IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i18p8153-d1746536.html
   My bibliography  Save this article

The Spatio-Temporal Characteristics and Factors Influencing of the Multidimensional Coupling Relationship Between the Land Price Gradient and Industrial Gradient in the Beijing–Tianjin–Hebei Urban Agglomeration

Author

Listed:
  • Deqi Wang

    (School of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China
    Research Institute for Integrated Urban-Rural and Territorial Space Governance, Capital University of Economics and Business, Beijing 100070, China)

  • Wei Liang

    (School of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China
    Research Institute for Integrated Urban-Rural and Territorial Space Governance, Capital University of Economics and Business, Beijing 100070, China)

Abstract

When considering an urban agglomeration as a unit, promoting the coupling and coordination of the land price gradient and industrial gradient is crucial for achieving regional integrated development. We selected the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) as a case study; constructed a three-dimensional analytical framework involving static coupling, dynamic coupling, and spatial matching; theoretically clarified the coupling mechanism between the land price gradient and industrial gradient; and systematically assessed their spatial-temporal patterns and coupling characteristics. The results indicate that from 2012 to 2022, both the land price gradient and industrial gradient within the BTHUA exhibited a “core-periphery” spatial distribution, gradually forming an over-all pattern of “one core, multiple nodes, and multi-level rings.” For the Beijing–Tianjin–Hebei urban agglomeration, overall static coupling and spatial matching exhibit an evolutionary trajectory of “first rising, then declining.” By contrast, dynamic coupling remains relatively weak, exhibiting a corridor-shaped distribution along core and sub-core cities. All three indicators consistently show that core cities outperform peripheral cities. Nonlinear mechanism analysis based on the gradient boosting decision tree method showed that “second-nature” factors like economic development and public utilities significantly promote multidimensional coupling. Conversely, “first-nature” factors, such as geographic conditions, have limited impacts with threshold effects; surpassing these thresholds results in inhibitory effects. Based on the research findings, this study proposes that regional integration should serve as the guiding principle, emphasizing the cultivation of regional development corridors, the implementation of flexible and functionally aligned land supply policies, the strengthening of land use performance audits, and the reorientation of fiscal and financial policies toward structural and qualitative improvements. These measures can provide valuable references for promoting coordinated industrial development and balanced land allocation in urban agglomerations.

