Identifying assets exposed to physical climate risk: A decision-support methodology

Abstract

Climate events are increasingly affecting supply chains, leading to frequent and costly impacts. Managers lack a systematic approach to evaluate risks to individual facilities and employees. We propose a decision support methodology to help quantify the exposure of both to ten most common climate hazards. Using both historical and scenario-based climate data, the methodology distinguishes three dimensions for understanding climate risk: anomaly, extreme variability, and acceleration, applied to each peril from historical to projected data. This approach allows for the isolation of the components of climate change by peril, facilitating a better understanding of each component. Furthermore, it enables the development of adaptative responses tailored to each of the climate dimensions. A case study of a logistics group with more than 200 warehouses across 181 locations in eight European countries illustrates the approach, demonstrating its practicality and effectiveness. Our methodology offers firms, large and small, the opportunity to reinforce their resilience in the face of multiple physical risks. The metrics and scores presented in this paper can be extended to assess the growing issues of climate risks as they apply to occupational health and safety as well as natural resources management.