Abstract:

Assessing the economic losses induced by disasters in complex industrial networks is fundamental for understanding the vulnerability and potential risks in industrial networks, and for making decisions on pre-disaster risk reduction and post-disaster reconstruction. From the perspective of disaster impact propagation in industrial networks, we systematically reviewed the types of disaster-induced economic losses and the remarkable research advances in recent years. The main method used in asset damage assessment is to establish the fragility curves between hazard intensity and damage degree of assets such as buildings, equipment, and inventory. For business interruption loss assessment, more research should be focused on production capacity loss rate (PCLR) of industrial sectors resulting from the physical damages of various input factors. Two distinctive analysis scales are often adopted for ripple loss assessment in industrial networks: micro-scale supply chain network and macro-scale industrial chain network. The former takes individual firms as basic units of analysis and utilizes complex network analysis to model the disaster impact spreading through inter-firm transactions and estimate the resulting supply chain disruption losses. In contrast, the latter considers industrial sectors or administrative regions as basic units and employs macro-scale econometric models, such as Input-Output (IO), Computable General Equilibrium (CGE), and so on, to simulate disaster impact propagation among industrial sectors or administrative regions, and evaluate the resulting gross output losses. Finally, from the perspective of scenario analysis, we proposed a multi-process, multi-scale integrated assessment methodology that combines hazard scenario, industrial network exposure, asset damages and business interruption losses at local nodes, supply chain disruption losses, and gross output losses, to comprehensively simulate the amplification effect of disasters in industrial networks.

Key words: industrial network, disaster, direct losses, indirect losses, research progress