While climate-induced population migration has received rising attention, the role played by human climate endeavors remains underexplored. Here, we combine machine learning with attribution mapping to analyze the impacts of 4,713 heat-related policies (HPs) on 11,177 migration flows between U.S. counties. We find that heat adaptation policies (APs) and heat mitigation policies (MPs) have significant and opposing impacts on internal migration: APs reduce out-migration, while MPs increase it. These policies have heterogeneous effects on migration among policy types. Behavioral and cultural MPs at origins lead to a 0.24%-0.68% (95% confidence interval) increase in annual outflows per policy, whereas behavioral and cultural APs at destinations elevate outflows of origins by 0.11%-1.55% (95% confidence interval). Migration patterns are nonlinearly moderated by income, ageing, education, and racial diversity of both origin and destination counties. Ageing rates have the most noticeable U-shaped relationship in shaping migration responses to behavioral and cultural MPs at origins, and inverted U-shapes for institutional MPs at origins and nature-based MPs at destinations. These findings offer critical insights for policymakers on how HPs influence migration as global warming and policy interventions persist.

翻译：尽管气候引发的人口迁移日益受到关注，但人类气候应对举措所起的作用仍未被充分探索。本研究将机器学习与归因映射相结合，分析了4,713项热相关政策对美国县域间11,177条迁移流的影响。研究发现，热适应政策与热减缓政策对内部迁移存在显著且相反的效应：适应政策减少人口流出，而减缓政策则加剧流出。这些政策对迁移的影响因类型不同而呈现异质性。来源地的行为与文化类减缓政策每项导致年流出量增加0.24%-0.68%（95%置信区间），而目的地的行为与文化类适应政策则使来源地年流出量增加0.11%-1.55%（95%置信区间）。迁移模式受到来源地与目的地县收入水平、老龄化程度、教育程度及种族多样性的非线性调节。老龄化率在塑造来源地行为与文化类减缓政策引发的迁移响应中呈现最显著的U型关系，而对来源地制度类减缓政策和目的地自然类适应政策的迁移响应则呈倒U型关系。这些发现为政策制定者提供了关键见解，揭示了在全球变暖与政策干预持续背景下热相关政策如何影响人口迁移。