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<p>Climate change and its severe health impacts raise serious concerns about climate justice. To measure a population’s vulnerability to climate change, researchers often apply indicator-based composite indices. In this work, we present a modeling framework for constructing a climate health vulnerability index (CHVI) and examine how methodological choices influence the identification of vulnerable communities. Using 44 indicators in New York State and two structural designs—inductive (principal component analysis) and deductive (indicator aggregation)—we conducted multiple sensitivity analyses to evaluate the robustness of CHVI outcomes. The deductive design was less sensitive to model inputs and specifications than the inductive design. Among the construction steps, principal component selection and indicator normalization were the most influential factors for the inductive and deductive designs, respectively. Quantification of climate-change-related vulnerability through transparent and reproducible index development can inform policy planning and resource allocation for the most disadvantaged populations.</p>

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An updated modeling framework and sensitivity analysis of methodology for the climate health vulnerability index

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Pin Wang,
  • Fiona O’Brien,
  • Ji-Young Son,
  • Seulkee Heo,
  • Michelle L. Bell,
  • Robert Dubrow,
  • Kai Chen

摘要

Climate change and its severe health impacts raise serious concerns about climate justice. To measure a population’s vulnerability to climate change, researchers often apply indicator-based composite indices. In this work, we present a modeling framework for constructing a climate health vulnerability index (CHVI) and examine how methodological choices influence the identification of vulnerable communities. Using 44 indicators in New York State and two structural designs—inductive (principal component analysis) and deductive (indicator aggregation)—we conducted multiple sensitivity analyses to evaluate the robustness of CHVI outcomes. The deductive design was less sensitive to model inputs and specifications than the inductive design. Among the construction steps, principal component selection and indicator normalization were the most influential factors for the inductive and deductive designs, respectively. Quantification of climate-change-related vulnerability through transparent and reproducible index development can inform policy planning and resource allocation for the most disadvantaged populations.