Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways
This work addresses climate adaptation policymaking for communities facing flood risks, though it is incremental as it applies existing RL methods to a new normative modeling context.
The study tackled the problem of designing climate adaptation policies under uncertainty by using Reinforcement Learning to compare economic versus quality of life priorities, finding that prioritizing quality of life leads to more adaptation spending and a more even distribution of spending across the study area.
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.