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Carbon Market Simulation with Adaptive Mechanism Design

Han Wang, Wenhao Li, Hongyuan Zha, Baoxiang Wang

TL;DR

This paper tackles the challenge of designing allocation policies in cap-and-trade carbon markets by building a hierarchical, model-free multi-agent reinforcement learning framework, featuring a high-level government agent that allocates carbon credits and lower-level enterprise agents that perform Gather-Trade-Build–style economic activities. The authors introduce a systematic carbon-market simulator that enables carbon credit allocation and trading, and they compare MARL-derived policies against baseline indicator approaches across multiple scenarios. Key contributions include (i) the simulator architecture and hierarchical MARL formulation, (ii) implementation of baseline indicator policies (GF, BM, SI) for rigorous comparison, and (iii) empirical evidence that learning-based allocations can better balance productivity, equality, and carbon emissions, even under different economy-climate coefficients and policy scales. By enabling policy experimentation without reliance on scarce real-world data, the work provides a flexible platform for exploring carbon-market design principles and validating economic theories under various market scales and regulatory settings, with potential practical impact for emission-reduction initiatives.

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://github.com/xwanghan/Carbon-Simulator.

Carbon Market Simulation with Adaptive Mechanism Design

TL;DR

This paper tackles the challenge of designing allocation policies in cap-and-trade carbon markets by building a hierarchical, model-free multi-agent reinforcement learning framework, featuring a high-level government agent that allocates carbon credits and lower-level enterprise agents that perform Gather-Trade-Build–style economic activities. The authors introduce a systematic carbon-market simulator that enables carbon credit allocation and trading, and they compare MARL-derived policies against baseline indicator approaches across multiple scenarios. Key contributions include (i) the simulator architecture and hierarchical MARL formulation, (ii) implementation of baseline indicator policies (GF, BM, SI) for rigorous comparison, and (iii) empirical evidence that learning-based allocations can better balance productivity, equality, and carbon emissions, even under different economy-climate coefficients and policy scales. By enabling policy experimentation without reliance on scarce real-world data, the work provides a flexible platform for exploring carbon-market design principles and validating economic theories under various market scales and regulatory settings, with potential practical impact for emission-reduction initiatives.

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://github.com/xwanghan/Carbon-Simulator.
Paper Structure (28 sections, 8 equations, 4 figures, 1 table)

This paper contains 28 sections, 8 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Works
  3. Carbon Market and Cap and Trade
  4. Carbon Market Simulated Models
  5. Machine Learning for Economic Design
  6. Multi-Agent Reinforcement Learning
  7. Carbon Market Modeling
  8. Hierarchical Multi-Agent RL Framework
  9. Lower-Level: Enterprise Modeling
  10. Reward Function.
  11. Skill.
  12. Action Space.
  13. Observation Space.
  14. Higher-Level: Government Modeling
  15. Action Space.
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Simulator structure. Left: One episode is divided into several periods, and in each periods, the government firstly acts to allocate carbon credits; the remaining of time, enterprises do their economics activities. Right: Enterprises' economics activities are modeled in a Gather-Trade-Build game; in this grid map, they can produce properties (build) to get coins, can move which can gather carbon credits and increase community's total power (green project investment), do carbon reduction invest action to reduce the carbon emission level (carbon reduction investment), also trade carbon credits and coins with each other.
  • Figure 2: Quantitative results of different allocation policies.
  • Figure 3: Simulation details for MARL and other approaches with SI, which include the social welfare, cumulative carbon emissions, the total number of trade activities, properties, government projects, and the investments.
  • Figure 4: Simulator dashboard, it presents detailed information encompassing enterprises' attributes, assets, and actions within a single time step across various example episodes under different policies. Additionally, it provides visual representations of the average carbon prices over different periods and presents rewards for both enterprises and government.