Carbon Market Simulation with Adaptive Mechanism Design

Carbon Market Simulation with Adaptive Mechanism Design

Han Wang, Wenhao Li, Hongyuan Zha, Baoxiang Wang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8824-8828. https://doi.org/10.24963/ijcai.2024/1041

A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility, i.e., reducing carbon emissions to tackle climate change. Cap and trade stands as a critical principle based on allocating and trading carbon allowances (carbon emission credit), enabling economic agents to follow planned emissions and penalizing excess emissions. A central authority is responsible for introducing and allocating those allowances in cap and trade. However, the complexity of carbon market dynamics makes accurate simulation intractable, which in turn hinders the design of effective allocation strategies. To address this, we propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL). Government agents allocate carbon credits, while enterprises engage in economic activities and carbon trading. This framework illustrates agents’ behavior comprehensively. Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions. Our project is available at https: //anonymous.4open.science/r/Carbon-Simulator.
Keywords:
Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence
Agent-based and Multi-agent Systems: MAS: Applications
Agent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools
Agent-based and Multi-agent Systems: MAS: Multi-agent learning
Game Theory and Economic Paradigms: GTEP: Mechanism design
Machine Learning: ML: Reinforcement learning
Multidisciplinary Topics and Applications: MDA: Economics