IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v190y2020ics0360544219320493.html
   My bibliography  Save this article

Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries

Author

Listed:
  • Qi, Xiaoyan
  • Guo, Pibin
  • Guo, Yanshan
  • Liu, Xiuli
  • Zhou, Xijun

Abstract

Different industries use energy with different levels of efficiency, even within the same industry. The biggest contributes of this study to the prior literature is that measured the energy efficiency by using coal consumption instead of energy consumption and explore the driving factors behind the variations in efficiency of different coal-intensive industries. The study selected 14 major Chinese coal-intensive industries and applied the super-efficiency model of data envelopment analysis to measure the total-factor energy efficiency of coal consumption (TFEEC) from 2006 to 2015. And the study used the Malmquist index to decompose the TFEEC and to analysis the variation situations of the TFEEC. The main results are as follows. (1) The TFEEC overall showed a trend of growth from 2006 to 2015, which showed that the energy saving and emission reduction policies should be further implemented, especially in developing innovative ways and policies to manage waste emissions. (2) In general, the factors causing the change of the TFEEC were mainly from technological progress, the government should encourage the enterprises to carry out energy technology innovation activities from both the energy-demand side and the supply side. However, the efficiency of different industries had different driving factor, the government should adopt different policies and measures to improve the TFEEC for different industries. (3) Half of the coal-intensive industries were still failing to efficiently utilize all of their resources, which showed that China still has large space to improve the TFEEC for different coal-intensive industries.

