IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i3p451-d93422.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/3/451/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/3/451/
    Download Restriction: no
    ---><---

    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.
    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. Weiguang Chen & Qing Guo, 2017. "Assessing the Effect of Carbon Tariffs on International Trade and Emission Reduction of China’s Industrial Products under the Background of Global Climate Governance," Sustainability, MDPI, vol. 9(6), pages 1-17, June.
    2. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    3. Tianyi Zeng & Hong Jin & Zhifei Geng & Zihang Kang & Zichen Zhang, 2022. "The Effect of Urban Shrinkage on Carbon Dioxide Emissions Efficiency in Northeast China," IJERPH, MDPI, vol. 19(9), pages 1-18, May.
    4. Derek Wang & Tianchi Li, 2018. "Carbon Emission Performance of Independent Oil and Natural Gas Producers in the United States," Sustainability, MDPI, vol. 10(1), pages 1-18, 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. 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.
    3. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    4. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    5. 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.
    6. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    7. 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).
    8. Adler, Nicole & Volta, Nicola, 2016. "Accounting for externalities and disposability: A directional economic environmental distance function," European Journal of Operational Research, Elsevier, vol. 250(1), pages 314-327.
    9. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    10. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    11. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    12. Noor Ramli & Susila Munisamy & Behrouz Arabi, 2013. "Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector," Annals of Operations Research, Springer, vol. 211(1), pages 381-398, December.
    13. Qingxian An & Haoxun Chen & Jie Wu & Liang Liang, 2015. "Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output," Annals of Operations Research, Springer, vol. 235(1), pages 13-35, December.
    14. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    15. 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.
    16. Harald Dyckhoff & Rainer Souren, 2023. "Are important phenomena of joint production still being neglected by economic theory? A review of recent literature," Journal of Business Economics, Springer, vol. 93(6), pages 1015-1053, August.
    17. Lu, Ching-Cheng & Chiu, Yung-Ho & Shyu, Ming-Kuang & Lee, Jen-Hui, 2013. "Measuring CO2 emission efficiency in OECD countries: Application of the Hybrid Efficiency model," Economic Modelling, Elsevier, vol. 32(C), pages 130-135.
    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. Zhou, Yan & Xing, Xinpeng & Fang, Kuangnan & Liang, Dapeng & Xu, Chunlin, 2013. "Environmental efficiency analysis of power industry in China based on an entropy SBM model," Energy Policy, Elsevier, vol. 57(C), pages 68-75.
    20. Kiril Simeonovski & Tamara Kaftandzieva & Gregory Brock, 2021. "Energy Efficiency Management across EU Countries: A DEA Approach," Energies, MDPI, vol. 14(9), pages 1-19, May.

    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:gam:jsusta:v:9:y:2017:i:3:p:451-:d:93422. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.