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<p>Measuring risk and social vulnerability to natural hazards is central to the Sendai Framework for Disaster Risk Reduction since communities that understand and reduce their vulnerabilities are better positioned to absorb the impacts of events such as earthquakes. This has driven strong interest in quantifying social vulnerability, typically through composite indicators. Yet existing metrics face three key limitations: validation efforts are largely absent, leading indices rely on variables that ignore hazard context, and no agreed-upon framework guides indicator construction for earthquake applications. This study addresses these gaps by identifying a validated set of indicators specific to the earthquake context and applying uncertainty analysis (UA) and sensitivity analysis (SA) to identify robust modelling decisions for composite index construction. The resulting index reveals geographic variation in communities’ differential susceptibility to earthquake damage, with population density, the type and density of residential infrastructure, poverty, and unemployment emerging as primary drivers.</p>

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Advancing the measurement of social vulnerability to earthquakes at subnational levels of geography: a contextually valid and robust framework

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Christopher G. Burton,
  • Daniel Kraemer

摘要

Measuring risk and social vulnerability to natural hazards is central to the Sendai Framework for Disaster Risk Reduction since communities that understand and reduce their vulnerabilities are better positioned to absorb the impacts of events such as earthquakes. This has driven strong interest in quantifying social vulnerability, typically through composite indicators. Yet existing metrics face three key limitations: validation efforts are largely absent, leading indices rely on variables that ignore hazard context, and no agreed-upon framework guides indicator construction for earthquake applications. This study addresses these gaps by identifying a validated set of indicators specific to the earthquake context and applying uncertainty analysis (UA) and sensitivity analysis (SA) to identify robust modelling decisions for composite index construction. The resulting index reveals geographic variation in communities’ differential susceptibility to earthquake damage, with population density, the type and density of residential infrastructure, poverty, and unemployment emerging as primary drivers.