IDEAS home Printed from https://ideas.repec.org/a/bba/j00009/v3y2024i1p1-16d295.html
   My bibliography  Save this article

Spatiotemporal pattern evolution and influencing factors of population spatial distribution in Changsha-Zhuzhou-Xiangtan urban agglomeration, China

Author

Listed:
  • Weiping Wu

    (School of Economy & Trade, Hunan University of Technology and Business, Changsha, 410205, China)

  • Wenhua Xie

    (School of Economy & Trade, Hunan University of Technology and Business, Changsha, 410205, China)

  • Yuwei Sun

    (School of Economy & Trade, Hunan University of Technology and Business, Changsha, 410205, China)

Abstract

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.

Suggested Citation

  • Weiping Wu & Wenhua Xie & Yuwei Sun, 2024. "Spatiotemporal pattern evolution and influencing factors of population spatial distribution in Changsha-Zhuzhou-Xiangtan urban agglomeration, China," Journal of Regional Economics, Anser Press, vol. 3(1), pages 1-16, January.
    • Handle: RePEc:bba:j00009:v:3:y:2024:i:1:p:1-16:d:295
    as

    Download full text from publisher

    File URL: https://www.anserpress.org/journal/jre/3/1/12/pdf
    Download Restriction: no
    File URL: https://www.anserpress.org/journal/jre/3/1/12
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Glaeser, Edward L. & Kahn, Matthew E., 2010. "The greenness of cities: Carbon dioxide emissions and urban development," Journal of Urban Economics, Elsevier, vol. 67(3), pages 404-418, May.
    2. Ouyang, Xiao & Xu, Jun & Li, Jiayu & Wei, Xiao & Li, Yonghui, 2022. "Land space optimization of urban-agriculture-ecological functions in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China," Land Use Policy, Elsevier, vol. 117(C).
    3. Wu, Wei-ping & Chen, Zi-gui & Yang, Dong-xiao, 2020. "Do internal migrants crowd out employment opportunities for urban locals in China?—Reexamining under the skill stratification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun Fu & Rui Ding & Yilin Zhang & Tao Zhou & Yiming Du & Yuqi Zhu & Linyu Du & Lina Peng & Jian Zou & Wenqian Xiao, 2022. "The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization," IJERPH, MDPI, vol. 19(23), pages 1-25, December.
    2. Chen, Zeyu & Tang, Yuhong & Shen, Hebin & Liu, Jiali & Hu, Zheng, 2024. "Threshold effects of Government digital development and land resource disparity on Urban carbon efficiency in China," Resources Policy, Elsevier, vol. 94(C).
    3. Matthew J. Holian & Matthew E. Kahn, 2014. "Household Demand for Low Carbon Public Policies: Evidence from California," NBER Working Papers 19965, National Bureau of Economic Research, Inc.
    4. Proost, Stef & Van Dender, Kurt, 2012. "Energy and environment challenges in the transport sector," Economics of Transportation, Elsevier, vol. 1(1), pages 77-87.
    5. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.
    6. Carozzi, Felipe & Roth, Sefi, 2023. "Dirty density: Air quality and the density of American cities," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    7. Schünemann, Johannes & Trimborn, Timo, 2023. "Boosting taxes for boasting about houses? Status concerns in the housing market," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 120-143.
    8. Nicholas Z Muller & Akshaya Jha, 2017. "Does environmental policy affect scaling laws between population and pollution? Evidence from American metropolitan areas," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    9. Julie Anne Cronin & Don Fullerton & Steven Sexton, 2019. "Vertical and Horizontal Redistributions from a Carbon Tax and Rebate," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 6(S1), pages 169-208.
    10. Long Qian & Yunjie Zhou & Ying Sun, 2023. "Regional Differences, Distribution Dynamics, and Convergence of the Green Total Factor Productivity of China’s Cities under the Dual Carbon Targets," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    11. Morikawa, Masayuki, 2012. "Population density and efficiency in energy consumption: An empirical analysis of service establishments," Energy Economics, Elsevier, vol. 34(5), pages 1617-1622.
    12. Attahiru, Yusuf Babangida & Aziz, Md. Maniruzzaman A. & Kassim, Khairul Anuar & Shahid, Shamsuddin & Wan Abu Bakar, Wan Azelee & NSashruddin, Thanwa Filza & Rahman, Farahiyah Abdul & Ahamed, Mohd Imra, 2019. "A review on green economy and development of green roads and highways using carbon neutral materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 600-613.
    13. Chegut, Andrea & Eichholtz, Piet & Kok, Nils, 2019. "The price of innovation: An analysis of the marginal cost of green buildings," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    14. Gudipudi, Ramana & Rybski, Diego & Lüdeke, Matthias K.B. & Zhou, Bin & Liu, Zhu & Kropp, Jürgen P., 2019. "The efficient, the intensive, and the productive: Insights from urban Kaya scaling," Applied Energy, Elsevier, vol. 236(C), pages 155-162.
    15. Miotti, Marco & Needell, Zachary A. & Jain, Rishee K., 2023. "The impact of urban form on daily mobility demand and energy use: Evidence from the United States," Applied Energy, Elsevier, vol. 339(C).
    16. Guangyang Chen & Kai Dong & Shaonan Wang & Xiuli Du & Ronghua Zhou & Zhongwei Yang, 2022. "The Dynamic Relationship among Bank Credit, House Prices and Carbon Dioxide Emissions in China," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    17. QIN, Bo & WU, Jianfeng, 2015. "Does urban concentration mitigate CO2 emissions? Evidence from China 1998–2008," China Economic Review, Elsevier, vol. 35(C), pages 220-231.
    18. He, Xiaoping, 2022. "Energy effect of urban diversity: An empirical study from a land-use perspective," Energy Economics, Elsevier, vol. 108(C).
    19. Qing Su, 2017. "Travel Demand Management Policy Instruments, Urban Spatial Characteristics, and Household Greenhouse Gas Emissions from Travel in the US Urban Areas," International Journal of Energy Economics and Policy, Econjournals, vol. 7(3), pages 157-166.
    20. Jing Tao & Ying Wang & Rong Wang & Chuanmin Mi, 2019. "Do Compactness and Poly-Centricity Mitigate PM 10 Emissions? Evidence from Yangtze River Delta Area," IJERPH, MDPI, vol. 16(21), pages 1-18, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bba:j00009:v:3:y:2024:i:1:p:1-16:d:295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ramona Wang (email available below). General contact details of provider: https://www.anserpress.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.