Risk mitigation: Numerical analysis of COVID-19's impact on major global construction economies

Abstract

Learning from the past is a key risk mitigation strategy, as it involves analysing previous experiences to identify mistakes or successes. By understanding these lessons, organisations can take proactive measures to address potential risks in the future, minimising both their likelihood and impact. By using high-performance computing, this study combines economic and pandemic predictors to enhance the understanding, modelling and quantification of disaster risks within major global construction industries during COVID-19. By systematically identifying key variables and developing a robust risk index through predictive modelling and empirical validations, the research presents the relative risk index for managing crisis responses and resilience strategies at global and continental levels. The framework used here aligns with the globally recognised disaster risk management initiatives, such as the Sendai Framework, and leverages real-time data and automation to facilitate proactive, data-driven decision-making in the face of global crises. Using the Arellano test specification, the study identified 10 significant explanatory variables pertaining to the productivity of the construction industry. FDI, share prices and total spending had positive regression coefficients. An increment in such explanatory variables is beneficial for the productivity of the construction industry. The analysis of the causal effect found that employment rate, inflation, pandemic growth, reproduction number R and the transmission rate had a negative link with the construction industry productivity. This study remarks that when excluding statistically independent pandemic variables, the model did not obtain valid regressors, emphasising the importance of accounting for both the economic and pandemic data for the causal effect detections.