The climate and ocean risk vulnerability index: Measuring coastal city resilience to inform action - Rouleau, Tracy; Stuart, Jack; Call, Maia; Yozell, Sally; Yoshioka, Nagisa; Maekawa, Miko; Fiertz, Natalie (Frontiers in Sustainable Cities, 2022)
Today, coastal cities worldwide are struggling with the complex interaction of environmental threats, economic development, and societal inequity. The acceleration of global climate change, which will impact ocean health, sea level, rainfall patterns, and temperatures, will only further exacerbate the ongoing challenges faced by coastal cities. Coastal cities face interconnected risks that necessitate the use of a data collection and an assessment approach that can assess these impacts through a holistic lens. Risk is the interaction of hazards, exposure, and vulnerability, and while data on hazards and exposure is becoming more widely available, data on the vulnerability of urban coastal populations remains limited. These data gaps are particularly acute for the Global South, where climate change is expected to have the greatest near-term impacts. Policymakers need city-specific data to best understand their levels of risk and engage in effective adaptation planning. This paper introduces the Climate and Ocean Risk Vulnerability Index (CORVI), its conceptual framework, methodology, and protocol. The article also demonstrates the application of CORVI through two pilot projects in Castries, Saint Lucia and Kingston, Jamaica. It concludes with a reflection of lessons learned from the pilot projects, and an assessment of the utility of the structured expert judgement (SEJ) for collecting data and measuring risk in data sparse environments. This paper makes two primary contributions. First it introduces CORVI, a decision support tool that assesses climate risk and resilience in a coastal city. The tool uses the SEJ methodology to display risk scores across 10 risk categories and 94 indicators addressing ecological, financial, and political risk. Second, it demonstrates the use of the SEJ methodology in data sparse environments.