Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making
This work addresses the problem of optimizing subjective wellbeing in climate adaptation for policymakers, though it is incremental as it applies existing reinforcement learning methods to a new domain-specific integration.
The authors tackled the challenge of climate adaptation decision-making by developing a reinforcement learning framework to integrate subjective wellbeing into policy selection for Copenhagen, Denmark, enabling identification of spatial and temporal interventions to sustain or enhance wellbeing over time.
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.