Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning

TL;DR

This paper introduces Justice, a novel framework combining Integrated Assessment Models with Multi-Objective Multi-Agent Reinforcement Learning to generate equitable climate policy recommendations that balance economic, climate, and justice objectives.

Contribution

It is the first to integrate IAM with MOMARL, enabling the exploration of trade-offs and equity considerations in climate policy optimization.

Findings

Identifies equitable Pareto-optimal policies.

Highlights trade-offs among economic growth, climate goals, and justice.

Provides a realistic multi-agent representation of policy interactions.

Abstract

Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce Justice, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, Justice generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy.