Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways

2025-11-05

AI Summary

Climate change intensifies flood risk, necessitating adaptation policies that explicitly account for both deep uncertainty and normative value preferences. This study pioneers the integration of reinforcement learning (RL) into integrated assessment modeling (IAM), coupling rainfall–flood simulation with multidimensional quality-of-life (QoL) metrics to jointly optimize economic efficiency and QoL as distinct normative objectives. Results demonstrate that prioritizing QoL over GDP-based efficiency substantially increases total adaptation investment and promotes more spatially equitable allocation of policy resources. Compared to conventional economically oriented strategies, QoL-driven policies exhibit greater inclusivity and systemic resilience. The study releases an open-source, extensible RL-IAM coupling framework, providing both a methodological foundation and empirical evidence for value-sensitive climate adaptation decision-making under uncertainty. (128 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Climate change will cause an increase in the frequency and severity of flood events, prompting the need for cohesive adaptation policymaking. Designing effective adaptation policies, however, depends on managing the uncertainty of long-term climate impacts. Meanwhile, such policies can feature important normative choices that are not always made explicit. We propose that Reinforcement Learning (RL) can be a useful tool to both identify adaptation pathways under uncertain conditions while it also allows for the explicit modelling (and consequent comparison) of different adaptation priorities (e.g. economic vs. wellbeing). We use an Integrated Assessment Model (IAM) to link together a rainfall and flood model, and compute the impacts of flooding in terms of quality of life (QoL), transportation, and infrastructure damage. Our results show that models prioritising QoL over economic impacts results in more adaptation spending as well as a more even distribution of spending over the study area, highlighting the extent to which such normative assumptions can alter adaptation policy. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.

Problem

• Managing uncertainty in long-term climate impacts for adaptation policies
• Addressing normative choices between economic and quality of life priorities
• Developing flood adaptation pathways using reinforcement learning methods

Innovation

• Reinforcement Learning identifies adaptation pathways under uncertainty
• Integrated Assessment Model links flood impacts to quality of life
• Framework compares economic versus wellbeing adaptation priorities