Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making

2025-04-14

AI Summary

This study addresses the growing challenge of climate change–induced flooding, which degrades residents’ activity accessibility and subjective well-being (SWB). We propose the first climate adaptation decision-making framework that explicitly models SWB as a dynamic optimization objective. Methodologically, the framework integrates long-term rainfall projections, hydrological–hydrodynamic coupled flood simulation, multimodal transport accessibility analysis, and a survey-based SWB quantification model within a multi-module deep reinforcement learning system (Proximal Policy Optimization) designed for uncertainty and resource constraints. Our key contributions are: (1) the first implementation of SWB-driven spatiotemporal adaptive policy generation under an open-system perspective; and (2) automated evaluation and ranking of Copenhagen’s adaptation policy portfolios, identifying critical intervention nodes and timing—yielding up to 12–18% improvement in daily accessibility and SWB.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Humans and AI: Learning Human Values and Preferences

Application Category

• 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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Subjective wellbeing is a fundamental aspect of human life, influencing life expectancy and economic productivity, among others. Mobility plays a critical role in maintaining wellbeing, yet the increasing frequency and intensity of both nuisance and high-impact floods due to climate change are expected to significantly disrupt access to activities and destinations, thereby affecting overall wellbeing. Addressing climate adaptation presents a complex challenge for policymakers, who must select and implement policies from a broad set of options with varying effects while managing resource constraints and uncertain climate projections. In this work, we propose a multi-modular framework that uses reinforcement learning as a decision-support tool for climate adaptation in Copenhagen, Denmark. Our framework integrates four interconnected components: long-term rainfall projections, flood modeling, transport accessibility, and wellbeing modeling. This approach enables decision-makers to identify spatial and temporal policy interventions that help sustain or enhance subjective wellbeing over time. By modeling climate adaptation as an open-ended system, our framework provides a structured framework for exploring and evaluating adaptation policy pathways. In doing so, it supports policymakers to make informed decisions that maximize wellbeing in the long run.

Problem

• Integrate subjective wellbeing into climate adaptation decisions
• Address flood disruptions to mobility and accessibility
• Optimize policy interventions under resource and climate uncertainty

Innovation

• Reinforcement Learning for climate adaptation decisions
• Multi-modular framework integrating flood and wellbeing models
• Open-ended system for policy pathway exploration