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

2025-05-02

AI Summary

Conventional Integrated Assessment Models (IAMs) typically rely on single-objective optimization, failing to simultaneously reconcile climate targets, economic growth, and climate justice—thereby exacerbating North–South inequities and impeding international climate negotiations. Method: This paper introduces the Justice framework, the first IAM to embed multi-objective multi-agent reinforcement learning (MOMARL), modeling sovereign states as heterogeneous agents whose strategic interactions jointly optimize temperature control, aggregate economic welfare, and distributive justice. The approach integrates multi-objective optimization with Pareto frontier analysis to generate interpretable, fairness-aware policy recommendations. Contribution/Results: Justice ensures feasibility of both 2°C climate stabilization and global GDP growth while significantly improving the equity and efficiency of mitigation and climate finance burden-sharing between Northern and Southern countries. By explicitly representing agent heterogeneity and justice-sensitive objectives, it establishes a novel paradigm for deliberative, cooperative climate governance.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly 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.

Problem

• Traditional IAMs fail to address equity in climate policies
• Single-objective IAMs ignore trade-offs among growth, temperature, justice
• Lack of multi-agent modeling oversimplifies policy actor interactions

Innovation

• Multi-Objective Multi-Agent Reinforcement Learning (MOMARL)
• Integrated Assessment Model (IAM) with equity focus
• Pareto-optimal policies balancing climate and economy