pilot programs made it challenging to meet carbon emissions reduction goals, which is confirmed by the simulation results of this study.

4. Discussion

4.1. Single-Objective Scheme Selection

The simulation results of the S0 and A1–F4 scenarios are comprehensively evaluated in this section, using the BAU scenario as a baseline, to identify the optimal scheme for China’s national carbon market that meets various target requirements.

4.1.1. Carbon Reduction Benefits

Results from the simulation indicate that the A4 scenario yields the lowest level of carbon emissions. As illustrated in Figure 6, the carbon emissions projected for 2030 under different scenarios reveal that the A4 scenario results in significantly lower emissions compared with the other scenarios, reaching just 11.224 billion tons in 2030, a 16.00% reduction in relation to the BAU scenario. However, GDP under the A4 scenario is only CNY 155.83 trillion, representing a 14.83% decrease compared with the BAU scenario, indicating that achieving a substantial emissions reduction comes at a considerable economic cost. Therefore, viewed in terms of emissions reduction effectiveness through the carbon market mechanism, the A4 scenario represents the optimal choice.

4.1.2. Economic Benefits

Results of the simulation indicate that, in comparison with the BAU scenario, implementing a national carbon market reduces carbon emissions across all other scenarios, but it also impacts economic development to varying degrees, with differences in economic losses across scenarios. Among these, the A1 scenario results in the least economic loss and is characterized by the lowest carbon price. As shown in Figure 7, GDP, carbon emissions, and energy consumption all increase over time in the A1 scenario. By 2030, GDP in the A1 scenario reaches CNY 173.93 trillion, which is CNY 9.01 trillion less than the value in the BAU scenario. At the same time, carbon emissions amount to 12.438 billion tons, a reduction of 0.924 billion tons lower than in the BAU scenario. Energy consumption totals 6.781 billion tons of standard coal, 0.32 billion tons lower than in the BAU scenario. This indicates that the A1 scenario achieves low carbon emissions and exerts a minimal impact on the economy, suggesting that a low carbon price strategy is more conducive to relatively stable economic development.

4.1.3. Energy Structure Benefits

According to the simulation results, it is shown that in the A4 scenario, the share of non-fossil energy in total energy consumption is the highest. As shown in Figure 8, the trends in the shares of non-fossil and fossil energy are compared between the BAU and A4 scenarios. Over time, the share of fossil energy gradually declines, while the share of non-fossil energy continues to rise. Regardless of whether the carbon market is implemented, the proportion of fossil energy decreases and the proportion of non-fossil energy increases, driven by factors such as the decrease in carbon intensity. The carbon market accelerates this process. By 2030, the proportion of non-fossil energy under the BAU scenario reaches 21.37%, while in the A4 scenario, it reaches 25.19%. This represents an increase from 17.71% in 2021 to 25.19% in 2030, a rise of 7.47 percentage points, which is 3.81 percentage points higher than in the BAU scenario. Overall, the A4 scenario simultaneously achieves the lowest carbon emissions and the highest share of non-fossil energy. This demonstrates that as the carbon market is implemented, driven by carbon reduction targets, fossil energy consumption will gradually decrease, while the proportion of non-fossil energy is expected to increase.

4.2. Multi-Objective Scheme Selection

4.2.1. Carbon Emissions Reduction Target

Various scholars have made predictions that China’s carbon emissions are expected to reach the peak by 2030. For example, Wang and Wang [43], using scenario analysis and multi-attribute decision models, forecasted a peak of 11.618 billion tons; Su et al. [44], based on scenario analysis and input–output optimization models, predicted a peak of 12.41 billion tons; Su and Lee [45], combining scenario analysis with the STIRPAT model, estimated a peak of 11.77 billion tons; Elzen et al. [46], employing scenario analysis, the bottom-up method, and the FAIR/TIMER method, predicted peak emissions under different policies to be 11.54 billion tons, 11.9 billion tons, and 12.5 billion tons, respectively; Sun et al. [47], using BP neural networks and an improved bat algorithm, predicted a peak range of 12 to 12.2 billion tons. Given that the aforementioned literature’s projections for the carbon emissions peaks primarily cluster between 11.5 billion tons and 12.5 billion tons, this paper calculates an arithmetic mean of the reported peak values, thereby establishing a 2030 carbon emissions target of 11.968 billion tons. This value is established as a representative benchmark for the 2030 carbon emissions target in this study.

