IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p5109-d1670490.html
   My bibliography  Save this article

Urban Agglomeration Technology Innovation Networks, Spatial Spillover, and Agricultural Ecological Efficiency: Evidence from the Urban Agglomeration in the Middle Reaches of the Yangtze River in China

Author

Listed:
  • Weihui Peng

    (School of Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Zehuan Hu

    (School of Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Jie Li

    (School of Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Chenggang Li

    (School of Applied Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

Abstract

Urban agglomerations serve as essential platforms for regional innovation, while agricultural technology innovation and diffusion play pivotal roles in enhancing agricultural eco-efficiency (AEE). Based on panel data from the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) (2001–2023), this study employs a super-efficiency slacks-based measure model incorporating undesirable outputs to evaluate agricultural eco-efficiency. A modified gravity model is utilized to construct agricultural technology innovation networks (ATINs) in urban agglomerations, and a spatial Durbin model is applied to examine the spillover effects of network structure on eco-efficiency. The results indicate that: (1) Higher-degree centrality within the innovation network significantly improves local agricultural eco-efficiency and produces positive spillover effects on neighboring cities; (2) both direct and spillover effects are significant in central cities, whereas sub-central cities exhibit only a significant direct effect, and peripheral cities display an insignificant direct effect but a significant spillover effect; and (3) enhanced urban informatization, agricultural financial development, and industrial scale substantially strengthen the spatial spillover effects of the innovation network, thereby further advancing agricultural eco-efficiency within the agglomeration. These findings offer theoretical and empirical support for optimizing agricultural technology pathways and enhancing eco-efficiency in urban agglomerations.

