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China's regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation


  • Ke Wang
  • Shiwei Yu
  • Wei Zhang


Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environment performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analysis China's regional total-factor energy and environment efficiency. This paper utilizes improved DEA models to measure the energy and environment efficiency of 29 administrative regions of China during the period of 2000 to 2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that east area of China has the highest energy and environmental efficiency, while the efficiency of west area is worst. All three areas of China have similar trend on the variation of efficiency and in general the energy and environment efficiency of China slightly increased from 2000 to 2008. The regions of east area have a more balanced development than the regions of central area and west area according to energy and environment efficiency.

Suggested Citation

  • Ke Wang & Shiwei Yu & Wei Zhang, 2011. "China's regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation," CEEP-BIT Working Papers 17, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  • Handle: RePEc:biw:wpaper:17

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    Cited by:

    1. Wang, Ke & Lu, Bin & Wei, Yi-Ming, 2013. "China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis," Applied Energy, Elsevier, vol. 112(C), pages 1403-1415.
    2. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    3. Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    4. Chia-Nan Wang & Hong-Xuyen Thi Ho & Ming-Hsien Hsueh, 2017. "An Integrated Approach for Estimating the Energy Efficiency of Seventeen Countries," Energies, MDPI, vol. 10(10), pages 1-16, October.
    5. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    6. 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.
    7. Zhi-Fu Mi & Yi-Ming Wei & Chen-Qi He & Hua-Nan Li & Xiao-Chen Yuan & Hua Liao, 2017. "Regional efforts to mitigate climate change in China: a multi-criteria assessment approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(1), pages 45-66, January.
    8. Huang, Chin-wei & Chiu, Yung-ho & Fang, Wei-ta & Shen, Neng, 2014. "Assessing the performance of Taiwan’s environmental protection system with a non-radial network DEA approach," Energy Policy, Elsevier, vol. 74(C), pages 547-556.
    9. Gang Tian & Jian Shi & Licheng Sun & Xingle Long & Benhai Guo, 2017. "Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach," 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. 89(2), pages 585-607, November.
    10. Qiong Xia & Min Li & Huaqing Wu & Zhenggang Lu, 2016. "Does the Central Government’s Environmental Policy Work? Evidence from the Provincial-Level Environment Efficiency in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    11. Ming Meng & Yanan Fu & Tianyu Wang & Kaiqiang Jing, 2017. "Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs," Sustainability, MDPI, vol. 9(3), pages 1-18, March.
    12. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    13. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    14. Zuoxiang Zhao, 2017. "Measurement of production efficiency and environmental efficiency in China’s province-level: a by-production approach," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(4), pages 735-759, October.
    15. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    16. 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.
    17. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2012. "A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?," Energy Policy, Elsevier, vol. 46(C), pages 574-584.
    18. Xiaoli, Zhao & Rui, Yang & Qian, Ma, 2014. "China's total factor energy efficiency of provincial industrial sectors," Energy, Elsevier, vol. 65(C), pages 52-61.
    19. Milin Lu & Zhaohua Wang, 2017. "Rebound effects for residential electricity use in urban China: an aggregation analysis based E-I-O and scenario simulation," Annals of Operations Research, Springer, vol. 255(1), pages 525-546, August.
    20. Mihail Nikolaevich Dudin & Nikolaj Vasilevich Lyasnikov & Vladimir Dmitriyevich Sekerin & Anna Evgenevna Gorohova & Vyacheslav Viktorovich Burlakov, 2016. "Provision of Energy Security at the National Level in the Context of the Global Gas Transportation Industry Development," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 234-242.
    21. Yelena Vechkinzova & Yelena Petrenko & Yelena Petrenko & Stanislav Benčič & Dmitriy Ulybyshev & Dmitriy Ulybyshev & Yerlan Zhailauov & Yerlan Zhailauov, 2019. "Evaluation of regional innovation systems performance using Data Envelopment Analysis (DEA)," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(1), pages 498-509, September.
    22. Linlin Zhao & Yong Zha & Kangning Wei & Liang Liang, 2017. "A target-based method for energy saving and carbon emissions reduction in China based on environmental data envelopment analysis," Annals of Operations Research, Springer, vol. 255(1), pages 277-300, August.

    More about this item


    CO2 emissions; DEA; Dynamic evaluation; Energy and Environmental efficiency; Undesirable outputs; Window analysis;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis


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