Evolutionary Game Theory for Sustainable Energy Systems: Strategic Bidding, Carbon Pricing, and Policy Optimization for Clean Energy Development

Abstract

As the world transitions toward a low-carbon economy, carbon pricing mechanisms, including carbon taxes and emissions trading systems, have emerged as fundamental policy instruments for reducing greenhouse gas emissions, particularly within the electricity sector. This comprehensive review examines the impact of these mechanisms on energy market dynamics through the analytical framework of evolutionary game theory (EGT), modeling strategic interactions among power generation companies, renewable energy firms, and regulatory authorities. Our analysis demonstrates that carbon pricing systematically increases operational costs for fossil fuel-based power plants while simultaneously providing competitive advantages to renewable energy producers, accelerating the adoption of cleaner energy technologies. The study emphasizes the critical role of coordinated policy interventions, including subsidies, penalties, and green certificate systems, in facilitating the adoption of clean technologies and optimizing market transition pathways. These findings underscore the importance of well-designed policy frameworks that align economic incentives across all stakeholders to drive sustainable energy system transformation. Additionally, this research demonstrates how EGT can effectively model the strategic bidding behavior of energy firms, providing valuable insights for optimal decision-making under carbon pricing fluctuations. Through comprehensive case studies and simulation analysis, the paper illustrates how firms can leverage evolutionary strategies to optimize investments in clean technologies, enhance inter-firm cooperation, and stabilize market dynamics. This work further explores future research directions, particularly the integration of machine learning and real-time data analytics with EGT to enhance predictive capabilities and strategic decision-making processes. By establishing connections between EGT and real-world energy market dynamics, this study provides a robust analytical framework for understanding long-term behavioral trends in energy markets. The results contribute significantly to the interdisciplinary literature at the intersection of game theory, energy policy, and sustainability science, offering valuable insights for policymakers, researchers, and industry leaders advancing clean energy transition strategies.

Keywords

Evolutionary game theory / Renewable energy systems / Carbon pricing mechanisms / Strategic bidding optimization / Energy market dynamics / Sustainability policy optimization

Author Contributions

Conceptualization, L.C., C.T., X.M. and T.Z.; methodology, L.C., C.T., X.M. and T.Z.; formal analysis, L.C., C.T.; investigation, L.C., C.T., X.M. and T.Z.; writing—original draft preparation, L.C., C.T., X.M. and T.Z.; writing—review and editing, L.C., C.T., X.M. and T.Z.; funding acquisition, L.C. and T.Z. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors are unable or have chosen not to specify which data has been used.

Funding

This work was supported in part by the National Natural Science Foundation of China (No. 52171331), in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515011311), in part by the Innovation Team Project for Universities in Guangdong Province (Natural Sciences) (No. 2024KCXTD031), and in part by the Guangzhou Education Bureau University Research Project - Graduate Research Project (No. 2024312278).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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