IDEAS home Printed from
   My bibliography  Save this article

Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials


  • Zhihai Yang

    () (College of Economics and Management, Huazhong Agricultural University, No. 1 Shizishan Street, Wuhan 430070, China
    These authors contributed equally to this work.)

  • Dong Wang

    () (UWA School of Agriculture and Environment, The University of Western Australia, M089, 35 Stirling Highway, Crawley, WA 6009, Australia
    Australia-China Sustainable Research and Development Center, Perth, WA 6009, Australia
    Inner Mongolia Honghe Energy and Environment Consultancy, Huhhot 010020, China
    These authors contributed equally to this work.)

  • Tianyi Du

    () (School of Business and Management, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
    Australia-China Sustainable Research and Development Center, Perth, WA 6009, Australia)

  • Anlu Zhang

    () (College of Land Management, Huazhong Agricultural University, No. 1 Shizishan Street, Wuhan 430070, China)

  • Yixiao Zhou

    () (School of Economics and Finance, Faculty of Business and Law, Curtin University, Rm 3016, Bldg 408, Bentley, Perth, WA 6004, Australia)


This paper investigates total-factor energy efficiency and analyses the trends of the efficiency changes in China’s agricultural production across 30 provinces and three regions from 2001 to 2011, based on data envelopment analysis (DEA) approach. The potential amount of energy savings and five potential factors for energy efficiency improvement are also empirically studied by Tobit regression model. The findings show that (1) total-factor energy efficiency in China’s agricultural sector is increasing over years but performs heterogeneously across regions; (2) agriculture intensive regions and energy abundant provinces tend to be relatively energy inefficient in agricultural production; and (3) economic structure, agricultural production structure, technological progress and income effect are major potentials for improving energy efficiency, whereas energy price is not a significant factor. This phenomenon results from the divergence of economic development, endowment effects as well as the scale of agricultural production. Policy implications drawn from this research are to upgrade industrial structure and promote agricultural transformation to enhance farmers’ income as well as to establish a land market with entitling land property rights to farmers. This conclusion can assist to form more scientific rural energy policy decision-making in China and also can be extended to other developing economies for sustainable agriculture.

Suggested Citation

  • Zhihai Yang & Dong Wang & Tianyi Du & Anlu Zhang & Yixiao Zhou, 2018. "Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-16, April.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:853-:d:139707

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Yanrui Wu, 2016. "China's Capital Stock Series by Region and Sector," Frontiers of Economics in China, Higher Education Press, vol. 11(1), pages 156-172, March.
    2. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.
    3. Wang, Dong, 2014. "A dynamic optimization on economic energy efficiency in development: A numerical case of China," Energy, Elsevier, vol. 66(C), pages 181-188.
    4. Song, Feng & Zheng, Xinye, 2012. "What drives the change in China's energy intensity: Combining decomposition analysis and econometric analysis at the provincial level," Energy Policy, Elsevier, vol. 51(C), pages 445-453.
    5. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    6. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    7. Karkacier, Osman & Gokalp Goktolga, Z. & Cicek, Adnan, 2006. "A regression analysis of the effect of energy use in agriculture," Energy Policy, Elsevier, vol. 34(18), pages 3796-3800, December.
    8. Khoshroo, Alireza & Mulwa, Richard & Emrouznejad, Ali & Arabi, Behrouz, 2013. "A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production," Energy, Elsevier, vol. 63(C), pages 189-194.
    9. Fan, Ying & Xia, Yan, 2012. "Exploring energy consumption and demand in China," Energy, Elsevier, vol. 40(1), pages 23-30.
    10. Chu Wei & Jinlan Ni & Manhong Shen, 2009. "Empirical Analysis of Provincial Energy Efficiency in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 17(5), pages 88-103, September.
    11. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    12. Catania, Peter, 1999. "China's rural energy system and management," Applied Energy, Elsevier, vol. 64(1-4), pages 229-240, September.
    13. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    14. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    15. Unakitan, G. & Hurma, H. & Yilmaz, F., 2010. "An analysis of energy use efficiency of canola production in Turkey," Energy, Elsevier, vol. 35(9), pages 3623-3627.
    16. Rahman, Sanzidur & Rahman, Md. Sayedur, 2013. "Energy productivity and efficiency of maize accounting for the choice of growing season and environmental factors: An empirical analysis from Bangladesh," Energy, Elsevier, vol. 49(C), pages 329-336.
    17. Zaman, Khalid & Khan, Muhammad Mushtaq & Ahmad, Mehboob & Rustam, Rabiah, 2012. "The relationship between agricultural technology and energy demand in Pakistan," Energy Policy, Elsevier, vol. 44(C), pages 268-279.
    18. Pahlavan, Reza & Omid, Mahmoud & Akram, Asadollah, 2011. "Energy use efficiency in greenhouse tomato production in Iran," Energy, Elsevier, vol. 36(12), pages 6714-6719.
    19. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    20. Sebri, Maamar & Abid, Mehdi, 2012. "Energy use for economic growth: A trivariate analysis from Tunisian agriculture sector," Energy Policy, Elsevier, vol. 48(C), pages 711-716.
    21. Zhang, Lixiao & Yang, Zhifeng & Chen, Bin & Chen, Guoqian, 2009. "Rural energy in China: Pattern and policy," Renewable Energy, Elsevier, vol. 34(12), pages 2813-2823.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Chang, Ming-Chung, 2020. "An application of total-factor energy efficiency under the metafrontier framework," Energy Policy, Elsevier, vol. 142(C).
    2. Yanqiu He & Xueying Cheng & Fang Wang & Ya Cheng, 2020. "Spatial correlation of China’s agricultural greenhouse gas emissions: a technology spillover perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2561-2590, December.
    3. Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
    4. Yiru Guo & Yan Hu & Ke Shi & Yuriy Bilan, 2020. "Valuation of Water Resource Green Efficiency Based on SBM–TOBIT Panel Model: Case Study from Henan Province, China," Sustainability, MDPI, Open Access Journal, vol. 12(17), pages 1-1, August.
    5. Jara Laso & Daniel Hoehn & María Margallo & Isabel García-Herrero & Laura Batlle-Bayer & Alba Bala & Pere Fullana-i-Palmer & Ian Vázquez-Rowe & Angel Irabien & Rubén Aldaco, 2018. "Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology," Energies, MDPI, Open Access Journal, vol. 11(12), pages 1-18, December.
    6. Xiaoyang Zhou & Hao Chen & Hao Wang & Benjamin Lev & Lifang Quan, 2019. "Natural and Managerial Disposability Based DEA Model for China’s Regional Environmental Efficiency Assessment," Energies, MDPI, Open Access Journal, vol. 12(18), pages 1-20, September.
    7. Tao Yi & Ling Tong & Mohan Qiu & Jinpeng Liu, 2019. "Analysis of Driving Factors of Photovoltaic Power Generation Efficiency: A Case Study in China," Energies, MDPI, Open Access Journal, vol. 12(3), pages 1-15, January.

