In recent years, accelerated global warming, rainstorms, typhoons, and other natural disasters have been frequently observed, bringing immeasurable direct and indirect economic losses to urban areas. Determining how to further enhance the resilience of urban areas has become an important topic in economic and social development. Therefore, based on waterlogging scenarios, this study uses a more accurate research method combining the subjective evaluation AHP (analytic hierarchy process) method and objective evaluation TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to evaluate the urban resilience of 16 districts of the Shanghai megacity as the research objects and divides the resilience grade results using the ArcGIS natural breakpoint method. The results show that (1) the overall resilience of all districts in Shanghai needs to be further improved. Among the 16 districts in Shanghai, Pudong New Area has the highest urban resilience level. There are more areas with moderate and above-moderate resilience levels, while some areas with low and moderate resilience levels are distributed mainly in the downtown area of Shanghai. (2) Through the analysis of obstacles to the development of urban resilience in the districts of Shanghai, such obstacles tend to be the same under the waterlogging disaster scenarios. Compared to ecological and social policy resilience indices, economic resilience indices and infrastructure resilience indices significantly impact the resilience of urban districts under waterlogging scenarios. The above conclusions can not only help improve the direction of urban resilience governance in various districts of Shanghai but also provide empirical theoretical experience for the resilient construction of urban areas in the future.
CITATION STYLE
Chen, X., Jiang, S., Xu, L., Xu, H., & Guan, N. (2023). Resilience assessment and obstacle factor analysis of urban areas facing waterlogging disasters: a case study of Shanghai, China. Environmental Science and Pollution Research, 30(24), 65455–65469. https://doi.org/10.1007/s11356-023-26861-1
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