Lidia Cano Pecharroman, Melissa O. Tier, Elke U. Weber
arXiv 15 Nov 2024 · Econometrics · publishedFrontiers in Climate (2025) · 2 citations (OpenAlex)
arXiv:2411.10628 · PDF · DOI · OpenAlex · Extracted main text
Efforts are needed to identify and measure both communities' exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating hazard vulnerability indicators is still in its infancy. Progress is needed to improve: 1) the selection of variables that are used as proxies to represent hazard vulnerability; 2) the applicability and scale for which these indicators are intended, including their transnational applicability. We administered an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea in order to collect data on exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to flood risk). We then use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | B. Wilson, E. Tate, and C. T. Emrich (2021) Flood recovery outcomes and disaster assistance barriers for vulnerable populations | 0.811 | 4 | 2 | 100% |
| 2 | I. Ajibade, M. Sullivan, C. Lower, L. Yarina, and A. Reilly (2022) Are managed retreat programs successful and just? a global mapping of success typologies, justice dimensions, and trade-offs | 0.737 | 3 | 2 | 100% |
| 3 | T. Chen and C. Guestrin (2016) XGBoost: A scalable tree boosting system | 0.644 | 2 | 2 | 100% |
| 4 | NASEM (2024) Constructing Valid Geospatial Tools for Environmental Justice | 0.585 | 3 | 1 | 100% |
| 5 | A. R. Siders and L. Gerber-Chavez (2021) Floodplain buyouts: Challenges, practices, and lessons learned | 0.585 | 3 | 1 | 100% |
| 6 | S. R. Foster, A. Baptista, K. H. Nguyen, J. Tchen, M. Tedesco, and R… NPCC4: Advancing climate justice in climate adaptation strategies for new york city | 0.511 | 2 | 1 | 100% |
| 7 | A. Greer, S. Brokopp Binder, and E. Zavar (2022) From hazard mitigation to climate adaptation: A review of home buyout program literature | 0.511 | 2 | 1 | 100% |
| 8 | K. J. Mach, C. M. Kraan, M. Hino, A. R. Siders, E. M. Johnston, and… (2019) Managed retreat through voluntary buyouts of flood-prone properties | 0.511 | 2 | 1 | 100% |
| 9 | A. Weber and R. Moore (2019) Going under: Long wait times for post-flood buyouts leave homeowners underwater | 0.511 | 2 | 1 | 100% |
| 10 | J. H. Friedman (2001) Greedy function approximation: A gradient boosting machine | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.