Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2323 2023-07-28 16:48:12 |
2 update references and layout -3 word(s) 2320 2023-07-31 04:07:17 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Hu, J.; Wang, T. Strategies of Participants in the Carbon Trading Market. Encyclopedia. Available online: (accessed on 14 June 2024).
Hu J, Wang T. Strategies of Participants in the Carbon Trading Market. Encyclopedia. Available at: Accessed June 14, 2024.
Hu, Jieli, Tieli Wang. "Strategies of Participants in the Carbon Trading Market" Encyclopedia, (accessed June 14, 2024).
Hu, J., & Wang, T. (2023, July 28). Strategies of Participants in the Carbon Trading Market. In Encyclopedia.
Hu, Jieli and Tieli Wang. "Strategies of Participants in the Carbon Trading Market." Encyclopedia. Web. 28 July, 2023.
Strategies of Participants in the Carbon Trading Market

To effectively understand the collaborative and evolutionary mechanisms of three stakeholders in carbon trading namely, government, emission reduction enterprises, and emission control enterprises, it is important to identify the factors that affect decision-making behaviors amongst game players, ultimately contributing to the goal of “double carbon”. Researchers constructed a tripartite game model, analyzing the selection mechanism for game strategies related to carbon trading participants through replicated dynamic equations. Researchers also discussed the main factors that influence the evolutionary and stable outcomes of carbon trading through scenario simulations. Additionally, researchers introduced prospect theory to examine the impact of risk sensitivity and loss avoidance levels amongst decision-makers on the optimal outcome of the system. The findings reveal that in the initial game model, the three decision-makers show a cyclical behavior pattern, but the system stabilizes in the optimal equilibrium state (1,1,1) when certain conditions are satisfied. Furthermore, the initial willingness of decision-makers impacts the ability of the game system to reach a stable point. Moreover, larger values for the risk sensitivity coefficient and loss avoidance coefficient can promote the evolution of the game system toward an optimal, stable point.

carbon trading evolutionary game prospect theory scenario simulation

1. Introduction

Climate change, caused by the emissions of carbon dioxide, presents a major challenge to humanity [1]. Nations around the world were compelled to collaborate and formulate novel strategies to mitigate the adverse effects of CO2 emissions. From the 1992 United Nations Framework Convention on Climate Change to the signing of the Kyoto Protocol in 1997, culminating in the Paris Climate Change Conference in 2015, the participation of numerous countries was apparent in the pursuit of climate governance as well as efforts to combat and mitigate climate change [2]. In recent years, China’s CO2 emissions continued to increase, especially since 2000, carbon emissions continued to increase rapidly, and China became the world’s largest country in terms of carbon emissions, and the issue of carbon emission reduction in China became the focus of the international community. In the face of increasingly serious environmental problems, China put forward the goal of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060” [3], and brought this goal into the overall layout of ecological civilization construction to achieve the goal of low-carbon transformation and contribute to global climate governance.
The carbon trading mechanism is an essential tool that can facilitate the promotion of carbon emission reduction and support sustainable development [4]. With the establishment of the national carbon market in 2017, followed by the creation and continual upgrade of an online trading system in 2021, China developed a well-functioning carbon market model. Results from the Blue Book on Low Carbon Development indicate that as of 2022, approximately 230 million tons of carbon emission allowances were traded within the national carbon market at a total value of 8.602 billion yuan [5], making it the largest carbon spot market in the world. As the main participants in the carbon trading market, the participation behavior of enterprises determines the implementation effect and emission reduction effect of carbon trading policy. Therefore, it is important to study how enterprises make behavioral decisions under the carbon trading mechanism, but their carbon trading behavior is influenced by multiple factors, such as participating parties and the external environment.

