JOURNAL ARTICLE
Urban resilience governance mechanism: Insights from COVID‐19 prevention and control in 30 Chinese cities.
Published In: Risk Analysis: An International Journal, 2025, v. 45, n. 1. P. 40 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Xia, Cao; Delei, Wang 3 of 3
Abstract
Due to the pervasive uncertainty in human society, super large and megacities are increasingly prone to becoming high‐risk areas. However, the construction of urban resilience in this new era lacks sufficient research on the core conditions and complex interactive mechanisms governing it. Hence, this study proposes a specialized event‐oriented framework for governing urban resilience in China based on the pressure‐state‐response (PSR) theory. We examined COVID‐19 cases in 30 cities across China and analyzed the distribution of prevention and control achievements between high‐level and non‐high‐level conditions. Our findings reveal the following key points: (1) High‐level achievements in COVID‐19 prevention and control rely on three condition configurations: non‐pressure‐responsive type, pressure‐state type, and pressure‐responsive type. (2) High economic resilience may indicate a robust state of urban systems amid demographic pressures. In cities experiencing fewer event pressure factors, the application of digital technology plays a crucial role in daily urban management. (3) The implementation of flexible policies proves beneficial in mitigating the impact of objective pressure conditions, such as environmental factors, on urban resilience. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Risk Analysis: An International Journal. 2025/01, Vol. 45, Issue 1, p40
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0272-4332
- DOI:10.1111/risa.14615
- Accession Number:183859341
- Copyright Statement:Copyright of Risk Analysis: An International Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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