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Optimizing Carbon Pricing Mechanisms in Power Sector Trading Systems Under Carbon Neutrality Goals: A Multi-Agent Evolutionary Game Simulation
- 1 South China University of Technology
* Author to whom correspondence should be addressed.
Abstract
This study proposes a multi-agent evolutionary game model to optimize carbon pricing mechanisms in electricity markets under carbon neutrality constraints. By simulating strategic interactions among power generators, regulators, and consumers, we analyze how dynamic carbon pricing affects investment decisions, emission reductions, and market stability. The model incorporates heterogeneous agents with adaptive learning behaviors, including coal-fired plants (cost minimizers), renewable energy firms (innovation seekers), and policymakers (emission cap enforcers). Using China’s power sector as a case study (2020–2040), our simulations reveal that a hybrid carbon pricing mechanism—combining a floor price with tradable green certificates—achieves Pareto efficiency, reducing cumulative emissions by 34% while maintaining grid reliability. Sensitivity analysis identifies critical thresholds: when carbon prices exceed $80/ton, coal-to-renewable transitions accelerate nonlinearly. The findings provide a computational toolkit for designing adaptive carbon markets aligned with net-zero transitions.
Keywords
Carbon pricing, power sector, carbon neutrality, evolutionary game theory, multi-agent simulation, tradable green certificates, coal phase-out
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Cite this article
Wang,L. (2025). Optimizing Carbon Pricing Mechanisms in Power Sector Trading Systems Under Carbon Neutrality Goals: A Multi-Agent Evolutionary Game Simulation. Journal of Economic and Managerial Dynamics,1(1),72-79.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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