IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i7p693-d1620167.html
   My bibliography  Save this article

Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China

Author

Listed:
  • Yu He

    (College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
    Institute of Horticultural Economics, Huazhong Agriculture University, Wuhan 430070, China
    Hubei Rural Development Research Center, Wuhan 430070, China)

  • Guozhu Fang

    (Department of Economics, Party School of Zhejiang Provincial Committee of Communist Party of China, Hangzhou 310012, China)

  • Chunjie Qi

    (College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
    Institute of Horticultural Economics, Huazhong Agriculture University, Wuhan 430070, China)

  • Yumeng Gu

    (College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
    Institute of Horticultural Economics, Huazhong Agriculture University, Wuhan 430070, China
    Hubei Rural Development Research Center, Wuhan 430070, China)

Abstract

Agricultural green development is an essential pathway to achieving comprehensive agricultural and rural modernization and holds significant importance for ensuring national food, resource, and ecological security. Based on panel data from 30 provinces in China during 2004–2022, this study employed the super-efficiency SBM-GML model, the modified gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) regression model to systematically analyze the spatial association network characteristics and driving mechanisms of agricultural green development in China. The results showed that (1) the number of spatial linkages in interprovincial agricultural green development had been increasing, with the network exhibiting strong connectivity, stability, and accessibility. (2) Major grain-producing areas and economically developed regions along the eastern coast had become the driving sources of spatial spillovers in agricultural green development. Meanwhile, the central and western regions acted as “brokers” in facilitating the reception and transfer of resources within the overall network, while municipalities such as Tianjin and Shanghai exhibited siphon effects on other regions. (3) Geographical proximity, government fiscal support, rural labor force size, progress in green technologies, and the agricultural economic development level significantly enhanced the spatial spillover effects of agricultural green development. However, regional disparities in agricultural industrial structures served as a key obstacle to realizing these spillover effects.

Suggested Citation

  • Yu He & Guozhu Fang & Chunjie Qi & Yumeng Gu, 2025. "Research on the Spatial Correlation Network and Driving Mechanism of Agricultural Green Development in China," Agriculture, MDPI, vol. 15(7), pages 1-22, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:7:p:693-:d:1620167
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/7/693/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/7/693/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongjia Fang & Ji Chai & Zhanqi Wang & Rou Zhang & Chao Huang & Meiling Luo, 2024. "Exploring the Spatial Correlation Network and Its Formation Mechanisms in Urban Land Use Performance: A Case Study of the Yangtze River Economic Belt," Land, MDPI, vol. 13(7), pages 1-21, July.
    2. Xiaocheng Wang & Chenxi Yang & Cuixia Qiao, 2024. "Agricultural Service Trade and Green Development: A Perspective Based on China’s Agricultural Total Factor Productivity," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    3. Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    4. Yumeng Gu & Chunjie Qi & Yu He & Fuxing Liu & Beige Luo, 2023. "Spatial Correlation Network Structure of and Factors Influencing Technological Progress in Citrus-Producing Regions in China," Agriculture, MDPI, vol. 13(11), pages 1-20, November.
    5. Bai, Rui & Lin, Boqiang, 2024. "An in-depth analysis of green innovation efficiency: New evidence based on club convergence and spatial correlation network," Energy Economics, Elsevier, vol. 132(C).
    6. Zhan, Yunqiu & Wang, Wei & Ren, Yuheng, 2024. "Inspection supervision and corporate green total factor productivity," Finance Research Letters, Elsevier, vol. 67(PB).
    7. Song, Malin & Du, Juntao & Tan, Kim Hua, 2018. "Impact of fiscal decentralization on green total factor productivity," International Journal of Production Economics, Elsevier, vol. 205(C), pages 359-367.
    8. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    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. Cheng, Hao & Wu, Boyu & Jiang, Xiaokun, 2024. "Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities," Applied Energy, Elsevier, vol. 371(C).
    2. Meiling Wang & Silu Pang & Ikram Hmani & Ilham Hmani & Cunfang Li & Zhengxia He, 2021. "Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 217-227, January.
    3. Lv, Chengchao & Song, Jie & Lee, Chien-Chiang, 2022. "Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 502-521.
    4. Liu, Zuankuo & Xin, Li, 2019. "Has China's Belt and Road Initiative promoted its green total factor productivity？——Evidence from primary provinces along the route," Energy Policy, Elsevier, vol. 129(C), pages 360-369.
    5. Junqi Wen & Yong Lan & Guoqin Bu, 2024. "Do Innovation and Entrepreneurship Support Policies Promote Urban Green Transformation?—The Mediating Role of Fiscal Technology Expenditure," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    6. Yuxin Fang & Hongjun Cao, 2022. "Environmental Decentralization, Heterogeneous Environmental Regulation, and Green Total Factor Productivity—Evidence from China," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    7. Lin Zhu & Xiaoming Li & Yao Huang & Fangyuan Liu & Chengji Yang & Dongyang Li & Hongpeng Bai, 2023. "Digital Technology and Green Development in Manufacturing: Evidence from China and 20 Other Asian Countries," Sustainability, MDPI, vol. 15(17), pages 1-20, August.
    8. Xie, Ronghui & Teo, Thompson S.H., 2022. "Green technology innovation, environmental externality, and the cleaner upgrading of industrial structure in China — Considering the moderating effect of environmental regulation," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    9. Song, Malin & Zhu, Shuai & Wang, Jianlin & Zhao, Jiajia, 2020. "Share green growth: Regional evaluation of green output performance in China," International Journal of Production Economics, Elsevier, vol. 219(C), pages 152-163.
    10. Ke Mao & Pierre Failler, 2022. "Local Government Debt and Green Total Factor Productivity—Empirical Evidence from Chinese Cities," IJERPH, MDPI, vol. 19(19), pages 1-13, September.
    11. Yung‐ho Chiu & Kuei‐Ying Huang & Tai‐Yu Lin & Tzu‐Han Chang, 2022. "Government debt and fiscal execution efficiency," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 111-128, January.
    12. Wang, Mei & Xu, Mi & Ma, Shaojun, 2021. "The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 427-441.
    13. Ning Li & Ruiling Li & Shangshang Yu, 2024. "Managerial Myopia and Enterprise Green Total Factor Productivity: Perspectives on the Supervisory Effect and Incentive Effect," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    14. Junwei Zhao & Yuxiang Zhang & Anhang Chen & Huiqin Zhang, 2022. "Analysis on the Spatio-Temporal Evolution Characteristics of the Impact of China’s Digitalization Process on Green Total Factor Productivity," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    15. Lee, Chien-Chiang & He, Zhi-Wen & Yuan, Zihao, 2023. "A pathway to sustainable development: Digitization and green productivity," Energy Economics, Elsevier, vol. 124(C).
    16. Wen-Min Lu & Qian Long Kweh & Chung-Wei Wang, 2021. "Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry," Annals of Operations Research, Springer, vol. 296(1), pages 163-194, January.
    17. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    18. 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.
    19. Dongqing Han & Dayong Zhang & Peng Yue & Zhengxu Cao, 2024. "Toward Sustainable Development: Can Digital Transformation of Industrial Enterprise Drive Carbon Reduction?," Sustainability, MDPI, vol. 16(23), pages 1-18, November.
    20. Chen, Ya & Pan, Yongbin & Liu, Haoxiang & Wu, Huaqing & Deng, Guangwei, 2023. "Efficiency analysis of Chinese universities with shared inputs: An aggregated two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).

    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:jagris:v:15:y:2025:i:7:p:693-:d:1620167. 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.