Suggested Citation

  • Deqi Wang & Wei Liang, 2025. "The Spatio-Temporal Characteristics and Factors Influencing of the Multidimensional Coupling Relationship Between the Land Price Gradient and Industrial Gradient in the Beijing–Tianjin–Hebei Urban Agg," Sustainability, MDPI, vol. 17(18), pages 1-36, September.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8153-:d:1746536
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/18/8153/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/18/8153/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Shuchang & Xiao, Wu & Li, Linlin & Ye, Yanmei & Song, Xiaoli, 2020. "Urban land use efficiency and improvement potential in China: A stochastic frontier analysis," Land Use Policy, Elsevier, vol. 99(C).
    2. Duranton, Gilles & Puga, Diego, 2005. "From sectoral to functional urban specialisation," Journal of Urban Economics, Elsevier, vol. 57(2), pages 343-370, March.
    3. Wang, Xipan & Song, Junnian & Duan, Haiyan & Wang, Xian'en, 2021. "Coupling between energy efficiency and industrial structure: An urban agglomeration case," Energy, Elsevier, vol. 234(C).
    4. Zhang, Wenxi & Wang, Bo & Wang, Jian & Wu, Qun & Wei, Yehua Dennis, 2022. "How does industrial agglomeration affect urban land use efficiency? A spatial analysis of Chinese cities," Land Use Policy, Elsevier, vol. 119(C).
    5. Chenxi Li & Xing Gao & Bao-Jie He & Jingyao Wu & Kening Wu, 2019. "Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    6. Haixin Huang & Jiageng Yang, 2024. "Analysis of the Spatiotemporal Differentiation and Influencing Factors of Land Use Efficiency in the Beijing–Tianjin–Hebei Urban Agglomeration," Land, MDPI, vol. 13(9), pages 1-18, September.
    7. Hakan Yilmazkuday, 2023. "COVID-19 and housing prices: evidence from U.S. county-level data," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(2), pages 241-263, August.
    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. Mengzhi Zou & Changyou Li & Yanni Xiong, 2022. "Analysis of Coupling Coordination Relationship between the Accessibility and Economic Linkage of a High-Speed Railway Network Case Study in Hunan, China," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    2. Saige Wang & Chenchen Zhai & Yunxiao Zhang, 2024. "Evaluating the Impact of Urban Digital Infrastructure on Land Use Efficiency Based on 279 Cities in China," Land, MDPI, vol. 13(4), pages 1-24, March.
    3. Yongqiang Li & Bowen Li & Jiani Chen & Yue Zhang & Yian Hu & Chengming Li, 2025. "Data-Driven Green Transformation: How Public Data Openness Fuels Urban Land Use Eco-Efficiency in Chinese Cities," Land, MDPI, vol. 14(5), pages 1-29, May.
    4. Jingyi Wang & Kaisi Sun & Jiupai Ni & Deti Xie, 2020. "Evaluation and Factor Analysis of the Intensive Use of Urban Land Based on Technical Efficiency Measurement—A Case Study of 38 Districts and Counties in Chongqing, China," Sustainability, MDPI, vol. 12(20), pages 1-19, October.
    5. Xue Luo & Weixin Luan & Qiaoqiao Lin & Zun Liu & Zhipeng Shi & Gai Cao, 2025. "Nonlinear Relationships Between Economic Development Stages and Land Use Efficiency in China’s Cities," Land, MDPI, vol. 14(9), pages 1-21, August.
    6. Mengchao Yao & Yihua Zhang, 2021. "Evaluation and Optimization of Urban Land-Use Efficiency: A Case Study in Sichuan Province of China," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    7. Yufan Sun & Zhuo Jia & Qi Chen & Heya Na, 2022. "Spatial Pattern and Spillover Effects of the Urban Land Green Use Efficiency for the Lanzhou-Xining Urban Agglomeration of the Yellow River Basin," Land, MDPI, vol. 12(1), pages 1-15, December.
    8. Shoupeng Wang & Haixin Huang & Fenghua Wu, 2025. "Can Local Industrial Policy Enhance Urban Land Green Use Efficiency? Evidence from the “Made in China 2025” National Demonstration Zone Policy," Land, MDPI, vol. 14(8), pages 1-24, July.
    9. Chen, Danling & Li, Yuying & Zhang, Chaozheng & Zhang, Yunlei & Hou, Jiao & Lin, Yaoben & Wu, Shiman & Lang, Yan & Hu, Wenbo, 2024. "Regional coordinated development policy as an instrument for alleviating land finance dependency: Evidence from the urban agglomeration development," Land Use Policy, Elsevier, vol. 143(C).
    10. Yan Jiang & Lun Yang & Xiaokun Wei & Xiaodong Zhang, 2024. "The Impact of Government Digital Transformation on Land Use Efficiency: Evidence from China," Land, MDPI, vol. 13(12), pages 1-26, December.
    11. Yanzhe Cui & Yingnan Niu & Yawen Ren & Shiyi Zhang & Lindan Zhao, 2024. "A Model to Analyze Industrial Clusters to Measure Land Use Efficiency in China," Land, MDPI, vol. 13(7), pages 1-22, July.
    12. Edward L. Glaeser & Giacomo A. M. Ponzetto, 2010. "Did the Death of Distance Hurt Detroit and Help New York?," NBER Chapters, in: Agglomeration Economics, pages 303-337, National Bureau of Economic Research, Inc.
    13. Duranton, Gilles & Jayet, Hubert, 2011. "Is the division of labour limited by the extent of the market? Evidence from French cities," Journal of Urban Economics, Elsevier, vol. 69(1), pages 56-71, January.
    14. Bono, Pierre-Henri & David, Quentin & Desbordes, Rodolphe & Py, Loriane, 2022. "Metro infrastructure and metropolitan attractiveness," Regional Science and Urban Economics, Elsevier, vol. 93(C).
    15. Matthias Firgo & Peter Mayerhofer, 2015. "Wissens-Spillovers und regionale Entwicklung - welche strukturpolitische Ausrichtung optimiert des Wachstum?," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 144, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    16. P. Charnoz & C. Lelarge & C. Trevien, 2016. "Communication Costs and the Internal Organization of Multi-Plant Businesses: Evidence from the Impact of the French High-Speed Rail," Documents de Travail de l'Insee - INSEE Working Papers g2016-02, Institut National de la Statistique et des Etudes Economiques.
    17. Teresa Garcia-Milà & Therese J. McGuire, 2001. "Tax incentives and the city," Economics Working Papers 631, Department of Economics and Business, Universitat Pompeu Fabra, revised Dec 2001.
    18. Andrés Rodríguez-Pose & Riccardo Crescenzi, 2008. "Mountains in a flat world: why proximity still matters for the location of economic activity," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 1(3), pages 371-388.
    19. Riccardo Crescenzi & Carlo Pietrobelli & Roberta Rabellotti, 2012. "Innovation Drivers, Value Chains and the Geography of Multinational Firms in European Regions," LEQS – LSE 'Europe in Question' Discussion Paper Series 53, European Institute, LSE.
    20. Na Lu & Tiantian Shan & Wen Li & Xuan Liu & Weidong Wang, 2025. "Does the Digital Economy Promote Green Land Use Efficiency?," Sustainability, MDPI, vol. 17(16), pages 1-22, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:17:y:2025:i:18:p:8153-:d:1746536. 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.