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Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs

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  • Ming Meng

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Yanan Fu

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Tianyu Wang

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Kaiqiang Jing

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

Abstract

Industrial energy and environment efficiency evaluation become especially crucial as industrial sectors play a key role in CO 2 emission reduction and energy consumption. This study adopts the additive range-adjusted measure data envelope analysis (RAM-DEA) model to estimate the low-carbon economy efficiency of Chinese industrial sectors in 2001–2013. In addition, the CO 2 emission intensity mitigation target for each industrial sector is assigned. Results show that, first, most sectors are not completely efficient, but they have experienced and have improved greatly during the period. These sectors can be divided into four categories, namely, mining, light, heavy, and electricity, gas, and water supply industries. The efficiency is diverse among the four industrial categories. The average efficiency of the light industry is the highest among the industries, followed by those of the mining and the electricity, gas, and water supply industries, and that of the heavy industry is the lowest. Second, the electricity, gas, and water supply industry shows the biggest potential for CO 2 emission reduction, thus containing most of the sectors with large CO 2 emission intensity mitigation targets (more than 45%), followed by the mining and the light industries. Therefore, the Chinese government should formulate diverse and flexible policy implementations according to the actual situation of the different sectors. Specifically, the sectors with low efficiency should be provided with additional policy support (such as technology and finance aids) to improve their industrial efficiency, whereas the electricity, gas, and water supply industry should maximize CO 2 emission reduction.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:451-:d:93422
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    References listed on IDEAS

    as
    1. Qunwei Wang & Peng Zhou & Zengyao Zhao & Neng Shen, 2014. "Energy Efficiency and Energy Saving Potential in China: A Directional Meta-Frontier DEA Approach," Sustainability, MDPI, vol. 6(8), pages 1-17, August.
    2. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    3. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    4. Xie, Bai-Chen & Shang, Li-Feng & Yang, Si-Bo & Yi, Bo-Wen, 2014. "Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countrie," Energy, Elsevier, vol. 74(C), pages 147-157.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation," Energy Economics, Elsevier, vol. 33(2), pages 292-303, March.
    6. Zhang, Yue-Jun & Hao, Jun-Fang & Song, Juan, 2016. "The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level," Applied Energy, Elsevier, vol. 174(C), pages 213-223.
    7. Cherniwchan, Jevan, 2012. "Economic growth, industrialization, and the environment," Resource and Energy Economics, Elsevier, vol. 34(4), pages 442-467.
    8. 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.
    9. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    10. Li, Tianxiang & Baležentis, Tomas & Makutėnienė, Daiva & Streimikiene, Dalia & Kriščiukaitienė, Irena, 2016. "Energy-related CO2 emission in European Union agriculture: Driving forces and possibilities for reduction," Applied Energy, Elsevier, vol. 180(C), pages 682-694.
    11. Wang, Qunwei & Zhou, Peng & Zhou, Dequn, 2012. "Efficiency measurement with carbon dioxide emissions: The case of China," Applied Energy, Elsevier, vol. 90(1), pages 161-166.
    12. Qingyou Yan & Jie Tao, 2014. "Biomass Power Generation Industry Efficiency Evaluation in China," Sustainability, MDPI, vol. 6(12), pages 1-16, December.
    13. Weibin Lin & Bin Chen & Lina Xie & Haoran Pan, 2015. "Estimating Energy Consumption of Transport Modes in China Using DEA," Sustainability, MDPI, vol. 7(4), pages 1-15, April.
    14. Watanabe, Michio & Tanaka, Katsuya, 2007. "Efficiency analysis of Chinese industry: A directional distance function approach," Energy Policy, Elsevier, vol. 35(12), pages 6323-6331, December.
    15. 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.
    16. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    17. 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.
    18. Guijun Li & Daohan Huang & Yulong Li, 2016. "China’s Input-Output Efficiency of Water-Energy-Food Nexus Based on the Data Envelopment Analysis (DEA) Model," Sustainability, MDPI, vol. 8(9), pages 1-16, September.
    19. 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.
    20. Qing Yang & Xingzi Wan & Huimin Ma, 2015. "Assessing Green Development Efficiency of Municipalities and Provinces in China Integrating Models of Super-Efficiency DEA and Malmquist Index," Sustainability, MDPI, vol. 7(4), pages 1-19, April.
    21. Dyckhoff, H. & Allen, K., 2001. "Measuring ecological efficiency with data envelopment analysis (DEA)," European Journal of Operational Research, Elsevier, vol. 132(2), pages 312-325, July.
    22. Reinhard, Stijn & Knox Lovell, C. A. & Thijssen, Geert J., 2000. "Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA," European Journal of Operational Research, Elsevier, vol. 121(2), pages 287-303, March.
    23. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    24. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    25. Dong Zhou & Xingang Zhao, 2015. "Industrial Performance of the Renewable Resources Industry in China," Sustainability, MDPI, vol. 7(9), pages 1-22, August.
    26. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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