2. Carbon Trading Mechanism

The carbon trading mechanism originated from the SO2 emission control program in the United States in 1968. Later, Europe and the United States utilized this program to regulate greenhouse gas emissions, which was since implemented in numerous countries. Presently, with the implementation of the national carbon market and its promotion of achieving “double carbon” targets, carbon trading emerged as one of the primary measures for the Chinese government. This trend not only directly impacts the efficiency of emission reduction and regulation in China, but also indirectly impacts enterprises’ business risks and strategic decisions, making it a subject of interest among policymakers operating within the sphere of carbon trading.
Academic research on carbon trading mechanisms primarily focuses on two facets: the design of carbon trading mechanisms and the emission reduction effect of the carbon market. Concerning the former, for example, Qi and Han assessed non-price factors by taking environmental externalities as an entry point to clarify the driving factors and mechanism of the proportion of intertemporal transactions [6]. Zhang et al. explored the optimal product pricing and carbon emission reduction benefit distribution of covered enterprises in cooperative supply chains based on carbon allowance allocation rules in carbon trading pilot regions [7]. Xu et al. examined robust emission reduction operation strategies for enterprises under historical and baseline carbon allowance allocation methods [8]. Chen et al. investigated the impact of different rent-seeking environments on the operational efficiency of carbon markets and demonstrated that price-based sale or auction methods of allocating carbon allowances clarify price signals [9]. The second facet focuses on investigating the formation mechanism of carbon trading prices and related factors. For instance, Lin et al. employed a regression model to study the possible nonlinear relationship between the carbon price and its influencing factors [10]. Xie and Dou simulated the process of carbon quota trading in China’s immature carbon market [11]. Lv et al. conducted a sensitivity analysis of relevant parameters affecting the transaction price of carbon emission rights, using data collected from pilot areas to demonstrate that the carbon price was significantly influenced by economic development level and energy prices [12].
Carbon trading is a governing environmental policy tool that directly influences emission reduction effectiveness and indirectly affects enterprise operational decisions and costs [13]. On one hand, scholars hold positive attitudes towards carbon trading policies, believing that firms with lower abatement costs will take the lead in reducing emissions and sell excess carbon emission rights to those with higher abatement costs [13]. Shi et al. verified the significant long-term effect of implementing carbon trading market policies on reducing carbon emissions through a difference-in-difference model [14]. Cao et al. demonstrated the effectiveness of carbon trading in reducing carbon emissions in the power sector by significantly reducing the coal consumption of regulated coal-fired power plants in pilot areas [15]. Pan and Wang confirmed carbon trading’s significant CO2 reduction capabilities by employing the double difference method [16]. Hu et al. found that carbon dioxide emissions in pilot areas were reduced by 15.5% compared to non-pilot areas [17]. Their finding indicated that carbon trading achieved substantial energy savings and emission reductions. On the other hand, the current performance of the Chinese carbon market revealed low carbon prices, limited activity, and low liquidity, which raises concerns regarding the practical effectiveness of carbon trading. For instance, Wen et al. concluded that the impact of carbon trading on industrial carbon emissions remained negligible due to ineffective environmental regulations by local governments and inadequate allocation of carbon quotas [18]. Lyu et al. argued that China’s carbon market lacked activity and exhibited insufficient stability with frequent fluctuations [19]. Zhang et al. developed an evaluation index system to assess the maturity of carbon markets, which demonstrated that China’s carbon market was still far from maturity and feasibility [20]. Lin and Huang also contended that non-market mechanisms were more effective in achieving carbon emission reductions [21]. Furthermore, if the market remains immature and a suitable pricing mechanism is yet to be established, then participants lack the motivation to implement carbon emission reductions. Lower carbon prices would minimize their costs, and they might not benefit significantly from reducing emissions. Therefore, carbon trading cannot sufficiently alter their emission behavior [22].

