{"type":"ScholarlyArticle","title":"Carbon Market Simulation with Adaptive Mechanism Design","authors":[{"name":"Han Wang","slug":"han-wang","given":"Han","family":"Wang","affiliation":"The Chinese University of Hong Kong , Shenzhen"},{"name":"Wenhao Li","slug":"wenhao-li","given":"Wenhao","family":"Li","affiliation":"The Chinese University of Hong Kong , Shenzhen"},{"name":"Hongyuan Zha","slug":"hongyuan-zha","given":"Hongyuan","family":"Zha","affiliation":"The Chinese University of Hong Kong , Shenzhen"},{"name":"Baoxiang Wang","slug":"baoxiang-wang","given":"Baoxiang","family":"Wang","affiliation":"The Chinese University of Hong Kong , Shenzhen; Vector Institute"}],"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:\r\n//anonymous.4open.science/r/Carbon-Simulator.","doi":"10.24963/ijcai.2024/1041","url":"https://ijcai.org/proceedings/2024/1041","pdf":"https://ijcai.org/proceedings/2024/1041.pdf","markdown":"https://ijcai.org/proceedings/2024/1041.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1041","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1041,"pages":{"first":8824,"last":8828},"keywords":["Agent-based and Multi-agent Systems: MAS: Applications","Agent-based and Multi-agent Systems: MAS: Multi-agent learning","Agent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools","Game Theory and Economic Paradigms: GTEP: Mechanism design","Machine Learning: ML: Reinforcement learning","Multidisciplinary Topics and Applications: MDA: Economics","Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence"],"videos":[],"references":[{"position":1,"raw":"[Bellman, 1957] Richard Bellman. 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