IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i7p2962-d1369112.html
   My bibliography  Save this article

Study on the Spatiotemporal Evolution of Urban Land Use Efficiency in the Beijing–Tianjin–Hebei Region

Author

Listed:
  • Zhang Zhang

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Huimin Zhou

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China)

  • Shuxian Li

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Zhibin Zhao

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Junbo Xu

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Yuansuo Zhang

    (College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China)

Abstract

The Beijing–Tianjin–Hebei region (BTH) is one of the crucial areas for economic development in China. However, rapid urban expansion and industrial development in this region have severely impacted the surrounding ecological environment. The air quality, water, and soil resources face significant pressure. Due to the close relationship between land utilization, population, investment, and industry, effective land use is a key factor in the coordinated development of the region. Therefore, clarifying the patterns of urban land use change and revealing its influencing factors can provide important scientific evidence for the coordinated development of the BTH region. This study aims to improve urban land use efficiency (ULUE) in the BTH region. Firstly, based on the input and output data of land elements for the 13 cities in the BTH region, the Data Envelopment Analysis (DEA) method is used to quantify the ULUE of the BTH urban agglomeration and analyze the spatiotemporal characteristics of ULUE. Input indicators includes capital, labor, and land. Output indicators includes economy, society, and environment. The results show that the overall ULUE in the BTH region has increased, albeit with notable fluctuations. Between 2000 and 2010, ULUE rose swiftly across all cities except Beijing and Tianjin, where changes were minimal. Post-2010, cities exhibited varied trends: steady growth, slow growth, sustained growth, step-wise growth, and initial growth followed by decline. Spatially, before 2010, the BTH showed a “high in the northeast and low in the southwest” pattern, transitioning post-2010 to a smoother “core-periphery” pattern. Mid-epidemic, high ULUE values reverted to the core area, shifting southward post-epidemic. Secondly, panel data analysis is conducted to explore the factors influencing ULUE. The results indicate that fiscal balance, the level of openness, the level of digitalization, industrial structure, and the level of green development are significant factors affecting ULUE. Finally, strategies are proposed to improve ULUE in the BTH region, including national spatial planning, industrial layout, existing land use, infrastructure construction, optimization of local fiscal revenue, and improvement in the business environment, aiming to enhance ULUE and promote the coordinated development of industries in the BTH region.

