Jasmina M. Buresch

Climate-induced migration is increasingly recognized as a major demographic consequence of environmental change, yet projections vary widely due to differences in spatial scale, hazard coverage, and modeling approaches. This study introduces the First Street Global Climate Migration Model (FS-GCMM), a globally consistent, multi-hazard framework that estimates climate-driven population redistribution at a 12.5 km resolution across all countries through 2100. The model integrates high-resolution global climate hazard datasets, including flood (GloFAS), wind (IBTrACS and ERA5), drought (ERA5), wildfire (Global Fire Atlas), and extreme heat and cold (ERA5-LAND) datasets, with gridded population data from NASA SEDAC’s Gridded Population of the World (GPWv4) and Shared Socioeconomic Pathway (SSP) projections. To identify climate-related migration effects, we applied within-country propensity score matching to construct balanced samples of exposed and unexposed grid cells with similar socioeconomic, demographic, geographic, and governance characteristics. Hazard-specific impacts on annualized population change from 2000 to 2020 were then estimated using mixed-effects ridge regression with country-level random effects to account for cross-national heterogeneity and multicollinearity. These empirically derived coefficients were applied to SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios to project future climate-driven outmigration, which was subsequently redistributed using a spatial attractiveness framework incorporating economic opportunity, population density, climate safety, and geographic proximity. Results indicate statistically significant negative effects of all modeled hazards on population retention globally, with approximately 199.5 million people projected to experience climate-driven displacement by 2055 under SSP2-4.5.

As climate disasters intensify, their financial shockwaves increasingly threaten residential stability and the resilience of the U.S. mortgage market. While prior research links natural disasters to payment delinquency, far less is known about foreclosure—the terminal outcome of housing distress. We construct a novel property-level panel covering 55 flood, wildfire, and hurricane events, integrating transactional, mortgage, and insurance data. A difference-in-differences framework compares foreclosure rates for damaged parcels with nearby undamaged controls within narrowly defined hazard perimeters. Results show that flooding substantially increases foreclosure risk: inundated properties experience a 0.29-percentage-point rise in foreclosure likelihood within three years, with effects concentrated outside federally mandated flood-insurance zones. In contrast, wildfire and hurricane wind damage are associated with lower foreclosure incidence, likely reflecting standard insurance coverage and rapid post-event price recovery. These findings suggest that physical destruction alone does not drive credit distress; rather, insurance liquidity and post-disaster equity dynamics mediate outcomes. Policy interventions that expand flood insurance coverage, stabilize insurance markets, and embed climate metrics in mortgage underwriting could reduce systemic exposure. Absent such measures, climate-driven foreclosures could account for nearly 30% of lender losses by 2035, posing growing risks to both household wealth and financial stability.

Extreme weather events, like flooding, are expected to become more severe due to climate change and increasingly impact populations across the US. Adding to this challenge, populations have concurrently settled in risky areas that were previously thought to have low, or no, exposure. Objective: This research seeks to understand the unique contribution of population growth and climate change as independent components of future risk levels in the US. To do so, future population level forecasts are coupled with future flood projections along all five Shared Socioeconomic Pathways (SSPs) at the block group level across the US. The results indicate that, across the five SSPs, the most increase in exposure will occur in SSP5 (+470,719), and the least will occur in SSP 3 (+57,189). By decomposing the contributions from flood and population growth, we find that the population growth-induced effect contributed to an increase in the population exposure for all of the SSPs except for SSP3. This research, and these results, provide a foundation for understanding future risks of flood exposure in an isolated framework and lay the groundwork for the development and integration of planning, adaptation, and mitigation efforts that may be used to address the growing risk of flooding in the context of the population forecasts provided here.

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.