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

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.

@article{vandervoort2025_2504.10031,
  title={ Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making },
  author={ Arthur Vandervoort and Miguel Costa and Morten W. Petersen and Martin Drews and Sonja Haustein and Karyn Morrissey and Francisco C. Pereira },
  journal={arXiv preprint arXiv:2504.10031},
  year={ 2025 }
}