A Bayesian and Analytic Hierarchy Process-Based Multilevel Community Resilience Evaluation Method and Application Study

Jianfu Lin, Yilin Li, Lixin Wang (), Junfang Wang (), Tianyu Zhang and Weilin Wu
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Jianfu Lin: Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China
Yilin Li: Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China
Lixin Wang: Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China
Junfang Wang: National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Tianyu Zhang: Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Weilin Wu: Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China

Sustainability, 2024, vol. 16, issue 14, 1-45

Abstract: Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the “relative demand coefficient” (RDC) is proposed to compare different communities’ resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method’s superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience.

Keywords: community resilience; multilevel evaluation indicator; Bayesian inference; analytic hierarchy process; application study (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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