Suggested Citation

  • Zhang Zhang & Huimin Zhou & Shuxian Li & Zhibin Zhao & Junbo Xu & Yuansuo Zhang, 2024. "Study on the Spatiotemporal Evolution of Urban Land Use Efficiency in the Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 16(7), pages 1-27, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2962-:d:1369112
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/7/2962/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/7/2962/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Yuanyuan & Liu, Yansui & Li, Yurui & Li, Jintao, 2018. "Measure of urban-rural transformation in Beijing-Tianjin-Hebei region in the new millennium: Population-land-industry perspective," Land Use Policy, Elsevier, vol. 79(C), pages 595-608.
    2. Haojie Liu & Jinyue Liu & Weixin Yang & Jianing Chen & Mingyang Zhu, 2020. "Analysis and Prediction of Land Use in Beijing-Tianjin-Hebei Region: A Study Based on the Improved Convolutional Neural Network Model," Sustainability, MDPI, vol. 12(7), pages 1-25, April.
    3. Jinliu Chen & Paola Pellegrini & Zhuo Yang & Haoqi Wang, 2023. "Strategies for Sustainable Urban Renewal: Community-Scale GIS-Based Analysis for Densification Decision Making," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    4. Lisha Pan & Hangang Hu & Xin Jing & Yang Chen & Guan Li & Zhongguo Xu & Yuefei Zhuo & Xueqi Wang, 2022. "The Impacts of Regional Cooperation on Urban Land-Use Efficiency: Evidence from the Yangtze River Delta, China," Land, MDPI, vol. 11(6), pages 1-16, June.
    5. Werner Z. Hirsch, 1977. "The Efficiency of Restrictive Land Use Instruments," Land Economics, University of Wisconsin Press, vol. 53(2), pages 145-156.
    6. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    7. Wanxu Chen & Guangqing Chi & Jiangfeng Li, 2020. "Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    9. Kun Ge & Shan Zou & Danling Chen & Xinhai Lu & Shangan Ke, 2021. "Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China," Land, MDPI, vol. 10(10), pages 1-20, October.
    10. Ma, Wenqiu & Jiang, Guanghui & Chen, Yunhao & Qu, Yanbo & Zhou, Tao & Li, Wenqing, 2020. "How feasible is regional integration for reconciling land use conflicts across the urban–rural interface? Evidence from Beijing–Tianjin–Hebei metropolitan region in China," Land Use Policy, Elsevier, vol. 92(C).
    11. Lu, Xinhai & Chen, Danling & Kuang, Bing & Zhang, Chaozheng & Cheng, Chen, 2020. "Is high-tech zone a policy trap or a growth drive? Insights from the perspective of urban land use efficiency," Land Use Policy, Elsevier, vol. 95(C).
    12. Wang, Chenglong & Liu, Hui & Zhang, Mengtian & Wei, Zongcai, 2018. "The border effect on urban land expansion in China: The case of Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 78(C), pages 287-294.
    13. Binpin Gao & Yingmei Wu & Chen Li & Kejun Zheng & Yan Wu, 2022. "Ecosystem Health Responses of Urban Agglomerations in Central Yunnan Based on Land Use Change," IJERPH, MDPI, vol. 19(19), pages 1-20, September.
    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. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    2. Yande Gong & Joe Zhu & Ya Chen & Wade D. Cook, 2018. "DEA as a tool for auditing: application to Chinese manufacturing industry with parallel network structures," Annals of Operations Research, Springer, vol. 263(1), pages 247-269, April.
    3. Jin XU & Panagiotis ZERVOPOULOS & Zhenhua QIAN & Gang CHENG, 2012. "A Universal Solution For Units - Invariance In Data Envelopment Analysis," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 3(2), pages 121-128.
    4. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    5. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 442-471, December.
    6. Alexandr Gedranovich & Mykhaylo Salnykov, 2012. "Productivity analysis of Belarusian higher education system," BEROC Working Paper Series 16, Belarusian Economic Research and Outreach Center (BEROC).
    7. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    8. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.
    9. Tao Ding & Ya Chen & Huaqing Wu & Yuqi Wei, 2018. "Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach," Annals of Operations Research, Springer, vol. 268(1), pages 497-511, September.
    10. Branda, Martin, 2013. "Diversification-consistent data envelopment analysis with general deviation measures," European Journal of Operational Research, Elsevier, vol. 226(3), pages 626-635.
    11. Yingying Shao & Gongbing Bi & Feng Yang & Qiong Xia, 2018. "Resource allocation for branch network system with considering heterogeneity based on DEA method," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 1005-1025, December.
    12. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    13. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    14. Francisco Javier Sáez-Fernández & Andrés J. Picazo-Tadeo & Mercedes Beltrán-Esteve & Caroline Elliott, 2015. "Assessing the performance of the Latin American and Caribbean banking industry: Are domestic and foreign banks so different?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1006976-100, December.
    15. Cheng, Gang & Qian, Zhenhua, 2011. "Dea数据标准化方法及其在方向距离函数模型中的应用 [Data normalization for data envelopment analysis and its application to directional distance function]," MPRA Paper 31995, University Library of Munich, Germany.
    16. Jean-Paul Chavas & Kwansoo Kim, 2015. "Nonparametric analysis of technology and productivity under non-convexity: a neighborhood-based approach," Journal of Productivity Analysis, Springer, vol. 43(1), pages 59-74, February.
    17. Meng-Chun Kao & Chien-Ting Lin & Lei Xu, 2012. "Do Financial Reforms Improve the Performance of Financial Holding Companies? The Case of T aiwan," International Review of Finance, International Review of Finance Ltd., vol. 12(4), pages 491-509, December.
    18. Fernández, David & Pozo, Carlos & Folgado, Rubén & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2018. "Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index," Applied Energy, Elsevier, vol. 212(C), pages 1563-1577.
    19. Alperovych, Yan & Amess, Kevin & Wright, Mike, 2013. "Private equity firm experience and buyout vendor source: What is their impact on efficiency?," European Journal of Operational Research, Elsevier, vol. 228(3), pages 601-611.
    20. Anna Ćwiąkała-Małys & Violetta Nowak, 2009. "Classification of Data Envelopment Analysis models," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 19(3), pages 5-18.

    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:16:y:2024:i:7:p:2962-:d:1369112. 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.