IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i10p1902-d1256913.html
   My bibliography  Save this article

Spatiotemporal Characteristics and Determinants of Rural Construction Land in China’s Developed Areas: A Case Study of the Yangtze River Delta

Author

Listed:
  • Fangqu Niu

    (Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Lan Wang

    (Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Wei Sun

    (Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Rural construction land (RCL) received less attention but played an important role to control rural land use. Studying the RCL of developed areas may provide valuable references for underdeveloped areas to optimize land use. The Yangtze River Delta (YRD) is the most economically developed region in China. The study is intended to explore the spatiotemporal characteristics and determinants of RCL in the YRD based on a period of data from 1990 to 2017. The results show that the RCL in the YRD increases at an average annual rate of 5.38% but the growth rate tends to decrease. There is a weak spatial linkage of the RCL growth between cities. Clear spatial differences exist in the effects of every determinant of RCL. The correlation between the rural population and the RCL is unstable, which proves the existence of hollow villages. There is no clear correlation between the RCL and the local economy and accessibility, as the rural population normally goes to few big cities for higher salary work but spends the money in their hometowns on building homes. These findings help optimize rural land use in the YRD and provide an important reference for planning land use in underdeveloped regions.

Suggested Citation

  • Fangqu Niu & Lan Wang & Wei Sun, 2023. "Spatiotemporal Characteristics and Determinants of Rural Construction Land in China’s Developed Areas: A Case Study of the Yangtze River Delta," Land, MDPI, vol. 12(10), pages 1-19, October.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1902-:d:1256913
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/10/1902/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/10/1902/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergio J. Rey & Xinyue Ye, 2010. "Comparative Spatial Dynamics of Regional Systems," Advances in Spatial Science, in: Antonio Páez & Julie Gallo & Ron N. Buliung & Sandy Dall'erba (ed.), Progress in Spatial Analysis, pages 441-463, Springer.
    2. Wang, Degen & Zhu, Yujia & Zhao, Meifeng & Lv, Qingyue, 2021. "Multi-dimensional hollowing characteristics of traditional villages and its influence mechanism based on the micro-scale: A case study of Dongcun Village in Suzhou, China," Land Use Policy, Elsevier, vol. 101(C).
    3. Ming Li & Guojun Zhang & Ying Liu & Yongwang Cao & Chunshan Zhou, 2019. "Determinants of Urban Expansion and Spatial Heterogeneity in China," IJERPH, MDPI, vol. 16(19), pages 1-19, October.
    4. Nikos Alexandratos, 2005. "Countries with Rapid Population Growth and Resource Constraints: Issues of Food, Agriculture, and Development," Population and Development Review, The Population Council, Inc., vol. 31(2), pages 237-258, June.
    5. 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.
    6. Isabelle D. Wolf & Parvaneh Sobhani & Hassan Esmaeilzadeh, 2023. "Assessing Changes in Land Use/Land Cover and Ecological Risk to Conserve Protected Areas in Urban–Rural Contexts," Land, MDPI, vol. 12(1), pages 1-22, January.
    7. Shengqiang Jing & Yueguan Yan & Fangqu Niu & Wenhui Song, 2022. "Urban Expansion in China: Spatiotemporal Dynamics and Determinants," Land, MDPI, vol. 11(3), pages 1-16, February.
    8. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    9. Ning Zhang & Kangning Xiong & Hua Xiao & Juan Zhang & Chuhong Shen, 2023. "Ecological Environment Dynamic Monitoring and Driving Force Analysis of Karst World Heritage Sites Based on Remote-Sensing: A Case Study of Shibing Karst," Land, MDPI, vol. 12(1), pages 1-15, January.
    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. 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.
    2. Huxiao Zhu & Xiangjun Ou & Zhen Yang & Yiwen Yang & Hongxin Ren & Le Tang, 2022. "Spatiotemporal Dynamics and Driving Forces of Land Urbanization in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 11(8), pages 1-21, August.
    3. Shengqiang Jing & Yueguan Yan & Fangqu Niu & Wenhui Song, 2022. "Urban Expansion in China: Spatiotemporal Dynamics and Determinants," Land, MDPI, vol. 11(3), pages 1-16, February.
    4. Cao, Xiang & Luo, Yuying & Chen, Xiaolan & Xie, Qiuyue & Yao, Zhenyu, 2024. "Spatial valuation of urban green lungs: Unveiling the true worth of urban parks through MGWR in Chengdu, China," Land Use Policy, Elsevier, vol. 145(C).
    5. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    6. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    7. Burhan Can Karahasan, 2020. "Can neighbor regions shape club convergence? Spatial Markov chain analysis for Turkey," Letters in Spatial and Resource Sciences, Springer, vol. 13(2), pages 117-131, August.
    8. Ye, Xinyue & Yue, Wenze, 2014. "Comparative analysis of regional development: Exploratory space-time data analysis and open source implementation," Economics Discussion Papers 2014-20, Kiel Institute for the World Economy (IfW Kiel).
    9. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    10. Zhongfa Zhou & Changli Zhu, 2022. "Relative Spatial Poverty Within Guizhou Province, A Multidimensional Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(1), pages 151-170, May.
    11. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    12. Ya-Di Dai & Hui-Guo Zhang, 2025. "Non-Iterative Estimation of Multiscale Geographically and Temporally Weighted Regression Model," Mathematics, MDPI, vol. 13(9), pages 1-16, April.
    13. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    14. Junfeng Wang & Shaoyao Zhang & Wei Deng & Qianli Zhou, 2024. "Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China," Land, MDPI, vol. 13(1), pages 1-19, January.
    15. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    16. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    17. Jiansheng Qu & Lina Liu & Jingjing Zeng & Tek Narayan Maraseni & Zhiqiang Zhang, 2022. "City-Level Determinants of Household CO 2 Emissions per Person: An Empirical Study Based on a Large Survey in China," Land, MDPI, vol. 11(6), pages 1-14, June.
    18. Zhang, Wei & Li, Yuqing & Zheng, Caigui, 2023. "The distribution characteristics and driving mechanism of vacant land in Chengdu, China: A perspective of urban shrinkage and expansion," Land Use Policy, Elsevier, vol. 132(C).
    19. Pengzhi Wei & Shaofeng Xie & Liangke Huang & Lilong Liu, 2021. "Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM 2.5 Concentration in Central and Southern China," IJERPH, MDPI, vol. 18(15), pages 1-26, July.
    20. Longjiang Zhang & Guoping Chen & Junsan Zhao & Yilin Lin & Haibo Yang & Jianhua He, 2025. "Spatiotemporal Characteristics and Scale Effects of Ecosystem Service Bundles in the Xijiang River Basin: Implications for Territorial Spatial Planning and Sustainable Land Management," Sustainability, MDPI, vol. 17(5), pages 1-23, February.

    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:gam:jlands:v:12:y:2023:i:10:p:1902-:d:1256913. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.