3. Carbon Trading Behavior

Recently, many scholars conducted a series of studies on the carbon trading behavioral game. For example, Perera applied the Stackelberg game theory to investigate how the government could determine continuous incentives for power plants in a competitive electricity market [23]. Feichtinger et al. analyzed the dynamic game between the government and enterprises in the case of environmental taxes [24]. Lu and Fang compared carbon trading with two different carbon allocation methods [25]. Zhu et al. proposed a decision-driven model to analyze construction market interest players [26]. Huang and Ling constructed a game model between the government and enterprises under the carbon trading scenario [27]. Additionally, Jiao et al. considered the evolutionary game of localized governments and enterprise group behavior with carbon constraints under carbon emission reduction reward and penalty mechanisms [28].
Enterprises play a crucial role in the carbon trading market and are significant drivers of energy conservation and emission reduction. Scholars worldwide increasingly focused on the innovative implications of firms’ participation in carbon trading. For instance, Calel and Dechezlepretre revealed that regulated enterprises could witness a 10% increase in low-carbon innovation due to carbon trading [29]. Zhu believed that the government can promote carbon emission reduction by increasing the activity of enterprises participating in the carbon trading market [30]. In contrast, China’s carbon trading market emerged comparatively late as compared to its foreign counterparts, and current research on corporate conduct in the carbon trading domain remains at an incipient stage. Liu and Zhang provided empirical evidence that company participation in carbon trading could stimulate R&D innovation alongside inducing higher levels of green R&D investments [31]. Yu and Liu also corroborated these outcomes through their empirical analysis [32].
The carbon trading behavior of enterprises is a significant indicator for assessing the effectiveness of the carbon trading market, primarily concerning the supply and demand dynamics within this market. Reviewing the historical progression of leading carbon trading markets worldwide, it is observed that during the early stages of carbon trading, a limited number of industries were covered, with only a few emission-controlled enterprises participating. This resulted in a low level of enterprise participation, which worsened year by year as carbon quotas were reduced. Consequently, enterprises held on to their quotas rather than trading them, perpetuating a shortage of carbon quotas in the market. This imbalance between supply and demand caused the carbon trading market to become sluggish. Many businesses held sufficient quotas while there were few potential buyers on the market, leaving little incentive for enterprises to reduce their carbon emissions. However, a healthy and regulated carbon trading market can improve the economic performance of enterprises, encourage low-carbon investment behavior, and motivate energy-saving and emission reduction practices.

4. Evolutionary Game Theory and Its Applications

Evolutionary game theory was first introduced by Maynard Smith, which since became a well-established mathematical tool for resolving multi-agent decision problems where there is incomplete information and limited rationality [33]. The evolutionary game theory extends traditional game theory and offers compelling advantages. In the realm of real economic activities, due to internal and external environmental factors, it is challenging for both governments and enterprises to act rationally with full confidence. Thus, a process of observation, imitation, and mutual learning often takes place in decision-making processes [34]. An evolutionary game enables participating agents’ strategies to eventually converge to an evolutionarily stable strategy through a dynamic process of behavioral adjustment toward equilibrium [35].
Evolutionary game theory is increasingly applied to resolve economic management problems related to corporate behavior decisions and regulation. This area of research attracted significant attention from scholars, especially regarding the applications of corporate and government regulations [36]. Liu et al. constructed an evolutionary game model and found that manufacturers tend to adopt low-carbon technologies, and synergistic effects resulting from a combination of subsidies and taxes were better than the impact of implementing a single policy [37]. Zhao and Zhang developed an evolutionary game model between the government and power producers based on carbon trading, which indicated that both lowering unit subsidies and raising unit fines promote power producers’ participation in carbon trading [38]. Chen and Wang employed the evolutionary game theory to analyze the strategic choices made by enterprises and households concerning photovoltaic subsidy withdrawal and found that photovoltaic projects were unlikely to succeed without subsidies [39]. Similarly, Zhang et al. used evolutionary game theory to provide evidence that the carbon tax and carbon subsidy were complementary based on the government–firm–consumer synergy perspective [40]. Fan et al. considered environmental taxes and local government preferences and confirmed that penalties function effectively in enhancing corporate pollution control and emission reductions by establishing a tripartite evolutionary game [41].
In fact, carbon trading participants are confronted with numerous uncertainties, such as uncertainty in the external environment and subjective limited rationality. Though evolutionary games based on this assumption possess theoretical advantages over traditional games, they lack a construct for cognitive levels of subjectivity amongst players [42]. However, a combination of prospect theory and evolutionary game theory can compensate for this limitation. Shen et al. studied local government and polluters within a watershed area while integrating prospect theory into their evolutionary game analysis [43]. Sun et al. combined prospect theory with evolutionary game studies to investigate the innovative strategies of governments and businesses under different quality deterioration level risks [44]. Uchida et al. incorporated prospect theory into evolutionary game analysis and proposed peer punishment as a mechanism to resolve social dilemmas [45]. Yang and Chen also combined prospect theory and evolutionary game studies, delineating underlying mechanisms and behavioral patterns of firms regarding breakthrough technological innovation [46]. Liu et al. adopted prospect theory in an evolutionary game approach to explore low-carbon production behavior among companies [47]. In sum, research combining prospect theory with evolutionary game theory offers insight through the examination of uncertain situations where objective and subjective factors play sizeable roles in shaping actors’ behavior.
The previously mentioned studies were refined, but still exhibit some limitations. Firstly, the emphasis of most scholars is on the macro-level aspects, such as the construction of carbon trading markets and their efficacy, but they seldom consider the micro-level factors that influence enterprise carbon trading strategies. Secondly, domestic and international researchers chiefly combined prospect theory and evolutionary games in their investigations into collaborative behavior, regulatory behavior, and social governance, yet few studies integrated the pair specifically for carbon trading behavior. Thirdly, the current literature centers on analyzing the extent of effectiveness of China’s carbon trading policy pilots in reducing carbon emissions; however, there is a dearth of comprehensive examinations into the inner workings of the Chinese carbon trading market’s operational mechanisms, and sparse research focuses on carbon trading market supply and demand. This situation left room for debate on how to construct an efficient carbon trading market encompassing the entire sector’s supply perspective.