Suggested Citation

  • Weihui Peng & Zehuan Hu & Jie Li & Chenggang Li, 2025. "Urban Agglomeration Technology Innovation Networks, Spatial Spillover, and Agricultural Ecological Efficiency: Evidence from the Urban Agglomeration in the Middle Reaches of the Yangtze River in China," Sustainability, MDPI, vol. 17(11), pages 1-27, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5109-:d:1670490
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/5109/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/11/5109/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yangyang Lin & Yiping Chen & Hongtao Nie & Lihong Peng, 2024. "Sustainable development of urban agglomeration based on material metabolism: a case study on Fujian Delta, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(4), pages 10533-10555, April.
    2. Li, Jinying & Li, Sisi, 2020. "Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model," Energy Policy, Elsevier, vol. 140(C).
    3. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    4. Liu, Yansui & Zou, Lilin & Wang, Yongsheng, 2020. "Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years," Land Use Policy, Elsevier, vol. 97(C).
    5. Tasso Adamopoulos & Loren Brandt & Jessica Leight & Diego Restuccia, 2022. "Misallocation, Selection, and Productivity: A Quantitative Analysis With Panel Data From China," Econometrica, Econometric Society, vol. 90(3), pages 1261-1282, May.
    6. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    7. Tamara Rudinskaya & Zdeňka Náglová, 2021. "Analysis of Consumption of Nitrogen Fertilisers and Environmental Efficiency in Crop Production of EU Countries," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Du, Jinfeng & Peiser, Richard B., 2014. "Land supply, pricing and local governments' land hoarding in China," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 180-189.
    10. Fei Chen & Guotong Qiao & Na Wang & Dandan Zhang, 2022. "Study on the Influence of Population Urbanization on Agricultural Eco-Efficiency and on Agricultural Eco-Efficiency Remeasuring in China," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    11. Fabrizio Fusillo & Francesco Quatraro & Stefano Usai, 2022. "Going green: the dynamics of green technological alliances," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 31(5), pages 362-386, July.
    12. Cohen, Barney, 2004. "Urban Growth in Developing Countries: A Review of Current Trends and a Caution Regarding Existing Forecasts," World Development, Elsevier, vol. 32(1), pages 23-51, January.
    13. Li Liu & Jin Luo & Xin Xiao & Bisong Hu & Shuhua Qi & Hui Lin & Xiaofang Zu, 2022. "Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China," Land, MDPI, vol. 11(5), pages 1-21, April.
    14. Xiaochen Wang & Yaqun Liu, 2024. "Enhancing Agricultural Ecological Efficiency in China: An Evolution and Pathways under the Carbon Neutrality Vision," Land, MDPI, vol. 13(2), pages 1-17, February.
    15. Yankang Hu & Hongchao Yu & Xinglong Yang, 2023. "Can Rural Human Capital Improve Agricultural Ecological Efficiency? Empirical Evidence from China," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    16. Song Wang & Jiexin Wang & Chenqi Wei & Xueli Wang & Fei Fan, 2021. "Collaborative innovation efficiency: From within cities to between cities—Empirical analysis based on innovative cities in China," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1330-1360, September.
    17. Sarah Velten & Julia Leventon & Nicolas Jager & Jens Newig, 2015. "What Is Sustainable Agriculture? A Systematic Review," Sustainability, MDPI, vol. 7(6), pages 1-33, June.
    18. Wang, Zhan & Deng, Xiangzheng & Bai, Yuping & Chen, Jiancheng & Zheng, Wentang, 2016. "Land use structure and emission intensity at regional scale: A case study at the middle reach of the Heihe River basin," Applied Energy, Elsevier, vol. 183(C), pages 1581-1593.
    19. Kawagoe, Toshihiko & Otsuka, Keijiro & Hayami, Yujiro, 1986. "Induced Bias of Technical Change in Agriculture: The United States and Japan, 1880-1980," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 523-544, June.
    20. 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.
    21. Evert Meijers & Martijn Burger & Martijn J. Burger & Evert J. Meijers, 2016. "Agglomerations and the rise of urban network externalities," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 5-15, March.
    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. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Mohammad Tavassoli & Mahsa Ghandehari & Masoud Taherinia, 2023. "Rang-adjusted measure: modelling and computational aspects from internal and external perspectives for network DEA," Operational Research, Springer, vol. 23(4), pages 1-34, December.
    3. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    4. Chen, Kuan-Chen & Lin, Sun-Yuan & Yu, Ming-Miin, 2022. "Exploring the efficiency of hospital and pharmacy utilizations in Taiwan: An application of dynamic network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    5. Zhen Shi & Fengping Wu & Huinan Huang & Xinrui Sun & Lina Zhang, 2019. "Comparing Economics, Environmental Pollution and Health Efficiency in China," IJERPH, MDPI, vol. 16(23), pages 1-30, December.
    6. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    7. Yu, Ming-Miin, 2010. "Assessment of airport performance using the SBM-NDEA model," Omega, Elsevier, vol. 38(6), pages 440-452, December.
    8. Sun, Han & Yuan, Ziyi & Wang, Xiaoxue & Chen, Lu & Zha, Zhiyun, 2025. "The security evaluation of nickel industrial and supply chains based on the NDEA window model," Resources Policy, Elsevier, vol. 100(C).
    9. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    10. Vincenzo Patrizii & Anna Pettini & Giuliano Resce, 2017. "The Cost of Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 985-1010, September.
    11. Shiu-Wan Hung & Chao-Liang Chang & Shu Ming Liu, 2019. "Innovation System Assessment Model for Sustainability Planning in Taiwan," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
    12. Yue Wu & Dong-Shang Chang, 2024. "Decomposing the comprehensive efficiency of major cities into divisions on governance, ICT and sustainability: network slack-based measure model," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    13. Eun Hak Lee & Hosuk Shin & Shin-Hyung Cho & Seung-Young Kho & Dong-Kyu Kim, 2019. "Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis," Energies, MDPI, vol. 12(19), pages 1-15, September.
    14. Gerami, Javad & Mozaffari, Mohammad Reza & Wanke, Peter F. & Correa, Henrique L., 2022. "Improving information reliability of non-radial value efficiency analysis: An additive slacks based measure approach," European Journal of Operational Research, Elsevier, vol. 298(3), pages 967-978.
    15. Wen-Min Lu & Qian Long Kweh & Kai-Chu Yang, 2022. "Multiplicative efficiency aggregation to evaluate Taiwanese local auditing institutions performance," Annals of Operations Research, Springer, vol. 315(2), pages 1243-1262, August.
    16. Aparicio, Juan & Ortiz, Lidia & Pastor, Jesus T., 2017. "Measuring and decomposing profit inefficiency through the Slacks-Based Measure," European Journal of Operational Research, Elsevier, vol. 260(2), pages 650-654.
    17. Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
    18. Chen, Ci & Yan, Hong, 2011. "Network DEA model for supply chain performance evaluation," European Journal of Operational Research, Elsevier, vol. 213(1), pages 147-155, August.
    19. Jingjie Li & Chenying Cui, 2024. "Spatiotemporal trends and coordination of agricultural carbon efficiency in the Yangtze River Economic Belt and Yellow River Basin, China: An analysis of influencing factors and green finance integrat," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-30, August.
    20. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.

    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:11:p:5109-:d:1670490. 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.