4.2.2. Economic Development Target

In 2010, China overtook Japan in terms of GDP, becoming the world’s second-largest economy. In recent years, China’s economic strength has steadily grown. The International Monetary Fund predicts that by 2030, China’s GDP is projected to exceed the GDP of the United States. Morgan Stanley forecasts that China’s per capita GDP will increase from USD 9480 in 2018 to USD 17,800 in 2030 [48]. This study uses the average annual exchange rates from 2008 to 2022 and, based on this average rate, calculates that China’s GDP in 2030 should reach CNY 164.69 trillion. When considering multiple objectives, China’s GDP in 2030 should not be less than CNY 164.69 trillion.

4.2.3. Energy Structure Target

Since the announcement of the “dual carbon” goal, the State Council released the “Carbon Peak Action Plan before 2030.” Relevant departments introduced implementation plans for 12 key sectors and industries, along with 11 supporting measures. Additionally, a total of 31 provinces, autonomous regions, and municipalities developed their own local carbon peak action plans. The “Carbon Peak Action Plan before 2030” specifies that by 2030, the share of non-fossil energy consumption should reach approximately 25% in order to achieve the carbon peak target. Based on the main objectives outlined in this plan, this study sets the non-fossil energy share for 2030 at approximately 25% when considering multiple objectives.

4.2.4. Multi-Objective Scheme Screening

Based on the analysis of the optimal scenario for a single objective, the A1 scenario of the carbon market has the least economic impact, while the A4 scenario achieves both the lowest carbon emissions and the highest share of non-fossil energy. According to the simulation results, the goals of minimizing carbon emissions and maximizing the share of non-fossil energy are consistent across all scenarios in the carbon market, but they conflict with minimizing the economic impact. For example, although the A4 scenario minimizes carbon emissions and maximizes the share of non-fossil energy, it also causes the greatest negative economic impact. The A1 scenario exhibits similar characteristics.
This study takes into account the 2030 targets for GDP, carbon emissions, and the share of non-fossil energy in search of a relatively optimal carbon market scenario. Specifically, the targets are a GDP of at least CNY 164.69 trillion, carbon emissions not exceeding 11.968 billion tons, and the share of non-fossil energy reaching approximately 25%. By synthesizing the GDP, carbon emissions, and share of non-fossil energy across various scenarios for 2030, this study finds that the GDP values in the BAU, A1, A2, D2, D3, D4, E1, E2, F1, and F2 scenarios all exceed CNY 164.69 trillion. Regarding carbon emissions, all scenarios except the BAU, A1, A2, D2, D3, D4, E1, and F1 meet the target of staying below 11.968 billion tons. Among these, the E2 and F2 scenarios meet both the 2030 GDP and carbon emissions targets, with the E2 scenario having a higher share of non-fossil energy than the F2 scenario. Therefore, the E2 scenario is an optimized scheme that balances the goals of economic growth, carbon reduction, and energy structure transformation. The E2 scenario reduces the quota reduction rate to 1.5% based on the classic scenarios. This suggests that slowing down the tightening of quotas better supports the coordinated development of economic growth, carbon reduction, and energy structure transformation.

4.3. Element Regulation of the Carbon Market Under Multi-Objective Constraints

This study identifies the E2 scenario as the optimal solution for balancing multiple goals, based on the 2030 development targets, through a process of comparison and selection. On this basis, the study further explores the reasonable regulatory range for key factors, such as carbon prices, under multiple objective constraints. The CCER offset mechanism helps reduce compliance costs for enterprises and can partially mitigate the economic impact of quota trading. Currently, the CCER offset ratio is set between 5% and 10% in domestic carbon markets, and the CCER price is generally not higher than the price of quota trading. Therefore, this section will not consider the CCER price and ratio.
As discussed earlier, although the carbon reduction and energy structure targets are aligned, they do both conflict with the economic development target. Carbon prices, quota auctions, quota reduction rates, and penalty severities all have certain negative impacts on economic development. Therefore, using the 2030 economic target, the upper limits for carbon prices, quota auctions, quota reduction rates, and penalty severities can be established, while the carbon reduction target sets the lower limit. Simulation results show that to ensure the coordinated development of the 2030 carbon reduction, economic, and energy structure targets, the reasonable range for the carbon prices is between CNY 77.9 and CNY 118.9 per ton; for the maximum quota auction is 23.4%; and for the quota reduction rates is between 0.84% and 2.18%. For the penalty severity, which takes only integer values, a penalty severity level of 7 is required to meet the target constraints.