  1. Hu, Z.-H.; Wang, S.-W. An Evolutionary Game Model between Governments and Manufacturers Considering Carbon Taxes, Subsidies, and Consumers’ Low-Carbon Preference. Dyn. Games Appl. 2022, 12, 513–551.
  2. Tong, W.; Du, J.; Zhao, F.; Mu, D.; Sutherland, J.W. Optimal Joint Production and Emissions Reduction Strategies Considering Consumers’ Environmental Preferences: A Manufacturer’s Perspective. Sustainability 2019, 11, 474.
  3. Wang, J.; Song, Y.; Li, M.; Yuan, C.; Guo, F. Study on Low-Carbon Technology Innovation Strategies through Government–University–Enterprise Cooperation under Carbon Trading Policy. Sustainability 2022, 14, 9381.
  4. Zhu, B.; Zhang, M.; Huang, L.; Wang, P.; Su, B.; Wei, Y.-M. Exploring the Effect of Carbon Trading Mechanism on China’s Green Development Efficiency: A Novel Integrated Approach. Energy Econ. 2020, 85, 104601.
  5. Yu, N.; Chen, J.; Cheng, L. Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy. Int. J. Environ. Res. Public Health 2022, 19, 8565.
  6. Qi, X.; Han, Y. The Design of the Intertemporal Trading Ratio of Carbon Quotas. J. Clean. Prod. 2022, 370, 133481.
  7. Zhang, Y.-J.; Sun, Y.-F.; Huo, B.-F. The Optimal Product Pricing and Carbon Emissions Reduction Profit Allocation of CET-Covered Enterprises in the Cooperative Supply Chain. Ann. Oper. Res. 2021, 1–29.
  8. Xu, J.; Gao, Y.; Bai, Q.; Hu, T. Robust emission reduction strategy under different quota allocation methods of carbon trading policy. J. Ind. Eng. Eng. Manag. 2023, 37, 1–10.
  9. Chen, X.; Wang, J.; Hu, D. Study on the effect of rent-seeking on carbon emission trading market performance under free carbon emission allowances. Syst. Eng.-Theory Pract. 2018, 38, 93–101.
  10. Lin, B.; Xu, B. A Non-Parametric Analysis of the Driving Factors of China’s Carbon Prices. Energy Econ. 2021, 104, 105684.
  11. Xie, J.; Dou, X. Carbon Cap-and-trade Pricing Mechanism Based on Cooperative Game Theory. Manag. Rev. 2016, 28, 15–24.
  12. Lv, J.; Fan, X.; Wu, H. Sensitivity Analysis of Factors Influencing Carbon Prices in China. Soft Sci. 2021, 35, 123–130.
  13. Guo, J.; Gu, F.; Liu, Y.; Liang, X.; Mo, J.; Fan, Y. Assessing the Impact of ETS Trading Profit on Emission Abatements Based on Firm-Level Transactions. Nat. Commun. 2020, 11, 2078.
  14. Shi, B.; Li, N.; Gao, Q.; Li, G. Market Incentives, Carbon Quota Allocation and Carbon Emission Reduction: Evidence from China’s Carbon Trading Pilot Policy. J. Environ. Manag. 2022, 319, 115650.
  15. Cao, J.; Ho, M.S.; Ma, R.; Teng, F. When Carbon Emission Trading Meets a Regulated Industry: Evidence from the Electricity Sector of China. J. Public Econ. 2021, 200, 104470.
  16. Pan, M.; Wang, C. Research on the Corporate Emission Reduction Effect of the Carbon Emission Trading Pilot. Econ. Rev. J. 2022, 10, 73–81.
