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Assessment of Vulnerability Caused by Earthquake Disasters Based on DEA: A Case Study of County-Level Units in Chinese Mainland

Author

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  • Yuxin Gao

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beijing 100191, China)

  • Xianrui Yu

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beijing 100191, China)

  • Menghao Xi

    (School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China)

  • Qiuhong Zhao

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beijing 100191, China)

Abstract

Earthquake activity can generate huge energy in a short period of time, bringing enormous risks to people’s lives and property safety. This poses a great challenge to regional sustainable development. Meanwhile, due to the complex mechanism, seismic activity is difficult to accurately predict. Therefore, it is of great significance to explore how to reduce earthquake disaster losses from the perspective of human society. In this study, we use vulnerability to reflect the relative impact of earthquake disasters on different counties. The vulnerability caused by earthquakes is calculated with the data envelopment analysis (DEA) method. We use CCR and BCC models to further decompose vulnerability into pure technology vulnerability and scale vulnerability. This study analyzes 69 earthquake disasters that occurred in the Chinese mainland from 2013 to 2020 and explores the influencing factors of pure technology vulnerability from both quantitative and qualitative perspectives. Three main conclusions are drawn. First, four factors, including the added value of the secondary industry, gross domestic product (GDP) per capita, investment density of fixed assets and energy released by earthquakes, have a significant impact on the pure technical vulnerability of counties caused by earthquake disasters. Second, in the samples under consideration, the average vulnerability of the regions with an earthquake magnitude below 5.0 is higher than that of the regions with an earthquake magnitude between 5.0 and 6.0. There are deficiencies in organization, management and facilities in regions with a small earthquake risk. Third, through qualitative analysis, it is shown that the seismic function of buildings affects the vulnerability of counties facing earthquake disasters. The results of the research can provide decision makers with new insights into earthquake prevention and disaster reduction management.

Suggested Citation

  • Yuxin Gao & Xianrui Yu & Menghao Xi & Qiuhong Zhao, 2023. "Assessment of Vulnerability Caused by Earthquake Disasters Based on DEA: A Case Study of County-Level Units in Chinese Mainland," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7545-:d:1139441
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    References listed on IDEAS

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    1. Li Peng & Jing Tan & Lei Lin & Dingde Xu, 2019. "Understanding sustainable disaster mitigation of stakeholder engagement: Risk perception, trust in public institutions, and disaster insurance," Sustainable Development, John Wiley & Sons, Ltd., vol. 27(5), pages 885-897, September.
    2. Seth Stein & Mian Liu, 2009. "Long aftershock sequences within continents and implications for earthquake hazard assessment," Nature, Nature, vol. 462(7269), pages 87-89, November.
    3. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    4. Mohsen Alizadeh & Esmaeil Alizadeh & Sara Asadollahpour Kotenaee & Himan Shahabi & Amin Beiranvand Pour & Mahdi Panahi & Baharin Bin Ahmad & Lee Saro, 2018. "Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    5. Blake Walker & Cameron Taylor-Noonan & Alan Tabbernor & T’Brenn McKinnon & Harsimran Bal & Dan Bradley & Nadine Schuurman & John Clague, 2014. "A multi-criteria evaluation model of earthquake vulnerability in Victoria, British Columbia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 1209-1222, November.
    6. Toya, Hideki & Skidmore, Mark, 2007. "Economic development and the impacts of natural disasters," Economics Letters, Elsevier, vol. 94(1), pages 20-25, January.
    7. Mingze Li & Jun Lv & Xin Chen & Nan Jiang, 2015. "Provincial evaluation of vulnerability to geological disaster in China and its influencing factors: a three-stage DEA-based analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1649-1662, December.
    8. Dingde Xu & Wenfeng Zhou & Xin Deng & Zhixing Ma & Zhuolin Yong & Cheng Qin, 2020. "Information credibility, disaster risk perception and evacuation willingness of rural households in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2865-2882, September.
    9. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    10. Jianyi Huang & Yi Liu & Li Ma & Fei Su, 2013. "Methodology for the assessment and classification of regional vulnerability to natural hazards in China: the application of a DEA model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 115-134, January.
    11. Xiaolu Gao & Jue Ji, 2014. "Analysis of the seismic vulnerability and the structural characteristics of houses in Chinese rural areas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1099-1114, January.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    1. Lihui Wu & Da Ma & Jinling Li, 2023. "Assessment of the Regional Vulnerability to Natural Disasters in China Based on DEA Model," Sustainability, MDPI, vol. 15(14), pages 1-12, July.

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