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

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.

A Study on the Inner Logic of Local Government Emergency Capacity Indicators Based on the G-Dematel-Aism Model

“…Figure 2 illustrates the cause-result diagram of the indicator distribution, as in Figure 2. The reachable matrix R was calculated based on the comprehensive influence matrix T and Equations ( 11) and (12). The reachable matrix was then extracted hierarchically in two ways (UP type and DOWN type) to finally form the hierarchical structure in Table 3.…”

“…Since the indicators in the evaluation system are coupled to a certain degree and may have mutual influence on each other, it is necessary to conduct further research on the intrinsic relationships of emergency response capability indicators. Some scholars have also studied the intrinsic relationship among indicators in depth, such as D. L. Wang proposed an ECST model based on the analytical network process approach and chose the small towns in Jiangyin City as a case study to explore the interactions and interdependent feedback among indicators [12]. Qian Zheng et al combined the gray DEMATEL method with the hierarchical analysis to study the risk of the 7.20 Zhengzhou rainstorm, which reduces the uncertainty of expert opinions and subjective decisions when studying the indicators, and helps understand the magnitude of the influence degree of each indicator in the system [13].…”

Research on Integrated Scheduling of Multi-Mode Emergency Rescue for Flooding in Chemical Parks

“…Liu et al [13] proposed a PCA-BP neural network-based emergency rescue capability assessment model to evaluate the professional capability of chemical professional emergency rescue teams. Wang et al [14] constructed an evaluation model for the emergency response capacity of environmental pollution accidents with a small town as the research object.…”

A study on the internal logic of local government emergency response capacity indicators based on the G-Dematel-Aism model

“…Since the indicators in the evaluation system are coupled to a certain degree and may have mutual influence on each other, it is necessary to conduct further research on the intrinsic relationships of emergency response capability indicators. Some scholars have also studied the intrinsic relationship among indicators in depth, such as D. L. Wang proposed an ECST model to explore the interactions and interdependent feedback among indicators [12]. Qian Zheng et al combined the gray DEMATEL method with the hierarchical analysis to study the risk of the 7.20 Zhengzhou rainstorm, which reduces the uncertainty of expert opinions and subjective decisions when studying the indicators, and helps understand the magnitude of the influence degree of each indicator in the system [13].…”