China’s regional social vulnerability to geological disasters: evaluation and spatial characteristics analysis

Jundong Hou, Jun Lv (), Xin Chen and Shiwei Yu ()
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Jundong Hou: China University of Geosciences
Jun Lv: China University of Geosciences
Xin Chen: China University of Geosciences
Shiwei Yu: China University of Geosciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 84, issue 1, No 8, 97-111

Abstract: Abstract The assessment of social vulnerability is a requirement for understanding the risk of natural hazards. This paper calculates the social vulnerability index of geological disasters in China with the super-efficiency DEA (data envelopment analysis) model, carries out global and local autocorrelation tests for social vulnerability to geological disasters in each province in China and identifies the characteristics of its spatial distribution pattern. The results show the following. (1) China’s social vulnerability to geological disasters is relatively high and has obvious differences. It represents the pattern of a significant increase by degrees in social vulnerability to geological disasters from east to west and a significantly negative correlation relationship between the vulnerability level and the economic level. (2) Based on the comparative analysis of the mean values of the indexes and the social vulnerability index of geological disasters, it is found that the social vulnerability index of geological disasters in China is directly related to the regional exposure degree and reaction and recovery ability, among which the reaction and recovery ability has great effects on the social vulnerability index. (3) Most of the regions in China are in a high–high clustering area or a low–low clustering area; that is to say, the regions with similar social vulnerability to geological disasters represent the pattern of clustering.

Keywords: Social vulnerability index; Geological disasters; Super-efficiency DEA; Spatial autocorrelation analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11069-015-1931-3

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