IDEAS home Printed from https://ideas.repec.org/p/biw/wpaper/17.html
   My bibliography  Save this paper

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

Author

Listed:
  • Ke Wang
  • Shiwei Yu
  • Wei Zhang

Abstract

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
    as

    Download full text from publisher

    File URL: http://ceep.bit.edu.cn/docs/2018-10/20181011134626118070.pdf
    Download Restriction: no
    ---><---

    Citations

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


    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. 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.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    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

    Keywords

    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

    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:biw:wpaper:17. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Zhi-Fu Mi (email available below). General contact details of provider: https://edirc.repec.org/data/cebitcn.html .

    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.