Carbon Market Simulation with Adaptive Mechanism Design

TL;DR

This paper introduces an adaptive, multi-agent reinforcement learning framework to simulate and optimize carbon market mechanisms, aiding in effective allowance allocation and emission reduction strategies.

Contribution

It presents a novel MARL-based simulation framework for carbon markets, capturing agent behaviors and enabling better policy design under complex market dynamics.

Findings

MARL effectively balances productivity, equality, and emissions.

The simulation provides comprehensive insights into agent behaviors.

The framework improves understanding of market responses to policy changes.

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.