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Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model

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  • Liao, Nuo
  • He, Yong

Abstract

The various industrial sub-sectors in China should require differentiated policy measures on energy conservation and emission reduction. The existing literature lack comprehensive and in-depth investigation on the influencing factors of energy efficiency at the level of industrial sub-sectors. We aim to examine the disparities in energy efficiency and the driving factors of the energy efficiency among industrial sub-sectors. Using super-SBM, cluster analysis and panel regression model, we explore the effects of influencing factors on energy efficiency across the industrial sub-sectors, and examines the entity effects and dynamic evolving characteristics of energy efficiency in various sub-sectors in China from 2005 to 2011. The results indicate that, there indeed exist significant disparities in energy efficiency across the sub-sectors. In the entire industry, technological progress, energy consumption structure and enterprise scale are the determining factors, but marketization degree and labor productivity do not have significant effects. Meanwhile, obvious variations are found in the effects of influencing factors across sub-sectors with different energy efficiency levels. The entity effects and evolving characteristics of energy efficiency across the industrial sub-sectors, and the dynamic effects of the influencing factors on energy efficiency all have distinct variances. Diverse policy implications are presented to the industrial sub-sectors with different energy efficiency.

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  • Liao, Nuo & He, Yong, 2018. "Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model," Energy, Elsevier, vol. 158(C), pages 782-795.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:782-795
    DOI: 10.1016/j.energy.2018.06.049
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    1. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    2. Norman, Jonathan B., 2017. "Measuring improvements in industrial energy efficiency: A decomposition analysis applied to the UK," Energy, Elsevier, vol. 137(C), pages 1144-1151.
    3. Wu, Libo & Huo, Hong, 2014. "Energy efficiency achievements in China׳s industrial and transport sectors: How do they rate?," Energy Policy, Elsevier, vol. 73(C), pages 38-46.
    4. Wade D. Cook & Joe Zhu, 2015. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 2, pages 23-43, Springer.
    5. Han, Yongming & Geng, Zhiqiang & Zhu, Qunxiong & Qu, Yixin, 2015. "Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry," Energy, Elsevier, vol. 83(C), pages 685-695.
    6. Hochman, Gal & Timilsina, Govinda R., 2017. "Energy efficiency barriers in commercial and industrial firms in Ukraine: An empirical analysis," Energy Economics, Elsevier, vol. 63(C), pages 22-30.
    7. Patrik Thollander & Jenny Palm, 2015. "Industrial Energy Management Decision Making for Improved Energy Efficiency—Strategic System Perspectives and Situated Action in Combination," Energies, MDPI, vol. 8(6), pages 1-10, June.
    8. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    9. Yong He & Siwei Gao & Nuo Liao, 2016. "An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 399-423, October.
    10. Özkara, Yücel & Atak, Mehmet, 2015. "Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey," Energy, Elsevier, vol. 93(P1), pages 495-510.
    11. 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.
    12. Ang, B.W. & Mu, A.R. & Zhou, P., 2010. "Accounting frameworks for tracking energy efficiency trends," Energy Economics, Elsevier, vol. 32(5), pages 1209-1219, September.
    13. Trianni, Andrea & Cagno, Enrico & Farné, Stefano, 2016. "Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises," Applied Energy, Elsevier, vol. 162(C), pages 1537-1551.
    14. 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.
    15. Peng, Lihong & Zhang, Yiting & Wang, Yejun & Zeng, Xiaoling & Peng, Najun & Yu, Ang, 2015. "Energy efficiency and influencing factor analysis in the overall Chinese textile industry," Energy, Elsevier, vol. 93(P1), pages 1222-1229.
    16. 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.
    17. Fang, Hsin-Hsiung & Lee, Hsuan-Shih & Hwang, Shiuh-Nan & Chung, Cheng-Chi, 2013. "A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach," Omega, Elsevier, vol. 41(4), pages 731-734.
    18. Rohdin, P. & Thollander, P., 2006. "Barriers to and driving forces for energy efficiency in the non-energy intensive manufacturing industry in Sweden," Energy, Elsevier, vol. 31(12), pages 1836-1844.
    19. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    20. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    21. Ang, B.W. & Xu, X.Y., 2013. "Tracking industrial energy efficiency trends using index decomposition analysis," Energy Economics, Elsevier, vol. 40(C), pages 1014-1021.
    22. Xu, Bin & Lin, Boqiang, 2018. "Assessing the development of China's new energy industry," Energy Economics, Elsevier, vol. 70(C), pages 116-131.
    23. Oikonomou, V. & Becchis, F. & Steg, L. & Russolillo, D., 2009. "Energy saving and energy efficiency concepts for policy making," Energy Policy, Elsevier, vol. 37(11), pages 4787-4796, November.
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