Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

Yu Chen, Mengke Zhu, Qian Zhou and Yurong Qiao
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Yu Chen: School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zheng Zhou 450000, China
Mengke Zhu: School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zheng Zhou 450000, China
Qian Zhou: Economics School, Zhongnan University of Economics and Law, Nanhu Avenue 182, Wuhan 430073, China
Yurong Qiao: School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zheng Zhou 450000, China

IJERPH, 2021, vol. 18, issue 3, 1-26

Abstract: Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

Keywords: urban resilience; spatiotemporal differentiation; MGWR; spatial scale; factors of urban resilience (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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