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.

翻译：应对气候变化需要全球各国协调一致的政策努力。这些努力以科学报告为依据，而科学报告部分依赖于综合评估模型（IAMs）——用于评估气候政策经济影响的突出工具。然而，传统的IAMs基于单一目标优化政策，限制了其捕捉经济增长、温度目标与气候正义之间权衡的能力。因此，政策建议被批评为延续不平等，加剧了政策谈判中的分歧。我们提出了Justice框架，这是首个将IAM与多目标多智能体强化学习（MOMARL）相结合的框架。通过纳入多目标，Justice生成的政策建议在平衡气候与经济目标的同时，揭示了公平性问题。此外，使用多智能体能够真实地反映不同政策参与者之间的互动。我们利用该框架识别了公平的帕累托最优政策，通过向决策者展示气候与经济政策中固有的权衡，促进了审慎决策。