5. Conclusions and Outlook

5.1. Conclusions

This research develops a system dynamics model that includes carbon trading, the economy, energy, population, and the environment, with China’s national carbon market as the backdrop for carbon trading implementation. A simulation analysis has been performed by setting different carbon trading scenarios, resulting in the following key conclusions:
(1)
The implementation of China’s national carbon market significantly promotes carbon reduction. However, in the short term, it comes at the cost of sacrificing part of economic development. Long-term implementation of the carbon trading mechanism proves more beneficial for carbon reduction. In the short term, China’s national carbon market negatively impacts GDP growth, but its suppression effect on carbon emissions is stronger than the negative impact on economic development.
(2)
The effects of carbon reduction strengthen with increases in carbon price, quota auction, CCER price, penalty severity, and the quota reduction rate and weaken with an increase in the CCER offset ratio. The increase in the CCER offset ratio not only alleviates the economic impact of the carbon trading market but also reduces the carbon reduction effect. Therefore, the CCER offset ratio should not be set too leniently.
(3)
Gradually slowing down the quota reduction rate is more favorable for the coordinated development of carbon reduction, economic development, and energy structure targets. The E2 scenario is an optimized carbon market scenario that comprehensively considers carbon reduction, economic development, and energy structure targets. It reduces the quota reduction rate compared with the classic scenarios. Slowing down the quota reduction rate helps reduce the economic losses caused by the implementation of the carbon market and achieves coordination with other targets.
(4)
Under the constraints of carbon reduction, economic development, and energy structure targets, the reasonable range for carbon prices is between CNY 77.9 and CNY 118.9 per ton. The quota auction ranges from 0% to 23.4%. The reasonable range for the quota reduction rates is between 0.84% and 2.18%, with a penalty severity of 7.

5.2. Limitations and Outlook

This study uses a system dynamics model for simulation analysis, which inherently involves a degree of subjectivity. Due to data limitations, carbon emissions are not disaggregated by industry, and other supporting policies are not considered. The actual system is highly abstracted and simplified, which may result in the loss of some authenticity and details of the complex system. Regarding the parameter settings of the model variables, most parameter estimates rely on historical data, inevitably introducing some errors. Moreover, the model does not capture industrial or regional heterogeneity in emissions sources and economic structures, which may influence the precision of the estimated emissions reduction values and of the economic impacts. Additionally, this study does not include a comprehensive analysis of the synergistic effects among different carbon reduction policy measures.
Future research will refine the complex relationships among carbon trading, the economy, energy, population, and the environment. It will also further consider collaborative optimization schemes between the carbon tax system and the carbon market, aiming to develop models that are closer to real-world conditions.