    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. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    2. Li, Jianglong & Lin, Boqiang, 2017. "Ecological total-factor energy efficiency of China's heavy and light industries: Which performs better?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 83-94.
    3. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    4. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    5. Hang, Ye & Sun, Jiasen & Wang, Qunwei & Zhao, Zengyao & Wang, Yizhong, 2015. "Measuring energy inefficiency with undesirable outputs and technology heterogeneity in Chinese cities," Economic Modelling, Elsevier, vol. 49(C), pages 46-52.
    6. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    7. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.
    8. Jiang, Xuemei & Zhu, Kunfu & Green, Christopher, 2015. "The energy efficiency advantage of foreign-invested enterprises in China and the role of structural differences," China Economic Review, Elsevier, vol. 34(C), pages 225-235.
    9. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    10. Li, Nan & Jiang, Yuqing & Mu, Hailin & Yu, Zhixin, 2018. "Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA)," Energy, Elsevier, vol. 164(C), pages 1145-1160.
    11. Fei, Rilong & Lin, Boqiang, 2016. "Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 25-34.
    12. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    13. Ke Wang & Xueying Yu, 2017. "Industrial Energy and Environment Efficiency of Chinese Cities: An Analysis Based on Range-Adjusted Measure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1023-1042, July.
    14. Zhang, Yue-Jun & Sun, Ya-Fang & Huang, Junling, 2018. "Energy efficiency, carbon emission performance, and technology gaps: Evidence from CDM project investment," Energy Policy, Elsevier, vol. 115(C), pages 119-130.
    15. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    16. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    17. Chang, Ming-Chung, 2016. "Applying the energy productivity index that considers maximized energy reduction on SADC (Southern Africa Development Community) members," Energy, Elsevier, vol. 95(C), pages 313-323.
    18. Changhong Zhao & Haonan Zhang & Yurong Zeng & Fengyun Li & Yuanxin Liu & Chengju Qin & Jiahai Yuan, 2018. "Total-Factor Energy Efficiency in BRI Countries: An Estimation Based on Three-Stage DEA Model," Sustainability, MDPI, Open Access Journal, vol. 10(1), pages 1-15, January.
    19. Sueyoshi, Toshiyuki & Yuan, Yan, 2015. "China's regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution," Energy Economics, Elsevier, vol. 49(C), pages 239-256.
    20. Georgia Makridou, Kostas Andriosopoulos, Michael Doumpos, and Constantin Zopounidis, 2015. "A Two-stage approach for energy efficiency analysis in European Union countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).

    More about this item


    energy efficiency; data envelopment analysis; agriculture transformation;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other


    Access and download statistics


    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:jeners:v:11:y:2018:i:4:p:853-:d:139707. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.