用强化学习比较经济与生活质量优先的气候适应路径
Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways
- 用强化学习在不确定气候下寻找适应路径
- 生活质量优先的方案支出更高且分布更均
- 适合关注气候政策价值权衡的研究者
气候变化将导致洪水事件频率和严重程度上升，亟需协调一致的适应政策。然而，制定有效政策依赖于应对长期气候影响的不确定性，且政策中常隐含重要规范性选择。本文提出利用强化学习（RL）在不确定性条件下识别适应路径，并显式建模与比较不同优先目标（如经济与福祉）的影响。我们采用集成评估模型（IAM）整合降雨与洪水模型，量化洪水对生活质量（QoL）、交通及基础设施破坏的影响。结果显示，以生活质量优先的模型导致更高适应支出，并实现研究区域更均匀的支出分布，凸显规范性假设对适应政策的重要影响。该框架已开源：https://github.com/MLSM-at-DTU/maat_qol_framework。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。