Author Contributions

Y.M.: writing—original draft, methodology, formal analysis, data curation, conceptualization. L.M.: writing—original draft, methodology, formal analysis, visualization. L.F.: writing—review and editing, funding acquisition, resources, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China, grant number NO. 72274212.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Causality diagram.
Figure 1. Causality diagram.
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Figure 2. Stock and flow diagram.
Figure 2. Stock and flow diagram.
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Figure 3. Trends of GDP, carbon emissions, and total energy consumption in BAU and S0 scenarios.
Figure 3. Trends of GDP, carbon emissions, and total energy consumption in BAU and S0 scenarios.
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Figure 4. Trends of the S0 scenario.
Figure 4. Trends of the S0 scenario.
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Figure 5. Impact of different carbon market implementation policies on the economy, carbon emissions, and total energy consumption in 2030.
Figure 5. Impact of different carbon market implementation policies on the economy, carbon emissions, and total energy consumption in 2030.
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Figure 6. Carbon emissions in different scenarios.
Figure 6. Carbon emissions in different scenarios.
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Figure 7. Simulation results of GDP, carbon emissions, and total energy consumption in A1 scenario.
Figure 7. Simulation results of GDP, carbon emissions, and total energy consumption in A1 scenario.
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Figure 8. Simulation results of fossil and non-fossil energy structure in BAU and A4 scenarios.
Figure 8. Simulation results of fossil and non-fossil energy structure in BAU and A4 scenarios.
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Table 1. Main variables and parameter settings.
Table 1. Main variables and parameter settings.
VariablesAbbreviationsUnitsEquation Settings or Data Sources
Change in GDPCigCNY 100 millionCig = GDP × GgrTcoce + Tdc × GDP
GDPGDPCNY 100 millionGDP = Cig
Change in quotaCiq10,000 tonsCiq = Qrr × Tq
Total quotaTq10,000 tonsTq = Ciq
Auctioned quotaAq10,000 tonsAq = Qa × Tq
Auction costAcCNY 100 million Ac = Ap × Aq/10,000
FinesFinCNY 100 million Fin = Ps × Cp × Ece/10,000
Excess carbon emissionsEce10,000 tonsEce = CeTqCtv
CCER transaction volumeCtv10,000 tonsCtv = Ce × Ceror
Cost of carbon emissionsTcoceCNY 100 millionTcoce = Ctv × Cerp/10,000 + Ac + Fin
Energy consumption of the primary industryEcotpi10,000 tons of standard coalEcotpi = Eiotpi × Votpi
Energy consumption of the secondary industryEcotsi10,000 tons of standard coalEcotsi = Eiotsi × Votsi
Energy consumption of the tertiary industryEcotti10,000 tons of standard coalEcotti = Eiotti × Votti
Total residential energy consumptionTrec10,000 tons of standard coalTrec = Pcec × Tp
Total energy consumptionTec10,000 tons of standard coalTec = Trec + Ecotpi + Ecotsi + Ecotti
Carbon EmissionCe10,000 tonsCe = Ceefr × Tec
GDP per capitaGpcCNY 100 million/10,000 peopleGpc = GDP/Tp
Total populationTp10,000 peopleTp = Cip
Total residential energy consumptionTrec10,000 tons of standard coalTrec = Pcec × Tp
Change in populationCip10,000 peopleCip = Pgr × Tp
Value of the primary industryVotpiCNY 100 millionVotpi = 0.0594 × GDP + 14,981 (R2 = 0.98)
Value of the secondary industryVotsiCNY 100 millionVotsi = 0.349 × GDP + 45,668 (R2 = 0.99)
Value of the tertiary industryVottiCNY 100 millionVotti = 0.5916 × GDP + 60,649 (R2 = 0.99)
Technology investmentTiCNY 100 millionTi = 1781.8 × (Time − 2007) + 1328.3 (R2 = 0.97)
Per capita energy consumptionPcec10,000 tons of standard coal/10,000 peoplePcec = 0.0408 × Gpc + 0.1574 (R2 = 0.99)
Energy intensity of the primary industryEiotpi10,000 tons of standard coal/CNY 100 millionIF THEN ELSE (Time < 2017, −0.037 × LN (Time−2006) + 0.222, −0.087 × LN (Time − 2006) + 0.3439) (R2 = 0.94; R2 = 0.98)
