Measuring Social Vulnerability to Flood Disasters in China

Abstract

Abstract:To proactively prevent losses from flood disasters and subsequent potential human conflicts, it is critical to measure the social vulnerability of a country or a region to flood. In this article, we first propose a list of potential indicators for measuring this social vulnerability. These indicators' significances are then tested based on their correlation coefficients with a vulnerability index obtained using nonparametric Data Envelopment Analysis. In the final measurement system, there are nine indicators: the proportion of the primary industry, infrastructure development level, income gap between urban and rural residents, the proportion of population over 60 years old, the proportion of children under 14 years old, the number of people receiving minimum income assistance, and the number of disasters per year. We then conduct principal component analysis to evaluate the social vulnerability level. Our results show that the social vulnerability level is mostly impacted by the economic principal component and the demographic and social security principal component. Moreover, our results also confirm that the social vulnerability level to flood in China declined overall from 2003 to 2015.

Flood Vulnerability Analysis in Urban Context: A Socioeconomic Sub-Indicators Overview

“…The results of this work can be used in future applications as is, or else, researchers could apply the proposed methodology to other natural hazards. [27,35,52,53] Ratio between taxable income and taxpayers in each municipality [39] Fixed investment per inhabitants [71,106] Ratio of investment over the total GDP/revenue-expenditure ratio [47,57] Replacement cost for dwellings located at flood prone area [ Reserve and distribution capacity of flood control materials [45] Emergency rescue capacity of public administration [45] Hospital beds [47,71,100] Medical staff [47,69,71,105] Reception centers [69] Investment in coping capacity [27,64] Preventive measures Land use regulation [60,89] Flood control standards/plans [39,43,94] Hydraulic infrastructures [39] Protective infrastructures Dikes/levees [27,57,66,100] Drainage network/pipelines density [35,43,45,47,52,53,57,68,70,92,94,96] Dams storage capacity/reser...…”

Evaluation of natural disaster treatment efficiency in 27 Chinese provinces

“…Sharma et al (2018) reviewed the flood disaster research in Nepal and found that Nepalʼs disaster prevention technology needs to be improved. Wang and Zhang (2018) studied disaster‐adapted investments at two ports competing for shippers and found that the probability of large disasters promoted port adaptations, that competition between the ports led to more adaptive investments (“competitive effects”), but that a high degree of change in disaster probability hindered port adaptation. Lam and Kuipers (2019) studied the 2015 Nepalese earthquake recovery and found that the entire reconstruction process had low community involvement and excluded vulnerable groups.…”

“…In other recent studies, Shi (2018) studied agricultural flood vulnerability changes in Shanghai suburbs using a CCR DEA model, the results from which provided a scientific decision‐making basis. Zhang et al (2018) used non‐parametric DEA to evaluate China's social vulnerability to floods and found that social vulnerability was primarily affected by the economy, the population, and social security, and to assist governments with disaster risk reduction decisions. The study also took the Yongkang district in Tainan, Taiwan as an example.…”

“…There have also been many studies focused on natural disaster prevention and treatment, management efficiency, risk control, and assessments, most of which employed Super‐DEA (data envelopment analysis) or CCR (Charnes, Cooper, and Rhodes model) DEA models (Cheng & Chang, 2018; Kawamoto & Kim, 2016; Li, Hu, Hu, He, & Tu, 2019; Liu, Wei, & Xu, 2018; Xiang, Wang, & Deng, 2017; Yu, Chen, & Li, 2019; Zhang, Xiang, Chen, & Mao, 2018). However, as neither of these models allow for the input and output proportions to independently change, they are incompatible with reality.…”

An Integrated Approach for Urban Pluvial Flood Risk Assessment at Catchment Level

“…According to Turner et al [29], vulnerability refers to the degree to which a system is likely to experience harm due to exposure to a hazard. Numerous studies have highlighted that the flood vulnerability is multidimensional [4,5,[30][31][32][33] and could be classified into several categories, including physical vulnerability [34][35][36][37], social vulnerability [12,28,36,38,39], economic vulnerability [30,35], and environmental vulnerability [40]. Commonly used vulnerability assessment methods include principal component analysis (PCA), which enables scientists to identify underlying components from a wide range of vulnerability indicators [9,38,41], and multi-criteria analysis method in which several key indicators are selected and weighted to derive a composite vulnerability index [23,42].…”