Regional resilience evaluation methods and influencing factors from perspective of natural disasters

doi:10.16265/j.cnki.issn1003-3033.2023.05.1892

Abstract

Abstract:

In order to evaluate regional resilience and influencing factors of different regions facing the impact of natural disasters, regional resilience was comprehensively reflected by the vulnerability and adaptability of the region in the impact of natural disasters, and a framework was constructed to understand and quantify regional resilience. The SBM-DEA method was applied to measure regional resilience levels of 27 provinces in China from 2011 to 2020, and the spatial effects of social, economic, environmental and other multidimensional influencing factors on regional resilience were examined in conjunction with the spatial Durbin model. The results show that: the regional resilience level in China from the perspective of natural disasters shows an overall pattern of low in the north and high in the south, with large differences between regions and relatively small differences in years. There is a trend of gradual expansion of the magnitude and frequency of changes and the regional resilience performance is not strongly correlated with the level of socio-economic development. In the spatial econometric regression, various influencing factors, such as education level, medical resources, social security, industrial structure and regional resilience, form a significant spatial effect relationship with regional resilience.

Key words: natural disasters, regional resilience, evaluation method, influencing factors, slacks based model-data envelopment analysis(SBM-DEA), spatial econometrics

LIU Qiyuan, LIU Jincheng. Regional resilience evaluation methods and influencing factors from perspective of natural disasters[J]. China Safety Science Journal, 2023, 33(5): 174-181.

Figures/Tables 5

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References 17

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指标类别	指标	变量
投入变量	风险暴露	各省(区、市)自然灾害暴露比例/%
	风险暴露	各省(区、市)自然灾害冲击频次/次
	劳动力	各省(区、市)就业人员数量/万人
	资本	各省(区、市)固定资本存量/亿元
期望产出	经济发展	各省(区、市)GDP/亿元
非期望产出	灾害损失	各省(区、市)受灾人口/万人
非期望产出	灾害损失	各省(区、市)直接经济损失/亿元

维度	影响因素	变量设定
社会	社会网络	户均人口数量/人
	教育程度	高等教育人才占人口比例/%
	生活压力	老幼人群抚养比/%
	弱势群体	残疾人占人口比例/%
	医疗资源	每千人卫生技术人员数量/人
	社会保障	人均财政社会保障支出/元
经济	收入稳定	公有制单位人员占人口比例/%
	就业形势	城镇失业登记率/%
	产业结构	产业多样性
	物资供应	每万人零售门店数量/个
	复苏潜力	人均保险投入额/元
基础设施	关键设施	城市供水普及率/%
	交通运输	日均客运量/万人
	通讯带宽	每平方公里移动通讯基站数/个
环境	自然生态	森林覆盖率/%
环境	污染排放	工业固体废物综合利用率/%

维度	影响因素	直接效应	间接效应	总体效应
社会	社会网络	0.079	-0.437	-0.359
	社会网络	(0.456)	(0.141)	(0.286)
	教育程度	0.020^*	-0.006	0.014
	教育程度	(0.061)	(0.755)	(0.536)
	生活压力	-0.011	-0.019	-0.031^*
	生活压力	(0.226)	(0.114)	(0.076)
	弱势群体	-0.146^***	0.121	-0.026
	弱势群体	(0.008)	(0.491)	(0.892)
	医疗资源	0.017	0.112^*	0.129^**
	医疗资源	(0.568)	(0.068)	(0.015)
	社会保障	0.188^***	-0.387^***	-0.200^***
	社会保障	(0.000)	(0.000)	(0.002)
维度	影响因素	直接效应	间接效应	总体效应
经济	收入稳定	0.014	-0.034	-0.020
	收入稳定	(0.263)	(0.203)	(0.519)
	就业形势	-0.026	-0.081	-0.107
	就业形势	(0.564)	(0.401)	(0.364)
	产业结构	0.016^***	-0.020	-0.004
	产业结构	(0.006)	(0.195)	(0.785)
	物资供应	0.086^**	0.136	0.222^*
	物资供应	(0.038)	(0.163)	(0.074)
	复苏潜力	-0.091^***	0.173^***	0.082^**
	复苏潜力	(0.001)	(0.002)	(0.050)
基础设施	关键设施	0.107^**	0.016	0.123^**
	关键设施	(0.044)	(0.384)	(0.039)
	交通运输	0.059^**	-0.094^**	-0.035
	交通运输	(0.022)	(0.026)	(0.415)
	通讯带宽	0.003^*	0.003^***	0.006^***
	通讯带宽	(0.933)	(0.905)	(0.912)
环境	自然生态	0.043^***	0.032	0.075^***
	自然生态	(0.003)	(0.288)	(0.003)
	污染排放	0.002	0.004	0.006
	污染排放	(0.820)	(0.895)	(0.871)
样本量		270	270	270
R²		0.017	0.017	0.017