  17. Hu, Y.; Ren, S.; Wang, Y.; Chen, X. Can Carbon Emission Trading Scheme Achieve Energy Conservation and Emission Reduction? Evidence from the Industrial Sector in China. Energy Econ. 2020, 85, 104590.
  18. Wen, Y.; Hu, P.; Li, J.; Liu, Q.; Shi, L.; Ewing, J.; Ma, Z. Does China’s Carbon Emissions Trading Scheme Really Work? A Case Study of the Hubei Pilot. J. Clean. Prod. 2020, 277, 124151.
  19. Lyu, J.; Cao, M.; Wu, K.; Li, H.; Mohi-ud-din, G. Price Volatility in the Carbon Market in China. J. Clean. Prod. 2020, 255, 120171.
  20. Zhang, Y.; Zhang, J. Estimating the Impacts of Emissions Trading Scheme on Low-Carbon Development. J. Clean. Prod. 2019, 238, 117913.
  21. Lin, B.; Huang, C. Analysis of Emission Reduction Effects of Carbon Trading: Market Mechanism or Government Intervention? Sustain. Prod. Consum. 2022, 33, 28–37.
  22. Lin, B.; Jia, Z. Does the Different Sectoral Coverage Matter? An Analysis of China’s Carbon Trading Market. Energy Policy 2020, 137, 111164.
  23. Perera, R.S. An Evolutionary Game Theory Strategy for Carbon Emission Reduction in the Electricity Market. Int. Game Theory Rev. 2018, 20, 1850008.
  24. Feichtinger, G.; Lambertini, L.; Leitmann, G.; Wrzaczek, S. R&D for Green Technologies in a Dynamic Oligopoly: Schumpeter, Arrow and Inverted-U’s. Eur. J. Oper. Res. 2016, 249, 1131–1138.
  25. Lu, M.; Fang, X. Game Analysison Carbon Market form Allocation of Allowance. Chin. J. Manag. Sci. 2015, 23, 807–811.
  26. Zhu, Q.; Wang, Y.; Tian, Y. Analysis of an Evolutionary Game between Local Governments and Manufacturing Enterprises under Carbon Reduction Policies Based on System Dynamics. Oper. Res. Manag. Sci. 2014, 23, 71–82.
  27. Huang, X.; Ling, N. A Differential Game Model of Government and Enterprise Emission Reduction Based on Emission Permits Trading and Subsidy for Emission Abatement. J. Syst. Manag. 2020, 29, 1150–1160.
  28. Jiao, J.; Chen, J.; Li, L.; Li, F. A Study of Local Governments’ and Enterprises’ Actions in the Carbon Emission Mechanism of Subsidy or Punishment Based on the Evolutionary Game. Chin. J. Manag. Sci. 2017, 25, 140–150.
  29. Calel, R.; Dechezleprêtre, A. Environmental Policy and Directed Technological Change: Evidence from the European Carbon Market. Rev. Econ. Stat. 2016, 98, 173–191.
  30. Zhu, Q. A Perspective of Evolution for Carbon Emissions Trading Market: The Dilemma between Market Scale and Government Regulation. Discret. Dyn. Nat. Soc. 2017, 2017, e1432052.
  31. Liu, Y.; Zhang, X. Carbon Emissions Trading System and Corporate R&D Innovation—An Empirical Study Based on Triple Difference Model. Econ. Sci. 2017, 3, 102–114.
  32. Yu, P.; Liu, J. Researchon the Effects of Carbon Trading Market Size on Environment and Economic Growth. China Soft Sci. 2020, 4, 46–55.
  33. Maynard Smith, J. The Theory of Games and the Evolution of Animal Conflicts. J. Theor. Biol. 1974, 47, 209–221.
