Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning
This addresses the issue of climate policy perpetuating inequalities, which fuels disagreements in negotiations, by providing a tool for policymakers to explore equity in decision-making.
The authors tackled the problem of traditional Integrated Assessment Models (IAMs) being limited to single-objective optimization, which fails to capture trade-offs in climate policies, by introducing Justice, a framework that integrates IAM with Multi-Objective Multi-Agent Reinforcement Learning to generate equitable Pareto-optimal policies that balance climate and economic goals.
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.