Social vulnerability assessment for an industrial city in Natech accidents: A Bayesian network approach

Abstract

ABSTRACT China is a large industrial country where tropical meteorological disasters occur frequently. Therefore, natural-technological (Natech) risk cannot be ignored. Assessing the social vulnerability of an industrial city prone to tropical meteorological disaster-induced Natechs is urgent. To analyze the social vulnerability of such cities, we propose a Bayesian network (BN)-based method to model the social vulnerability framework. Natech is characterised by high-consequence and low-probability. The industrial cities in Southeast China are selected as a case study. The Monte Carlo method simulates the data generated in industrial cities suffering from tropical disaster-induced Natechs, and the conditional probability tables of BN descendant nodes are obtained by the expert scoring method. After sensitivity analysis, we conclude that the ‘catastrophic index of tropical cyclones,’ ‘population density,’ and ‘prevention capacity’ have important impacts on social vulnerability. Human traits, the social environment, and the economy play important roles in social vulnerability assessments. Therefore, reducing the catastrophic index of tropical cyclones and population density and strengthening prevention capacity management measures are necessary. Some suggestions obtained after sensitivity analysis can assist governments in improving disaster prevention and mitigation abilities and formulating urban planning policies for sustainable development. Highlights Industry city’s vulnerability analysis for Natural-technological (Natech) accidents A social vulnerability assessment framework for sustainable development A Bayesian network combined with the Monte Carlo simulation data and expert judgment Risk management for high-consequence and low-probability events