Analysis of the influencing factors on industrial resilience to flood disasters using a semi-markov recovery model: A case study of the Heavy Rain Event of July 2018 in Japan

Abstract

Industrial sectors are progressively threatened by the risk associated with flood disasters. They are also increasingly aware that building resilience is critical for their continuity, competitiveness, and survival. However, empirical evidence of industrial resilience to flood disasters is rarely provided, especially for the impact of infrastructure disruptions, resourcefulness, and other socioeconomic factors on industry resilience. Hence, this study presents a parametric semi-Markov recovery model to quantitatively estimate the business recovery rate, which is conditional on initial damage and can evaluate the recovery of industrial sectors to flood disasters. Additionally, the susceptibility of business resilience to inundation depth, deposited sediment, lifeline services (i.e., electricity, water, gas), and transportation were investigated by incorporating these covariates into the recovery function. The proposed model was applied to a case study of the Heavy Rain Event of July 2018 in Japan. The recovery data from 535 individual businesses were analyzed, revealing an estimated average production capacity loss rate of 7.16% and 6.13% in manufacturing and non-manufacturing sectors, respectively. By comparing the PCLR under different damage scenarios, results indicate that inundation depth and lifeline supply status significantly affect total losses. Analyzing the dynamic resilience of firms could help calculate the production capacity losses caused by flood disasters. It can also provide empirical evidence for decision-makers and business managers to allocate reconstruction resources in the aftermath of disasters and to establish business continuity plans to avoid potential losses in the future.