Hotspots of Inequity in Climate Adaptation: Explaining the Stratification of U.S. Ecowelfare Using Space-Time and Machine Learning Analysis

As climate risk intensifies and ecowelfare is increasingly implicated in climate adaptation, we examine how FEMA’s Individuals and Households Program (IHP) allocates aid in the United States. We ask how and why IHP allocates aid, framing the analysis through a climate-justice lens that centers distributive and procedural equity. Using a county–year panel (2009–2022), we map funding hot/cold spots and estimate space–time models of per-recipient IHP funding, benchmarking against machine learning approaches. Results show that aid rises with a county’s own disaster frequency but falls when neighboring counties are simultaneously hit. Direct sociodemographic penalties are limited once space–time dependence is modeled, except for a persistent shortfall in counties with larger multiracial populations and a negative neighboring effect tied to Hispanic composition. Poverty and population size show positive neighboring effects, and counties in Democratic-governed states receive more aid, consistent with higher state capacity. Machine learning corroborates hazards’ primacy and highlights disaster-count thresholds and interactions. Implications for climate justice and adaptation include strengthening regional capacity, expanding language-access and navigator programs that help households apply for aid, and adopting local-national coordination standards to make ecowelfare more equitable and resilient.