Suggested Citation

  • Qi, Xiaoyan & Guo, Pibin & Guo, Yanshan & Liu, Xiuli & Zhou, Xijun, 2020. "Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219320493
    DOI: 10.1016/j.energy.2019.116354
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219320493
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116354?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    3. Wei, Chu & Löschel, Andreas & Liu, Bing, 2015. "Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis," Energy Economics, Elsevier, vol. 49(C), pages 33-43.
    4. 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.
    5. Xie, Wei & Sheng, Pengfei & Guo, Xiaohui, 2015. "Coal, oil, or clean energy: Which contributes most to the low energy efficiency in China?," Utilities Policy, Elsevier, vol. 35(C), pages 67-71.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. 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.
    8. Ang, B.W., 2006. "Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index," Energy Policy, Elsevier, vol. 34(5), pages 574-582, March.
    9. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    10. Borozan, Djula, 2018. "Technical and total factor energy efficiency of European regions: A two-stage approach," Energy, Elsevier, vol. 152(C), pages 521-532.
    11. Geller, Howard & Harrington, Philip & Rosenfeld, Arthur H. & Tanishima, Satoshi & Unander, Fridtjof, 2006. "Polices for increasing energy efficiency: Thirty years of experience in OECD countries," Energy Policy, Elsevier, vol. 34(5), pages 556-573, March.
    12. 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.
    13. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    14. Guo, Pibin & Wang, Ting & Li, Dan & Zhou, Xijun, 2016. "How energy technology innovation affects transition of coal resource-based economy in China," Energy Policy, Elsevier, vol. 92(C), pages 1-6.
    15. Yuxiang, Karl & Chen, Zhongchang, 2010. "Government expenditure and energy intensity in China," Energy Policy, Elsevier, vol. 38(2), pages 691-694, February.
    16. Xu, Jiuping & Dai, Jingqi & Xie, Heping & Lv, Chengwei, 2017. "Coal utilization eco-paradigm towards an integrated energy system," Energy Policy, Elsevier, vol. 109(C), pages 370-381.
    17. Zhou, Nan & Levine, Mark D. & Price, Lynn, 2010. "Overview of current energy-efficiency policies in China," Energy Policy, Elsevier, vol. 38(11), pages 6439-6452, November.
    18. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    19. Li, Ke & Lin, Boqiang, 2018. "How to promote energy efficiency through technological progress in China?," Energy, Elsevier, vol. 143(C), pages 812-821.
    20. Moon, Hana & Min, Daiki, 2017. "Assessing energy efficiency and the related policy implications for energy-intensive firms in Korea: DEA approach," Energy, Elsevier, vol. 133(C), pages 23-34.
    21. Boyd, Gale A. & Pang, Joseph X., 2000. "Estimating the linkage between energy efficiency and productivity," Energy Policy, Elsevier, vol. 28(5), pages 289-296, May.
    22. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    23. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73-86, March.
    24. Proskuryakova, L. & Kovalev, A., 2015. "Measuring energy efficiency: Is energy intensity a good evidence base?," Applied Energy, Elsevier, vol. 138(C), pages 450-459.
    25. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    26. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    27. Chen, Wenying & Xu, Ruina, 2010. "Clean coal technology development in China," Energy Policy, Elsevier, vol. 38(5), pages 2123-2130, May.
    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. Liu, Xiuli & Guo, Pibin & Yue, Xiaohang & Qi, Xiaoyan & Guo, Shufeng & Zhou, Xijun, 2021. "Measuring metabolic efficiency of the Beijing–Tianjin–Hebei urban agglomeration: A slacks-based measures method," Resources Policy, Elsevier, vol. 70(C).
    2. Chiu, Yung-ho & Huang, Kuei-Ying & Chang, Tzu-Han & Lin, Tai-Yu, 2021. "Efficiency assessment of coal mine use and land restoration: Considering climate change and income differences," Resources Policy, Elsevier, vol. 73(C).
    3. Wang, Qiang & Song, Xiaoxin, 2021. "Why do China and India burn 60% of the world’s coal? A decomposition analysis from a global perspective," Energy, Elsevier, vol. 227(C).
    4. Lu, Angdi & Zhang, Jiang & Li, Jie, 2023. "The impact of export VAT rebate reduction on firms' pollution emissions: Evidence from Chinese enterprises," Energy Economics, Elsevier, vol. 120(C).
    5. Zhang, Lixiao & Yang, Min & Zhang, Pengpeng & Hao, Yan & Lu, Zhongming & Shi, Zhimin, 2021. "De-coal process in urban China: What can we learn from Beijing's experience?," Energy, Elsevier, vol. 230(C).
    6. Tao Ma & Xiaoxi Cao, 2022. "FDI, technological progress, and green total factor energy productivity: evidence from 281 prefecture cities in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11058-11088, September.
    7. Dehua Zhang & Haiqing Wang & Sha Lou & Shen Zhong, 2021. "Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    8. Honma, Satoshi & Ushifusa, Yoshiaki & Okamura, Soyoka & Vandercamme, Lilu, 2023. "Measuring carbon emissions performance of Japan's metal industry: Energy inputs, agglomeration, and the potential for green recovery reduction," Resources Policy, Elsevier, vol. 82(C).
    9. Zhu, Runqing & Lin, Boqiang, 2021. "Energy and carbon performance improvement in China's mining Industry:Evidence from the 11th and 12th five-year plan," Energy Policy, Elsevier, vol. 154(C).
    10. Liu, Haomin & Zhang, Zaixu & Zhang, Tao & Wang, Liyang, 2020. "Revisiting China’s provincial energy efficiency and its influencing factors," Energy, Elsevier, vol. 208(C).
    11. Mushtaq, Zulqarnain & Wei, Wei & Jamil, Ihsan & Sharif, Maimoona & Chandio, Abbas Ali & Ahmad, Fayyaz, 2022. "Evaluating the factors of coal consumption inefficiency in energy intensive industries of China: An epsilon-based measure model," Resources Policy, Elsevier, vol. 78(C).
    12. Yue Zhao & Jorge Antunes & Yong Tan & Peter Wanke, 2024. "Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi‐activity network data envelopment analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1762-1780, April.
    13. Wei, Wei & Hu, Haiqing & Chang, Chun-Ping, 2022. "Why the same degree of economic policy uncertainty can produce different outcomes in energy efficiency? New evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 467-481.
    14. Qi, Xiaoyan & Guo, Yanshan & Guo, Pibin & Yao, Xilong & Liu, Xiuli, 2022. "Do subsidies and R&D investment boost energy transition performance? Evidence from Chinese renewable energy firms," Energy Policy, Elsevier, vol. 164(C).
    15. Hafidz Wibisono & Jon C. Lovett & Dhimas Bayu Anindito, 2023. "The contestation of ideas behind Indonesia's rural electrification policies: The influence of global and national institutional dynamics," Development Policy Review, Overseas Development Institute, vol. 41(1), January.
    16. Zhao, Zhibo & Shi, Xunpeng & Zhao, Lingdi & Zhang, Jinggu, 2020. "Extending production-theoretical decomposition analysis to environmentally sensitive growth: Case study of Belt and Road Initiative countries," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    17. Tang, Liwei & He, Gang, 2021. "How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China," Energy, Elsevier, vol. 235(C).
    18. Zhang, H. & Fan, L.W. & Zhou, P., 2020. "Handling heterogeneity in frontier modeling of city-level energy efficiency: The case of China," Applied Energy, Elsevier, vol. 279(C).
    19. Andreoni, Valeria, 2022. "Drivers of coal consumption changes: A decomposition analysis for Chinese regions," Energy, Elsevier, vol. 242(C).
    20. Lin, Boqiang & Xu, Bin, 2020. "Effective ways to reduce CO2 emissions from China's heavy industry? Evidence from semiparametric regression models," Energy Economics, Elsevier, vol. 92(C).

