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

TL;DR

This paper explores how reinforcement learning can be used to develop climate adaptation policies that explicitly consider different priorities like economic impact versus quality of life, revealing how normative choices influence adaptation strategies.

Contribution

It introduces a novel RL-based framework integrating an IAM with flood impact models to compare adaptation pathways based on different normative priorities.

Findings

Models prioritizing QoL lead to more adaptation spending.

Distribution of adaptation efforts varies significantly with normative assumptions.

Framework is publicly available for further research.

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.