Climate change adaptation policy requires assessing a community's vulnerability based on its socio-economic characteristics. A predominant approach to vulnerability assessment is indicator-based, wherein variables are aggregated to assess the vulnerability of units in a system (e.g., neighborhoods in a city). Here we show that a particular evidence-based predictive statistics approach can address two shortcomings of the most commonly-used indicator-based approach: lack of a means of validation and problematic weighting of individual indicators. We demonstrate how robust evidence-based models can produce frameworks that overcome these limitations. Using the case study of Hurricane Sandy in the State of New Jersey, we conducted two-pronged validated vulnerability assessments, based on insurance claim payouts and assistance grants. The latter needs-based assessment shows that Minorities are substantially more vulnerable than others based on a significant negative association with assistance approval rate (approved claims divided by all claims). Our findings highlight issues discussed in the literature within the context of climate justice and equity. Such an approach is helpful locally, but also when adaptation plans are developed over broad scales of time and space considering disparities between regions or across multiple jurisdictions.
Barankin, RA; Portman, ME; Kirshen, P; Bowen, RE
Evidence-Driven Approach for Assessing Social Vulnerability and Equality During Extreme Climatic Events
Frontiers In Water