Unveiling contrasting impacts of heat mitigation and adaptation policies on U.S. internal migration

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Methodological approach combining machine learning with attribution mapping to assess policy impacts on migration offers a template for climate-migration studies.

🏢実務担当者:Local planners should consider that heat mitigation policies may inadvertently increase out-migration, while adaptation policies can retain populations.

🏛政策担当者:Heat policy design must account for heterogeneous effects on migration, with aging populations particularly sensitive to behavioral and cultural policies.

気候誘発性の人口移動が注目を集める一方で、人間の気候への取り組みが果たす役割は未だ十分に調査されていない。本研究では、機械学習とアトリビューション・マッピングを組み合わせ、4,713件の熱関連政策（HPs）が米国郡間の11,177件の移動フローに与える影響を分析する。熱適応政策（APs）と熱緩和政策（MPs）は国内移動に対して有意かつ相反する影響を及ぼすことが判明した。すなわち、APsは流出移動を減少させる一方、MPsはそれを増加させる。これらの政策は、政策タイプ間で移動に対して異質な効果を持つ。出身地における行動・文化型MPsは、政策1件あたり年間流出量を0.24%-0.68%（95%信頼区間）増加させる一方、目的地における行動・文化型APsは出身地の流出量を0.11%-1.55%（95%信頼区間）増加させる。移動パターンは、出身地および目的地の両方における所得、高齢化、教育、人種的多様性によって非線形的に調整される。高齢化率は、出身地における行動・文化型MPsへの移動反応を形成する上で最も顕著なU字型の関係を示し、出身地における制度型MPsおよび目的地における自然基盤型MPsに対しては逆U字型を示す。これらの知見は、地球温暖化と政策介入が継続する中で、HPsが移動にどのように影響するかについて、政策立案者に重要な洞察を提供する。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

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.

• openalex https://doi.org/10.48550/arxiv.2604.10570first seen 2026-05-05 19:13:57 · last seen 2026-06-17 09:17:14