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

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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
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    References listed on IDEAS

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    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
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