IDEAS home Printed from https://ideas.repec.org/a/eee/recore/v127y2017icp21-28.html
   My bibliography  Save this article

The shadow prices and demand elasticities of agricultural water in China: A StoNED-based analysis

Author

Listed:
  • Shen, Xiaobo
  • Lin, Boqiang

Abstract

Based on stochastic nonparametric envelopment of data, this paper estimates the shadow prices of agricultural water and the technical efficiencies, using an input-output data of 30 provincial units in Mainland China from 2002 to 2012. The results show that the average shadow price estimates for agricultural water range between 2.57 yuan/m3 and 3.88 yuan/m3; the estimated price elasticity of agricultural water is 0.12, and that improving technical efficiencies of the agricultural sector has a significant effect on the water demand. That means the prospect of reducing the amount of agricultural water depends on the improvement of technical efficiency and the spread of water-saving irrigation techniques.

Suggested Citation

  • Shen, Xiaobo & Lin, Boqiang, 2017. "The shadow prices and demand elasticities of agricultural water in China: A StoNED-based analysis," Resources, Conservation & Recycling, Elsevier, vol. 127(C), pages 21-28.
    • Handle: RePEc:eee:recore:v:127:y:2017:i:c:p:21-28
    DOI: 10.1016/j.resconrec.2017.08.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921344917302495
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resconrec.2017.08.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ziolkowska, Jadwiga R., 2015. "Shadow price of water for irrigation—A case of the High Plains," Agricultural Water Management, Elsevier, vol. 153(C), pages 20-31.
    2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    3. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, July.
    4. Hu, Zhineng & Chen, Yazhen & Yao, Liming & Wei, Changting & Li, Chaozhi, 2016. "Optimal allocation of regional water resources: From a perspective of equity–efficiency tradeoff," Resources, Conservation & Recycling, Elsevier, vol. 109(C), pages 102-113.
    5. Dan Rigby & Francisco Alcon & Michael Burton, 2010. "Supply uncertainty and the economic value of irrigation water," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(1), pages 97-117, March.
    6. Huang, Qiuqiong & Rozelle, Scott & Howitt, Richard & Wang, Jinxia & Huang, Jikun, 2010. "Irrigation water demand and implications for water pricing policy in rural China," Environment and Development Economics, Cambridge University Press, vol. 15(3), pages 293-319, June.
    7. Gao, Hongchao & Wei, Tong & Lou, Inchio & Yang, Zhifeng & Shen, Zhenyao & Li, Yingxia, 2014. "Water saving effect on integrated water resource management," Resources, Conservation & Recycling, Elsevier, vol. 93(C), pages 50-58.
    8. Bithas, Kostas, 2008. "The sustainable residential water use: Sustainability, efficiency and social equity. The European experience," Ecological Economics, Elsevier, vol. 68(1-2), pages 221-229, December.
    9. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    10. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    11. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Buttinelli, Rebecca & Cortignani, Raffaele & Caracciolo, Francesco, 2024. "Irrigation water economic value and productivity: An econometric estimation for maize grain production in Italy," Agricultural Water Management, Elsevier, vol. 295(C).
    2. Yubing Wang & Kai Zhu & Xiao Xiong & Jianuo Yin & Haoran Yan & Yuan Zhang & Hai Liu, 2022. "Assessment of the Ecological Compensation Standards for Cross-Basin Water Diversion Projects from the Perspective of Main Headwater and Receiver Areas," IJERPH, MDPI, vol. 20(1), pages 1-31, December.
    3. Tian, Guiliang & Wu, Xuan & Zhao, Qiuya & Li, Jiawen & Zhu, Mengqiu, 2024. "The impact of integrated agricultural water pricing reform on farmers' income in China," Agricultural Water Management, Elsevier, vol. 299(C).
    4. Alexander Arévalo S & Víctor Giménez G & Diego Prior J, 2022. "Análisis de eficiencia en educación: una aplicación del método StoNED," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 92(2), pages 45-91, October.
    5. Chebil, Ali & Soula, Rania & Souissi, Asma & Bennouna, Bechir, 2022. "Efficiency, valuation, and pricing of irrigation water in northeastern Tunisia," Agricultural Water Management, Elsevier, vol. 266(C).
    6. Zihan Guo & Ni Wang & Xiaolian Mao & Xinyue Ke & Shaojiang Luo & Long Yu, 2022. "Benefit Analysis of Economic and Social Water Supply in Xi’an Based on the Emergy Method," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    7. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "A comparison of chance-constrained data envelopment analysis, stochastic nonparametric envelopment of data and bootstrap method: A case study of cultural regeneration performance of cities," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1179-1191.
    8. Huaicheng Li & Qing He & Chenming Liu & Wei Dai & Rilong Fei, 2022. "How to Maintain Sustainable Development of China’s Agriculture under the Restriction of Production Resources? Research with Respect to the Effect on Output of the Substitution of Input Factors," Energies, MDPI, vol. 15(10), pages 1-19, May.
    9. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(C).

    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. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    2. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
    3. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    4. Stefan Seifert, 2016. "Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation," Discussion Papers of DIW Berlin 1571, DIW Berlin, German Institute for Economic Research.
    5. Lee, Chia-Yen & Wang, Ke, 2019. "Nash marginal abatement cost estimation of air pollutant emissions using the stochastic semi-nonparametric frontier," European Journal of Operational Research, Elsevier, vol. 273(1), pages 390-400.
    6. Xian, Yujiao & Yu, Dan & Wang, Ke & Yu, Jian & Huang, Zhimin, 2022. "Capturing the least costly measure of CO2 emission abatement: Evidence from the iron and steel industry in China," Energy Economics, Elsevier, vol. 106(C).
    7. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
    8. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
    9. Jradi, Samah & Ruggiero, John, 2019. "Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier," European Journal of Operational Research, Elsevier, vol. 278(2), pages 385-393.
    10. Chung, William & Yeung, Iris M.H., 2017. "Benchmarking by convex non-parametric least squares with application on the energy performance of office buildings," Applied Energy, Elsevier, vol. 203(C), pages 454-462.
    11. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
    12. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    13. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    14. Mekaroonreung, Maethee & Johnson, Andrew L., 2014. "A nonparametric method to estimate a technical change effect on marginal abatement costs of U.S. coal power plants," Energy Economics, Elsevier, vol. 46(C), pages 45-55.
    15. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    16. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    17. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    18. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    19. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    20. De la Cruz, Marco & Mergoni, Anna, 2024. "Assessing the performance of Peruvian education system from a governance perspective," Socio-Economic Planning Sciences, Elsevier, vol. 93(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:eee:recore:v:127:y:2017:i:c:p:21-28. 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: Kai Meng (email available below). General contact details of provider: https://www.journals.elsevier.com/resources-conservation-and-recycling .

    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.