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Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

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  • Xiaojun Dong

    (School of Economics Teaching & Research, Party School of the Central Committee of C.P.C. (Chinese Academy of Governance), Beijing 100091, China)

  • Tao Shi

    (School of Economics Teaching & Research, Party School of the Central Committee of C.P.C. (Chinese Academy of Governance), Beijing 100091, China
    Economic Institute, Henan Academy of Social Science, Zhengzhou 450002, China)

  • Wei Zhang

    (School of Public Administration, Central China Normal University, Wuhan 430019, Hubei, China)

  • Qian Zhou

    (School of Shanghai Development & Institute of Free Trade Zone, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

Based on the sample data of 81 non-provincial smart cities in China in 2017, the comprehensive evaluation index of the resilience of sample cities is calculated by using the entropy method, and the spatial differences of different factors on the resiliency are analyzed by using the geographical weighted regression (GWR) model. The conclusions are as follows: Firstly, the comprehensive evaluation index of the resilience of smart cities presents a spatial distribution characteristic of decreasing from the east to the west. At the same time, the resilience comprehensive index, the public infrastructure resilience capacity index, the economic development resilience index, the social security resilience index, and the ecological environment resilience index of smart cities have obvious agglomeration effects on their geographical spaces. Secondly, the public infrastructure resilience capacity index and the ecological environment resilience index are both low with a discrete distribution, while the economic development resilience index and the social security resilience index are both high with a concentrated distribution. Thirdly, different factors have significantly positive effects on the resilience of smart cities. In particular, the public infrastructure capacity resilience index decreases from the north to the south with the spatial distribution pattern of concentration, the economic development resilience index and the ecological environment resilience index of smart cities decrease from the east to the west with a concentrated spatial distribution pattern, and the social security resilience index of smart cities decreases from the southwest to the northeast with a concentrated spatial distribution pattern. Therefore, it is necessary to enhance awareness of smart cities, strengthen the driving force of science and technology innovation, strengthen public infrastructure and service construction, and continuously improve the rapid resilience of smart cities.

Suggested Citation

  • Xiaojun Dong & Tao Shi & Wei Zhang & Qian Zhou, 2020. "Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1321-:d:319460
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    3. Pengpeng Chang & Xueru Pang & Xiong He & Yiting Zhu & Chunshan Zhou, 2022. "Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China," Sustainability, MDPI, vol. 14(12), pages 1-22, June.

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