  34. Cui, N.; Li, J.; Tu, J.; Zhou, M. Evolutionary Game Analysis of Non-Governmental Organizations Participating in Garbage Management under the Background of Internet of Things. Sustainability 2022, 14, 13008.
  35. Gao, Y.; Jia, R.; Yao, Y.; Xu, J. Evolutionary Game Theory and the Simulation of Green Building Development Based on Dynamic Government Subsidies. Sustainability 2022, 14, 7294.
  36. Zhao, D.; Hao, J.; Cao, C.; Han, H. Evolutionary Game Analysis of Three-Player for Low-Carbon Production Capacity Sharing. Sustainability 2019, 11, 2996.
  37. Liu, L.; Wang, Z.; Li, X.; Liu, Y.; Zhang, Z. An Evolutionary Analysis of Low-Carbon Technology Investment Strategies Based on the Manufacturer-Supplier Matching Game under Government Regulations. Environ. Sci. Pollut. Res. 2022, 29, 44597–44617.
  38. Zhao, X.; Zhang, Y. The System Dynamics (SD) Analysis of the Government and Power Producers’ Evolutionary Game Strategies Based on Carbon Trading (CT) Mechanism: A Case of China. Sustainability 2018, 10, 1150.
  39. Chen, Z.; Wang, T. Photovoltaic Subsidy Withdrawal: An Evolutionary Game Analysis of the Impact on Chinese Stakeholders’ Strategic Choices. Sol. Energy 2022, 241, 302–314.
  40. Zhang, J.; Wen, S.; Li, H.; Lü, X. Evolutionary Game Analysis of Supply Chain Operations Decision under the Background of Low-carbon Economy—Based on the Perspective of Government-Enterprise-Consumer Synergy. Oper. Res. Manag. Sci. 2022, 1–9. Available online: (accessed on 27 April 2023).
  41. Fan, R.; Wu, T.; Fan, W. Research on Tripartite Governance Evolutionary Game Model and Environmental Governance Strategy Under Environment Tax and Regulation Capture. Soft Sci. 2022, 36, 122–130.
  42. Chen, L.; Wang, C.; Li, S.; Li, X.; Cao, D. Research on Multi-agent Evolution Game of Construction Safety Management Based on Prospect Theory. J. Saf. Environ. 2022, 1–11.
  43. Shen, J.; Gao, X.; He, W.; Sun, F.; Zhang, Z.; Kong, Y.; Wan, Z.; Zhang, X.; Li, Z.; Wang, J.; et al. Prospect Theory in an Evolutionary Game: Construction of Watershed Ecological Compensation System in Taihu Lake Basin. J. Clean. Prod. 2021, 291, 125929.
  44. Sun, H.; Gao, G.; Li, Z. Evolutionary Game Analysis of Enterprise Carbon Emission Regulation Based on Prospect Theory. Soft Comput. 2022, 26, 13357–13368.
  45. Uchida, S.; Yamamoto, H.; Okada, I.; Sasaki, T. Evolution of Cooperation with Peer Punishment under Prospect Theory. Games 2019, 10, 11.
  46. Yang, G.; Chen, J. Research on Enterprise Radical Technology Innovation Behavior—Evolutionary Game Analysis Based on Prospect Theory. J. Ind. Technol. Econ. 2020, 39, 57–64.
  47. Liu, M.; Li, Z.; Zhang, J. Evolutionary Game Analysis on Low-carbon Strategies of Government and Business Based on the View of Prospect Theory. Sci. Technol. Manag. Res. 2017, 37, 245–253.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
View Times: 236
Revisions: 2 times (View History)
Update Date: 31 Jul 2023
Video Production Service