Unveiling contrasting impacts of heat mitigation and adaptation policies on U.S. internal migration
Chao Li, Xing Su, Chao Fan, Yang Li, Luping Li, Chunmo Zheng, Wenglong Chao, Leena Jarvi, Han Lin, Juan Tu

TL;DR
This study uses machine learning and attribution mapping to analyze how heat-related policies in the U.S. differently influence internal migration, revealing opposing effects of adaptation and mitigation policies on population flows.
Contribution
It introduces a novel combination of machine learning and attribution mapping to quantify the contrasting impacts of heat adaptation and mitigation policies on migration patterns.
Findings
Heat adaptation policies reduce out-migration, while mitigation policies increase it.
Behavioral and cultural policies at origins increase outflows by 0.24%-0.68%.
Migration responses are nonlinearly moderated by income, age, education, and racial diversity.
Abstract
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…
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