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<p>This study investigates hurricane risk distribution across 6767 Texas census tracts using FEMA’s National Risk Index and geospatial analyses to evaluate the environmental justice (EJ) hypothesis that socially vulnerable populations face disproportionate hurricane exposure, measured by expected annual loss (EAL). Statistical analysis, including Pearson correlation and regression, indicates no significant global relationship (<i>r</i> = 0.0071, <i>p</i> &gt; 0.05), with social vulnerability factors (derived from FEMA’s Social Vulnerability Index Score, encompassing 29 socioeconomic, demographic, and housing indicators from the American Community Survey) accounting for only 0.01% of hurricane risk variance. Geospatial methods reveal distinct patterns: social vulnerability clusters in eastern rural areas, urban centers, and border regions, while hurricane risk is concentrated along the Gulf Coast. Only 3.02% of tracts exhibit significant bivariate associations, suggesting limited broad-scale overlap, though localized compound effects in high-impact zones (e.g., Q4 quartiles) indicate potential interactions meriting further exploration. These results highlight that geographic factors, such as coastal proximity and elevation, predominantly drive EAL, though localized EJ disparities persist. Vulnerable populations often inhabit low-lying, flood-prone areas with inadequate infrastructure, exacerbating flooding impacts and recovery challenges. Accordingly, the study advocates geographic-based mitigation in hurricane-prone regions and vulnerability-targeted recovery to address flooding risks, infrastructure gaps, and long-term inequities, enhancing hazard-specific EJ frameworks. Geographically weighted regression (GWR) strengthens these findings, achieving a robust local fit (adjusted <i>R</i><sup>2</sup> = 0.95) and revealing spatial non-stationarity, with localized coefficients (− 0.031 to 0.040) and non-clustered residuals, reinforcing targeted interventions in coastal EJ hotspots despite weak global correlations.</p>

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Challenging Environmental Justice Assumptions: Geographic Dominance Over Social Vulnerability in Texas Hurricane Risk Distribution

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Omid Mansourihanis,
  • Ebrahim Seyedebrahimi,
  • Ayda Zaroujtaghi,
  • Tara Zafari

摘要

This study investigates hurricane risk distribution across 6767 Texas census tracts using FEMA’s National Risk Index and geospatial analyses to evaluate the environmental justice (EJ) hypothesis that socially vulnerable populations face disproportionate hurricane exposure, measured by expected annual loss (EAL). Statistical analysis, including Pearson correlation and regression, indicates no significant global relationship (r = 0.0071, p > 0.05), with social vulnerability factors (derived from FEMA’s Social Vulnerability Index Score, encompassing 29 socioeconomic, demographic, and housing indicators from the American Community Survey) accounting for only 0.01% of hurricane risk variance. Geospatial methods reveal distinct patterns: social vulnerability clusters in eastern rural areas, urban centers, and border regions, while hurricane risk is concentrated along the Gulf Coast. Only 3.02% of tracts exhibit significant bivariate associations, suggesting limited broad-scale overlap, though localized compound effects in high-impact zones (e.g., Q4 quartiles) indicate potential interactions meriting further exploration. These results highlight that geographic factors, such as coastal proximity and elevation, predominantly drive EAL, though localized EJ disparities persist. Vulnerable populations often inhabit low-lying, flood-prone areas with inadequate infrastructure, exacerbating flooding impacts and recovery challenges. Accordingly, the study advocates geographic-based mitigation in hurricane-prone regions and vulnerability-targeted recovery to address flooding risks, infrastructure gaps, and long-term inequities, enhancing hazard-specific EJ frameworks. Geographically weighted regression (GWR) strengthens these findings, achieving a robust local fit (adjusted R2 = 0.95) and revealing spatial non-stationarity, with localized coefficients (− 0.031 to 0.040) and non-clustered residuals, reinforcing targeted interventions in coastal EJ hotspots despite weak global correlations.