An Improved DPSIR-DEA Assessment Model for Urban Resilience: A Case Study of 105 Large Cities in China

Liudan Jiao (), Bowei Han, Qilin Tan, Yu Zhang, Xiaosen Huo, Liu Wu and Ya Wu
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Liudan Jiao: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Bowei Han: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Qilin Tan: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Yu Zhang: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Xiaosen Huo: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Liu Wu: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Ya Wu: College of Resources and Environment, Southwest University, Chongqing 400715, China

Land, 2024, vol. 13, issue 8, 1-23

Abstract: Urban development is facing increasingly complex disturbances. Assessing large cities’ urban resilience is important for improving their ability to withstand disturbances and promoting sustainable development. Therefore, this paper establishes an improved assessment model for urban resilience based on the driving force–pressure–state–impact–response (DPSIR) and data envelopment analysis (DEA) model. The Malmquist index, Dagum Gini coefficient, and Markov chain were sequentially used for spatiotemporal evolution and differential resilience analysis. Then, 105 large Chinese cities were selected as case studies. The results indicate their overall resilience is relatively high; each year’s average resilience efficiency can achieve DEA effectiveness. The distribution pattern of resilience level presents a healthy olive-shaped structure. However, there is also a significant difference between the two poles. During the research period, the combined effect of technological efficiency improvement and technological progress resulted in the overall resilience slowly improving, and this process was more driven by technological innovation. At the same time, the overall regional difference in resilience also shows a narrowing trend, and the current spatial differences mainly come from the difference within subregions and super-density. In future transfer predictions, the resilience of large cities will show good stability with a higher probability of maintaining stability; if the resilience undergoes a transition, the probability of an increase will be higher than a decrease. Based on the life cycle process of resilience, this study selects indicators that can characterize the level of resilience according to the DPSIR model, which comprehensively reflects the characteristics of urban resilience. This study’s results can provide particular reference values for urban disaster response emergency planning and sustainable development construction, and it also provides new ideas for the assessment research of urban resilience.

Keywords: urban resilience; DPSIR-DEA model; assessment model; Dagum Gini coefficient (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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