IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p3926-d1075851.html
   My bibliography  Save this article

Receiving Robust Analysis of Spatial and Temporary Variation of Agricultural Water Use Efficiency While Considering Environmental Factors: On the Evaluation of Data Envelopment Analysis Technique

Author

Listed:
  • Hongguang Dong

    (School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China)

  • Jie Geng

    (School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China)

  • Yue Xu

    (School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

With accelerated urbanisation, continued growth in water demand and the external pressure of water demand from the South–North Water Transfer Project, agricultural water use in Jiangsu is facing a critical situation. Therefore, it is important to explore the spatial and temporal variation in agricultural water use efficiency in order to clarify the pathway for improving agricultural water use efficiency. Firstly, the Super-Slacks-Based Measure (SBM) model was utilized to measure agricultural water use efficiency in Jiangsu Province, China, from 2011 to 2020, and secondly, a fixed-effects model was used to investigate agricultural water use efficiency and the factors influencing it in 13 prefectures in Jiangsu Province in both time and space. The results show that (1) the overall value of agricultural water use efficiency in Jiangsu Province is below 1, which means that agricultural water use efficiency in Jiangsu Province is low and far from the effective boundary, and there is more room for improvement in agricultural water use efficiency; (2) a total of 92% of prefectures in Jiangsu Province have input redundancy, which seriously inhibits the progress of agricultural water use efficiency in Jiangsu Province, among which the redundancy of total agricultural machinery power and agricultural water use is the highest; (3) Regarding total factor productivity and its decomposition index for agricultural use in Jiangsu Province, in the time dimension, the number of professional and technical personnel inputs has a positive impact on agricultural water use efficiency. In the spatial dimension, the number of professional and technical personnel inputs, industrial structure and arable land area have a positive impact on improving regional agricultural water use efficiency, among which the industrial structure has a smaller contribution to agricultural water use efficiency.

Suggested Citation

  • Hongguang Dong & Jie Geng & Yue Xu, 2023. "Receiving Robust Analysis of Spatial and Temporary Variation of Agricultural Water Use Efficiency While Considering Environmental Factors: On the Evaluation of Data Envelopment Analysis Technique," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3926-:d:1075851
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/3926/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/5/3926/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Nguyen Bich Hong & Mitsuyasu Yabe, 2017. "Improvement in irrigation water use efficiency: a strategy for climate change adaptation and sustainable development of Vietnamese tea production," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(4), pages 1247-1263, August.
    3. Druckman, A. & Jackson, T., 2008. "Measuring resource inequalities: The concepts and methodology for an area-based Gini coefficient," Ecological Economics, Elsevier, vol. 65(2), pages 242-252, April.
    4. 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.
    5. Min Li & Kaisheng Long, 2019. "Direct or Spillover Effect: The Impact of Pure Technical and Scale Efficiencies of Water Use on Water Scarcity in China," IJERPH, MDPI, vol. 16(18), pages 1-13, September.
    6. Shuai Qin & Hong Chen & Haokun Wang, 2021. "Spatial–Temporal Heterogeneity and Driving Factors of Rural Residents’ Food Consumption Carbon Emissions in China—Based on an ESDA-GWR Model," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    7. Krishna Malakar & Trupti Mishra & Anand Patwardhan, 2018. "Inequality in water supply in India: an assessment using the Gini and Theil indices," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 841-864, April.
    8. Yanling Zhi & Fan Zhang & Huimin Wang & Teng Qin & Jinping Tong & Ting Wang & Zhiqiang Wang & Jinle Kang & Zhou Fang, 2022. "Agricultural Water Use Efficiency: Is There Any Spatial Correlation between Different Regions?," Land, MDPI, vol. 11(1), pages 1-22, January.
    9. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    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. 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.
    2. Ruiqing Yuan & Xiangyang Xu & Yanli Wang & Jiayi Lu & Ying Long, 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    3. 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.
    4. 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.
    5. Bogetoft, Peter & Leth Hougaard, Jens, 2004. "Super efficiency evaluations based on potential slack," European Journal of Operational Research, Elsevier, vol. 152(1), pages 14-21, January.
    6. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    7. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    8. Xiao Zhang & Di Wang, 2023. "Beyond the Ecological Boundary: A Quasi-Natural Experiment on the Impact of National Marine Parks on Eco-Efficiency in Coastal Cities," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    9. Seyed Rakhshan & Ali Kamyad & Sohrab Effati, 2015. "Ranking decision-making units by using combination of analytical hierarchical process method and Tchebycheff model in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 505-525, March.
    10. Alireza Amirteimoori & Sohrab Kordrostami, 2012. "A distance-based measure of super efficiency in data envelopment analysis: an application to gas companies," Journal of Global Optimization, Springer, vol. 54(1), pages 117-128, September.
    11. Lin, L.C. & Hong, C.H., 2006. "Operational performance evaluation of international major airports: An application of data envelopment analysis," Journal of Air Transport Management, Elsevier, vol. 12(6), pages 342-351.
    12. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    13. Patricija Bajec & Danijela Tuljak-Suban, 2019. "An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    14. Haugland, Sven A. & Myrtveit, Ingunn & Nygaard, Arne, 2007. "Market orientation and performance in the service industry: A data envelopment analysis," Journal of Business Research, Elsevier, vol. 60(11), pages 1191-1197, November.
    15. 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.
    16. Ruiz, Jose L. & Sirvent, Inmaculada, 2001. "Techniques for the assessment of influence in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 390-399, July.
    17. Pelloneová Natalie, 2023. "Evaluating Hockey Players Using Andersen and Petersen's Super-Efficiency Model: Who is the Best Czech Hockey Player in the NHL?," Polish Journal of Sport and Tourism, Sciendo, vol. 30(3), pages 23-28, September.
    18. Alexandr Gedranovich & Mykhaylo Salnykov, 2012. "Productivity analysis of Belarusian higher education system," BEROC Working Paper Series 16, Belarusian Economic Research and Outreach Center (BEROC).
    19. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    20. Suzuki, Soushi & Nijkamp, Peter, 2016. "An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis," Energy Policy, Elsevier, vol. 88(C), pages 100-112.

    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:15:y:2023:i:5:p:3926-:d:1075851. 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.