    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. 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.
    2. 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.
    3. Bhat, Javed Ahmad & Haider, Salman & Kamaiah, Bandi, 2018. "Interstate energy efficiency of Indian paper industry: A slack-based non-parametric approach," Energy, Elsevier, vol. 161(C), pages 284-298.
    4. Song, Malin & Mangla, Sachin Kumar & Wang, Jianlin & Zhao, Jiajia & An, Jiafu, 2022. "Asymmetric information, “coal-to-gas” transition and coal reduction potential: An analysis using the nonparametric production frontier method," Energy Economics, Elsevier, vol. 114(C).
    5. 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).
    6. Shuangjie Li & Hongyu Diao & Liming Wang & Chunqi Li, 2021. "Energy Efficiency Measurement: A VO TFEE Approach and Its Application," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    7. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    8. 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.
    9. Fan, Di & Peng, Bo & Wu, Jianxin & Zhang, ZhongXiang, 2024. "The convergence of total-factor energy efficiency across Chinese cities: A distribution dynamics approach," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 406-416.
    10. Du, Minzhe & Wang, Bing & Zhang, Ning, 2018. "National research funding and energy efficiency: Evidence from the National Science Foundation of China," Energy Policy, Elsevier, vol. 120(C), pages 335-346.
    11. Li, Ke & Lin, Boqiang, 2015. "The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model," Energy, Elsevier, vol. 84(C), pages 589-599.
    12. Ding, Li-Li & Lei, Liang & Zhao, Xin & Calin, Adrian Cantemir, 2020. "Modelling energy and carbon emission performance: A constrained performance index measure," Energy, Elsevier, vol. 197(C).
    13. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    14. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    15. Blomberg, Jerry & Henriksson, Eva & Lundmark, Robert, 2012. "Energy efficiency and policy in Swedish pulp and paper mills: A data envelopment analysis approach," Energy Policy, Elsevier, vol. 42(C), pages 569-579.
    16. Li, Ke & Lin, Boqiang, 2015. "The efficiency improvement potential for coal, oil and electricity in China's manufacturing sectors," Energy, Elsevier, vol. 86(C), pages 403-413.
    17. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    18. 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.
    19. Bian, Yiwen & Yang, Feng, 2010. "Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon's entropy," Energy Policy, Elsevier, vol. 38(4), pages 1909-1917, April.
    20. Wang, Zhaohua & Feng, Chao & Zhang, Bin, 2014. "An empirical analysis of China's energy efficiency from both static and dynamic perspectives," Energy, Elsevier, vol. 74(C), pages 322-330.

    More about this item

    Keywords

    Coal consumption; Total-factor energy efficiency; Malmquist index; China; DEA;
    All these keywords.

    JEL classification:

    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

    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:eee:energy:v:190:y:2020:i:c:s0360544219320493. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.