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Analysis of Factors Influencing the Urban Carrying Capacity of the Shanghai Metropolis Based on a Multiscale Geographically Weighted Regression (MGWR) Model

Author

Listed:
  • Xiangyang Cao

    (School of Civil Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Yishao Shi

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China)

  • Liangliang Zhou

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China)

  • Tianhui Tao

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China)

  • Qianqian Yang

    (College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China)

Abstract

With the rapid development of urban economy and the continuous expansion of urban scale, the limitations of urban carrying capacity begin to appear. For the sustainable development of the city, more and more scholars are paying attention to the research onurban carrying capacity. Basedon the continuous research of the authors’ research group over the past ten years, this paper uses a multiscale geographically weighted regression model and method to explore the impact of geographical location, floor area ratio, public transportation, residents’ consumption level, the density of high-tech enterprises, and the ecological environment on the carrying capacity of the Shanghai metropolis. The results show that (1) the impact of geographical location on the bearing capacity decreases from downtown to the outer areas and from the northeastern area to the southwestern area of Shanghai. (2) On the whole, the elasticity of the average floor area ratio to the urban carrying capacity is 0.52%. In different regions, most of the central urban areas have exceeded the optimal average plot ratio. With an increase in the average plot ratio, the urban carrying capacity presents a downward trend. Other sample areas generally did not reach the average optimal plot ratio, especially the southwestern area of Shanghai. With an increase in the average plot ratio, the urban carrying capacity of this area improved significantly. (3) The elasticity of public transportation convenience to the urban carrying capacity is 0.23%; that is, the average increase in the urban carrying capacity is 0.23% for every 1% increase in public transportation convenience. The elasticity of residents’ consumption level is −0.18%; in other words, every 1% increase in residents’ consumption level will reduce the urban carrying capacity by 0.18% on average. The elasticity of the density of high-tech enterprises is 0.08%; hence, when the density of high-tech enterprises increases by 1%, the urban carrying capacity increases by 0.08% on average. Lastly, the elasticity of the eco-environmental status index is 0.17%; that is, every 1% increase in the eco-environmental status index increases the urban carrying capacity by 0.17% on average.

Suggested Citation

  • Xiangyang Cao & Yishao Shi & Liangliang Zhou & Tianhui Tao & Qianqian Yang, 2021. "Analysis of Factors Influencing the Urban Carrying Capacity of the Shanghai Metropolis Based on a Multiscale Geographically Weighted Regression (MGWR) Model," Land, MDPI, vol. 10(6), pages 1-19, May.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:578-:d:565517
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    References listed on IDEAS

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    2. Yao Wang & Meilin Zhu & Xiaodong Meng, 2023. "Spatial impacts of the intercity flow of long stay international visitors based on mobile phone data in Yangtze River Delta, China," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-27, September.
    3. Shih, Hsin-Cheng Ivan & Wijaya, Kenny Chandra & Hsueh, Hung-Yi & Hsu, Yu-Ting, 2025. "Away from over-populated development: Estimating urban carrying capacity with an integrated perspective of transportation and urban planning," Transport Policy, Elsevier, vol. 162(C), pages 283-295.
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    5. Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    6. Guo, Qing & Sun, Hongrui, 2025. "Spatial distribution of Chinese urban residents' electricity consumption and its driving factors—An empirical study based on the MGWR model," Energy, Elsevier, vol. 319(C).

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