A perspective on quantifying resilience: Combining community and infrastructure capitals

The resilience of communities has emerged as a major goal in policy and practice. Cities, states, and counties within the United States and around the world are passing laws requiring the incorporation of climate-related hazard vulnerability assessments within their master plan updates for resilience planning and design. The resilience of communities under present and future scenarios is thus becoming a cornerstone of decision making and actions. Decisions that would enhance resilience, however, span multiple sectors and involve various stakeholders. Quantifying community resilience is a key step in order to describe the preparedness level of communities, and subsequently locating non-resilient areas to further enhance their capacity to endure disasters. Two main approaches are currently being pursued to evaluate resilience. The first approach is the “community resilience” developed mainly by social scientists and planners, and it captures social resilience using numerous pre-disaster attributes to describe the functioning of a community. This approach subsumes that pre-disaster attributes can predict the community resilience to a disaster. The second approach is adopted for infrastructure resilience, mostly used by engineers, and it focuses on robustness, redundancy, resourcefulness, and rapidity. This approach is appropriate for systems that are operated by highly skilled personnel and where the actions are of engineering type. In this paper, we provide an overview of the two approaches, and we leverage their limitations to propose a hybrid approach that combines community and infrastructure capitals into an Area Resilience metric, called ARez. ARez captures the role/impact of both infrastructure and community and combines five sectors: energy, public health, natural ecosystem, socio-economic, and transportation. We present a proof-of-concept for the ARez metric, showing its practicality and applicability as a direct measure for resilience, over various time scales.