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Exploring the Influence Mechanisms and Spatial Heterogeneity of Urban Vitality Recovery in the University Fringe Areas of Nanjing

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Listed:
  • Zhen Cai

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Dongxu Li

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Binhe Ji

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Huishen Liu

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Shougang Wang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

After the lifting of the COVID-19 pandemic restrictions, urban socio-economic development has been continuously recovering. Researchers’ attention to urban vitality recovery has increased. However, few studies have paid attention to the recovery and driving of urban vitality in university fringe areas. This study aims to address this gap by exploring the driving mechanisms of urban vitality recovery in the university fringe areas using both linear and nonlinear models. The results reveal the following: (1) The recovery of urban vitality in university fringe areas follows a distinct pattern where central urban areas with greater openness recover more rapidly, while university fringe areas farther from the city center with stricter management experience slower recovery. (2) The fitting coefficients of the student enrollment, school area, the density of various POIs, and opening hours are 0.0020, −0.0105, −0.0053, and 0.0041 respectively. These variables exhibit a more pronounced linear relationship, and the significance level is quite high. Recovery effects also express significant spatial heterogeneity. (3) Both university opening hours and school area show a nonlinear positive relationship with the urban vitality recovery of university fringe areas, demonstrating a clear threshold effect. This relationship is characterized by slow growth at lower values, rapid acceleration once a critical threshold is reached, and eventual stabilization at higher values. This study offers targeted strategies for urban planning, fostering more responsive and adaptive urban governance that aligns with the evolving needs of urban development.

Suggested Citation

  • Zhen Cai & Dongxu Li & Binhe Ji & Huishen Liu & Shougang Wang, 2024. "Exploring the Influence Mechanisms and Spatial Heterogeneity of Urban Vitality Recovery in the University Fringe Areas of Nanjing," Sustainability, MDPI, vol. 17(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:223-:d:1557595
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    References listed on IDEAS

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    1. Padhan, Rakesh & Prabheesh, K.P., 2021. "The economics of COVID-19 pandemic: A survey," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 220-237.
    2. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    3. Xiaochen Lin & Hai Long & Yu Chen, 2024. "Looking Back Deeper, Recovering up Better: Resilience-Oriented Contrarian Thinking about COVID-19 Economic Impact," Sustainability, MDPI, vol. 16(15), pages 1-25, August.
    4. Gengying Jiao & Lin Lu & Guangsheng Chen & Zhiqiang Huang & Giuseppe T. Cirella & Xiaozhong Yang, 2021. "Spatiotemporal Characteristics and Influencing Factors of Tourism Revenue in the Yangtze River Delta Urban Agglomeration Region during 2001–2019," Sustainability, MDPI, vol. 13(7), pages 1-14, March.
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