IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2643-d1614028.html

Spatiotemporal Heterogeneity in the Efficiency of Agricultural Eco-Product Value Conversion: An Empirical Study from China

Author

Listed:
  • Guanshisheng Xie

    (College of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China)

  • Zhongjie Zhang

    (College of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China)

  • Bida Wang

    (College of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China)

Abstract

Understanding the efficiency of agricultural eco-product value realization is critical for sustainable development and regional equity. Here, we present a comprehensive analysis of the spatiotemporal patterns and regional disparities in the value realization efficiency of agricultural ecological products across China’s 31 provinces from 2010 to 2022. Utilizing an advanced Super-NSBM model, we quantify three dimensions of efficiency: overall value realization, economic value conversion, and social welfare value realization. Spatial mapping and dynamic evolution analysis are conducted through Dagum Gini coefficient decomposition and conditional kernel density estimation. Our results reveal three key insights: (1) China’s agricultural eco-product value realization efficiency remains suboptimal, with a gradual upward trend. Economic value conversion outperforms social welfare value realization, which exhibits significant regional heterogeneity. A distinct east–west gradient is observed, with Western regions demonstrating notable progress despite initial inefficiencies. (2) Inter-regional disparities are narrowing, particularly between Eastern and Central regions. While polarization in Northeast China has diminished, Western regions show widening efficiency gaps and emerging polarization trends. (3) Regional differences are predominantly driven by inter-group disparities, with Eastern China exhibiting the lowest intra-regional variability. Cross-regional differences follow a U-shaped trajectory, decreasing initially before rebounding in recent years. These findings provide a robust empirical foundation for optimizing regional strategies in ecological product value conversion and offer critical insights for addressing spatial inequities in sustainable agricultural development.

Suggested Citation

  • Guanshisheng Xie & Zhongjie Zhang & Bida Wang, 2025. "Spatiotemporal Heterogeneity in the Efficiency of Agricultural Eco-Product Value Conversion: An Empirical Study from China," Sustainability, MDPI, vol. 17(6), pages 1-26, March.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2643-:d:1614028
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2643/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/6/2643/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    2. repec:lan:wpaper:1115 is not listed on IDEAS
    3. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    4. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    5. Kristiaan Kerstens & Ignace Van de Woestyne, 2018. "Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions," Annals of Operations Research, Springer, vol. 271(2), pages 1067-1078, December.
    6. Ahmad, Usman, 2011. "Financial Reforms and Banking Efficiency: Case of Pakistan," MPRA Paper 34220, University Library of Munich, Germany.
    7. Bowlin, W. F., 1995. "A characterization of the financial condition of the United States' aerospace-defense industrial base," Omega, Elsevier, vol. 23(5), pages 539-555, October.
    8. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    9. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    10. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    11. Khanal, Aditya & Koirala, Krishna & Regmi, Madhav, "undated". "Do Financial Constraints Affect Production Efficiency in Drought Prone Areas? A Case from Indonesian Rice Growers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230087, Southern Agricultural Economics Association.
    12. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    13. Vuciterna, Rina & Thomsen, Michael & Popp, Jennie & Musliu, Arben, 2017. "Efficiency and Competitiveness of Kosovo Raspberry Producers," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252770, Southern Agricultural Economics Association.
    14. Bogetoft, Peter & Nielsen, Kurt, 2003. "Yardstick Based Procurement Design In Natural Resource Management," 2003 Annual Meeting, August 16-22, 2003, Durban, South Africa 25910, International Association of Agricultural Economists.
    15. Singer, Marcos & Donoso, Patricio & Poblete, Francisco, 2002. "Semi-autonomous planning using linear programming in the Chilean General Treasury," European Journal of Operational Research, Elsevier, vol. 140(2), pages 517-529, July.
    16. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    17. Fang, Lei, 2022. "Measuring and decomposing group performance under centralized management," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1006-1013.
    18. Chih-HAI YANG & WU Leah & Hui-Lin LIN, 2010. "Analysis of total-factor cultivated land efficiency in China's agriculture," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(5), pages 231-242.
    19. Jinyi Hu, 2023. "Linguistic Multiple-Attribute Decision Making Based on Regret Theory and Minimax-DEA," Mathematics, MDPI, vol. 11(20), pages 1-14, October.
    20. 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).
    21. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.

    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:6:p:2643-:d:1614028. 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.