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How Does the Spatial Structure of Urban Agglomerations Affect the Spatiotemporal Evolution of Population Aging?

Author

Listed:
  • Miao Fu

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Lucang Wang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
    Key Laboratory of Resource Environment and Sustainable Development of Oasis, Lanzhou 730070, China)

  • Qianguo Li

    (College of Agricultural and Forestry Economics and Management, Lanzhou University of Finance and Economics, Lanzhou 730020, China)

Abstract

China has fully become an aging society, and the scientific response to population aging has become a major task that the country must face in the future. Research on population aging in the Chengdu-Chongqing urban agglomeration (CCUA) can provide a scientific basis for future population management in the CCUA. This paper applies spatial autocorrelation, geodetection, and other methods to analyze the temporal and spatial pattern of population aging and its driving factors in the CCUA from 2000 to 2020, taking districts (counties) as the basic unit and combining them with the spatial structure of the urban agglomeration. The results show that: ① in the time dimension, the population aging in CCUA has gone through the evolution process of “mild–moderate–heavy”; in the spatial dimension, the influence of the urban agglomeration’s development planning axes on the spatial differentiation of the aging population has become more and more prominent. ② The aging level has a strong spatial correlation, and with time, the spatial correlation has changed from weak to strong, and the spatial difference has increased. The dual core city shows a typical spatial pattern of a decreasing aging level in the core area and an increasing aging level in the peripheral area, and the heavily aging area is spreading along the axis. ③ The overall aging speed is high, and the aging speeds of the core cities and node cities are lower than those of other regions. There is a clearer positive correlation between the aging level and the speed of aging, showing the characteristic of “the older the faster”. ④ Endogenous factors such as the aging level and fertility level at the beginning of the period have a significant determining power on the change in the aging level, while exogenous factors such as the in-migration rate and the out-migration rate have a persistent determining power on the urban agglomerations and key areas (core cities, central cities, main axes of development, city belts, and dense urban areas).

Suggested Citation

  • Miao Fu & Lucang Wang & Qianguo Li, 2024. "How Does the Spatial Structure of Urban Agglomerations Affect the Spatiotemporal Evolution of Population Aging?," Sustainability, MDPI, vol. 16(9), pages 1-24, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3710-:d:1385282
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    References listed on IDEAS

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    1. Cao, Huoqing & Chen, Shiyi & Xi, Xican, 2023. "Aging, migration, and structural transformation in China," Economic Modelling, Elsevier, vol. 126(C).
    2. Joost Van Hoof & Jan K. Kazak & Jolanta M. Perek-Białas & Sebastiaan T. M. Peek, 2018. "The Challenges of Urban Ageing: Making Cities Age-Friendly in Europe," IJERPH, MDPI, vol. 15(11), pages 1-17, November.
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    Cited by:

    1. Guangzhi Qi & Zhibao Wang & Chengxin Wang, 2025. "Towards Demographic Sustainability: Multi-Scale Spatiotemporal Evolution and Factors of Population Aging in the Bohai Rim Region," Sustainability, MDPI, vol. 17(4), pages 1-26, February.
    2. Jingyuan Sun & Jinchuan Huang & Xiujuan Jiang & Xinlan Song & Nan Zhang, 2025. "Spatiotemporal Evolution and Influencing Factors of Population Aging in the Triangle of Central China at Multiple Scales," Sustainability, MDPI, vol. 17(14), pages 1-28, July.

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