Assessing Ripple Effects of Production Capacity Loss from Compound Hazards: A Case Study of Flood and COVID-19 in Enshi, Hubei Province

Abstract

Understanding the impacts of compound hazards on economic systems is essential for integrated disaster risk management. However, quantifying the ripple effects remains challenging, especially as the relative contributions of each hazard are unclear. Compared to the uncertainty of demand-driven impacts, supply-side shocks are more pronounced and quantifiable, providing a practical pathway to separate the effects. This study proposed a supply-side methodological framework for assessing the ripple effects of compound hazards, focusing on the contributions of each hazard to overall economic disruptions. Overall production capacity loss rates (PCLRs) across industries were evaluated using on-site survey data. A time series model, utilizing urban travel intensity big data, was used to estimate the PCLRs attributable to the COVID-19 pandemic. A mixed multi-regional input–output (mixed-MRIO) model was constructed to assess the ripple effects. The framework was applied to the 17 July 2020 flood in Enshi City, Hubei Province, China, during the COVID-19 pandemic. The results indicate that overall ripple effects exceeded direct production capacity losses. The ratio of production capacity losses attributed to the flood versus the pandemic was approximately 6:4, but it increased to 7:3 for ripple effects. Economic core cities exhibited greater economic stability as evidenced by lower loss rates, while more dependent small- and medium-sized cities were more vulnerable. The secondary industry was sensitive to floods, and the tertiary industry to the pandemic. The study highlights the importance of integrating field surveys and travel intensity big data with economic modeling to evaluate ripple effects, offering new insights into compound hazards impact assessment and management strategies.