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Emergency Capacity of Small Towns to Endure Sudden Environmental Pollution Accidents: Construction and Application of an Evaluation Model

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  • Delu Wang

    (International College of Applied Technology, Yibin University, Yibin 644000, China
    School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Yadong Wang

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Sudden environmental pollution accidents (SEPAs) in small towns are characterized by high uncertainty, complex evolution, and fast spread speed, and they cause serious harm to a wide geographic range. Thus, SEPAs greatly challenge the emergency management systems of enterprises and governments. Therefore, improving the emergency capacity of small towns (ECST) to withstand SEPAs deserves more attention. In this study, the evolution mechanism of SEPAs is systematically analyzed, revealing the interactions among various situational elements in the SEPA occurrence process. Then, an evaluation index system of the ECST response to SEPAs is constructed based on four dimensions: monitoring and early warning capacity, preparedness and mitigation capacity, response, and recovery capacity. The system includes 68 indicators and covers the key stages of the SEPA life cycle. Finally, an evaluation model of the ECST to SEPAs is proposed based on the analytic network process method, and the small town of Jiangyin City is selected as a case study for empirical evaluation. The proposed evaluation model considers the interactions and interdependent feedback between indexes, effectively improving the accuracy and scientific nature of the evaluation results. Thus, this model provides a solid decision-making reference for governments and a quantitative theoretical basis for the formulation of measures targeted at SEPAs.

Suggested Citation

  • Delu Wang & Yadong Wang, 2021. "Emergency Capacity of Small Towns to Endure Sudden Environmental Pollution Accidents: Construction and Application of an Evaluation Model," Sustainability, MDPI, vol. 13(10), pages 1-34, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5511-:d:554848
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    References listed on IDEAS

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    4. Ao Yu & Zhuoqiang Jia & Weike Zhang & Ke Deng & Francisco Herrera, 2020. "A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    5. Antonio Nesticò & Pierfrancesco Fiore & Emanuela D’Andria, 2020. "Enhancement of Small Towns in Inland Areas. A Novel Indicators Dataset to Evaluate Sustainable Plans," Sustainability, MDPI, vol. 12(16), pages 1-21, August.
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    Cited by:

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    2. Shi, Lingyuan & Yang, Xin & Chang, Ximing & Wu, Jianjun & Sun, Huijun, 2023. "An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

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