Abstract
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
