Research on the evaluation index system of urban resilience from the perspective of SDGS

In the context of confronting urban risk crises, the evaluation of urban resilience holds utmost significance. This study, based on the United Nations 2030 Agenda for Sustainable Development Goals (UN-SDGs), employs the grey correlation analysis method to construct an urban resilience evaluation index system. Firstly, it deeply analyzes the influencing factors of urban resilience in six dimensions, namely economy, society, environment, culture, governance, and infrastructure, and establishes the corresponding construction and evaluation objectives. Subsequently, according to the connotation of a resilient city, a preliminary selection and classification of SDGs indicators is carried out, obtaining 131 indicators, and multiple experts in relevant fields are invited to score these indicators. The improved generalized grey correlation analysis method was used to screen the indicators. Considering the data availability comprehensively, 72 indicators were initially selected to construct the system. The analysis shows that the grey correlation degrees of indicators in different dimensions reflect the differences in the influence degrees of various factors on urban resilience. The high-correlation-degree indicators incorporated in each dimension are conducive to comprehensively considering urban resilience, providing a scientific and reasonable reference for its construction and evaluation, helping cities to understand their own resilience status and enhance their ability to cope with risks, and having both theoretical and practical significance for achieving the global sustainable development goals.

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Published
2024-12-27
DOI
https://doi.org/10.1145/3718491.3718526
Primary Topic
Regional Development and Environment
Type
article
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Research on the evaluation index system of urban resilience from the perspective of SDGS

Maomao Yan, Tingxin Qin, Fengjiao Xu, Xiangcheng Meng et al.
Regional Development and Environment
article

Research on the evaluation index system of urban resilience from the perspective of SDGS

Maomao Yan, Tingxin Qin, Fengjiao Xu, Xiangcheng Meng, Wan Wang
article en

Abstract

In the context of confronting urban risk crises, the evaluation of urban resilience holds utmost significance. This study, based on the United Nations 2030 Agenda for Sustainable Development Goals (UN-SDGs), employs the grey correlation analysis method to construct an urban resilience evaluation index system. Firstly, it deeply analyzes the influencing factors of urban resilience in six dimensions, namely economy, society, environment, culture, governance, and infrastructure, and establishes the corresponding construction and evaluation objectives. Subsequently, according to the connotation of a resilient city, a preliminary selection and classification of SDGs indicators is carried out, obtaining 131 indicators, and multiple experts in relevant fields are invited to score these indicators. The improved generalized grey correlation analysis method was used to screen the indicators. Considering the data availability comprehensively, 72 indicators were initially selected to construct the system. The analysis shows that the grey correlation degrees of indicators in different dimensions reflect the differences in the influence degrees of various factors on urban resilience. The high-correlation-degree indicators incorporated in each dimension are conducive to comprehensively considering urban resilience, providing a scientific and reasonable reference for its construction and evaluation, helping cities to understand their own resilience status and enhance their ability to cope with risks, and having both theoretical and practical significance for achieving the global sustainable development goals.

China National Institute of Standardization (CN)
Sustainable cities and communities, Climate action
Openalex Percentile: Top 54%
Regional Development and Environment
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Research on the evaluation index system of urban resilience from the perspective of SDGS — Maomao Yan, Tingxin Qin, et al. · (2024) | TGRS Research Map | TGRS