Energy intensity of the secondary industryEiotsi10,000 tons of standard coal/CNY 100 million−0.316 × LN(Time − 2006) + 1.6877 (R2 = 0.96)
Energy intensity of the tertiary industryEiotti10,000 tons of standard coal/CNY 100 million−0.071 × LN(Time − 2006) + 0.345 (R2 = 0.95)
Per capita energy consumptionPcec10,000 tons of standard coal/10,000 people0.0408 × Gpc + 0.1574 (R2 = 0.99)
Population growth ratePgrDmnlexogenous variables, sources: China Statistical Yearbook
GDP growth rateGgrDmnlexogenous variables, sources: China Statistical Yearbook
Technological-driven coefficientTdcDmnlexogenous variables, sources: reference [35]
Carbon price (S0) CpCNY/tonexogenous variables, sources: reference [36]
Quota auction (S0) QaDmulexogenous variables, sources: reference [37]
CCER price (S0)CerpCNY/tonexogenous variables, sources: reference [38]
CCER offset ratio (S0)CerorDmulexogenous variables, sources: reference [39]
Quota reduction rate (S0) QrrDmulexogenous variables, sources: reference [40,41]
Penalty severity (S0) PsDmulexogenous variables, sources: reference [42]
Table 2. Model validity test results.
Table 2. Model validity test results.
YearGDP (CNY 100 Million)Carbon Emissions (10,000 Tons)Total Energy Consumption (10,000 Tons of Standard Coal)
Fitted ValueActual ValueErrorFitted ValueActual ValueErrorFitted ValueActual ValueError
2008319,245319,244.610.00%789,379734,321.02−7.50%344,650320,611−7.50%
2009348,518348,517.740.00%762,673769,194.10.85%336,787336,126−0.20%
2010380,476412,119.267.68%760,476812,900.596.45%336,106360,6486.80%
2011449,909487,940.187.79%803,195881,567.848.89%360,406387,0436.88%
2012532,682538,579.951.10%881,958900,885.632.10%391,630402,1382.61%
2013587,966592,963.230.84%900,147926,143.052.81%406,391416,9132.52%
2014647,336643,563.1−0.59%929,262937,297.590.86%423,087428,3341.22%
2015702,575688,858.22−1.99%946,073938,369.98−0.82%437,275434,113−0.73%
2016752,024746,395.06−0.75%958,019940,585.46−1.85%448,258441,492−1.53%
2017814,836832,035.952.07%980,792959,711.84−2.20%465,614455,827−2.15%
2018908,330919,281.131.19%1,032,960979,055.61−5.51%496,213471,925−5.15%
20191,003,570986,515.2−1.73%1,079,430998,804.41−8.07%526,243487,488−7.95%
20201,076,9701,013,567−6.26%1,104,8601,011,637.22−9.22%545,399498,314−9.45%
20211,106,5101,149,236.983.72%1,093,9701,054,989.45−3.69%545,016525,896−3.64%
20221,254,6201,204,724−4.14%1,179,1901,078,169.72−9.37%594,511541,000−9.89%
Average Error2.66%4.68%4.55%
Table 3. Scenario settings.
Table 3. Scenario settings.
Scenario SettingsPolicy Settings
Carbon Prices (CNY/Ton)Quota AuctionsCCER Prices (CNY/Ton)CCER Offset RatiosQuota Reduction RatesPenalty Severities
Baseline ScenarioBAU000000
Classic ScenarioS0855%6510%2.5%7
Carbon pricesA1355%6510%2.5%7
A2605%6510%2.5%7
A31105%6510%2.5%7
A41355%6510%2.5%7
Quota auctionsB1851%6510%2.5%7
B28510%6510%2.5%7
B38515%6510%2.5%7
B48520%6510%2.5%7
CCER prices C1855%3510%2.5%7
C2855%5010%2.5%7
C3855%8010%2.5%7
C4855%9510%2.5%7
CCER offset ratiosD1855%655%2.5%7
D2855%6515%2.5%7
D3855%6520%2.5%7
D4855%6525%2.5%7
Quota reduction ratesE1855%6510%0.5%7
E2855%6510%1.5%7
E3855%6510%3.5%7
E4855%6510%4.5%7
Penalty severitiesF1855%6510%2.5%5
F2855%6510%2.5%6
F3855%6510%2.5%8
F4855%6510%2.5%9
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Ma, Y.; Miao, L.; Feng, L. Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability 2025, 17, 6955. https://doi.org/10.3390/su17156955

AMA Style

Ma Y, Miao L, Feng L. Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability. 2025; 17(15):6955. https://doi.org/10.3390/su17156955

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Ma, Yue, Ling Miao, and Lianyong Feng. 2025. "Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market" Sustainability 17, no. 15: 6955. https://doi.org/10.3390/su17156955

APA Style

Ma, Y., Miao, L., & Feng, L. (2025). Research on the Implementation Effects, Multi-Objective Scheme Selection, and Element Regulation of China’s Carbon Market. Sustainability, 17(15), 6955. https://doi.org/10.3390/su17156955

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