A Multi-Hazard Approach to Climate Migration: Testing the Intersection of Climate Hazards, Population Change, and Location Desirability from 2000 to 2020
Author:
Hirsch Zachary M.1ORCID, Porter Jeremy R.123, Buresch Jasmina M.1ORCID, Medgyesi Danielle N.13ORCID, Shu Evelyn G.14ORCID, Hauer Matthew E.5
Affiliation:
1. First Street, New York, NY 10017, USA 2. Department of Sociology and Demography, City University of New York, New York, NY 10017, USA 3. Environmental Health Sciences Department, Columbia University’s Mailman School of Public Health, New York, NY 10032, USA 4. American Institutes for Research, Arlington, VA 22202, USA 5. Department of Sociology and Center for Demography and Population Health, Florida State University, Tallahassee, FL 32306, USA
Abstract
Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, we analyze historical population trends in relation to climate risk and exposure metrics for various hazards. Our findings reveal nuanced patterns of climate-induced population change, including “risky growth” areas where economic opportunities mitigate climate risks, sustaining growth in the face of observed exposure; “tipping point” areas where the amenities are slowly giving way to the disamenity of escalating hazards; and “Climate abandonment” areas experiencing exacerbated out-migration from climate risks, compounded by other out-migration market factors. Even within a single county, these patterns vary significantly, underscoring the importance of localized analyses. Projecting population impacts due to climate risk to 2055, flood risks are projected to impact the largest percentage of areas (82.6%), followed by heatwaves (47.4%), drought (46.6%), wildfires (32.7%), wildfire smoke (21.7%), and tropical cyclone winds (11.1%). The results underscore the importance of understanding hyperlocal patterns of risk and change in order to better forecast future patterns.
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