Climate change and federal aid disbursements after Hurricane Harvey

An extreme event attribution analysis

Datos Bibliográficos

ID15548844
AutoresKevin T Smiley (0000-0003-1856-6590, Louisiana State University, autor de correspondencia), Ilan Noy (0000-0003-3214-6568, Victoria University of Wellington), Michael Wehner (0000-0001-8423-7870), Michael F Wehner (0000-0001-5991-0082, Lawrence Berkeley National Laboratory), Oliver Wing (0000-0001-7515-6550, Clifton Hospital), KayLynn Larrison (0000-0002-7048-0682, Louisiana State University)
Año2025
Volumen20
Número9
Páginas094048-094048
Fecha de publicación2025-08-08
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Research Letters (JOURNAL)
Identificadores de la revistaISSN: 1748-9326 • E-ISSN: 1748-9326
EditorialIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/adf978
OpenAlexW4413124439
IdiomaEN
Referencias citadas27

The role climate change plays in increasing the burden placed on governments and insurers to pay for recovery has not been extensively explored and is the focus of this study. This study examines the impacts of climate change attributed flooding on federal disaster aid disbursement in Harris County, Texas following Hurricane Harvey in 2017. Our approach uses flood models to estimate the amount of flood damages attributable and not attributable to climate change under two climate change attribution scenarios from peer reviewed studies: 20% and 38% increases in rainfall associated with the hurricane due to climate change. These estimates are combined with census tract-level disbursement data for FEMA’s National Flood Insurance Program (NFIP) and the Individual Assistance (IA) part of the Individuals and Households Program. We employ spatial lag regression models with direct and spatial spillover effects to analyze the relationship between a tract’s flood damages—both attributed and not attributed to climate change—and federal disaster aid. We find that both types of flood damage shape federal aid disbursements, but that climate change attributed damages tend to have larger effect sizes (elasticities) especially for IA. Specifically, for a 1% increase in additional climate change attributed damages per household in a census tract (under the 20% scenario), expected NFIP levels in that census tract are 0.26% higher and IA levels are 0.3% higher. Implications center on federal funding in an era of climate change

Attribution · Climate change · Climatology · Event (particle physics · Extreme weather · Environmental Science · Fiscal Policy and Economic Growth · International Development and Aid · Psychology · Geology