IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v189y2019ics0360544219319759.html
   My bibliography  Save this article

Quantitative assessment of factors affecting energy intensity from sector, region and time perspectives using decomposition method: A case of China’s metallurgical industry

Author

Listed:
  • Lin, Boqiang
  • Xu, Mengmeng

Abstract

As a typical high-energy consumption industry, the metallurgical industry has brought great challenges to energy saving and emission reduction in the whole China. Reducing the energy intensity of this crucial sector is of great importance to China’s green transition. This paper seeks to determine the drivers of the energy intensity change in China’s metallurgical industry during 2000–2016. To serve this purpose, we construct a comprehensive framework which combines Index Decomposition Analysis (IDA) and Production Decomposition Analysis (PDA) methods to fully decompose the change of energy intensity, and in this way more complete and in-depth insights can be provided. The results showed that the technological progress effect made the greatest contribution to the decline in energy intensity, with a cumulative reduction of 72.32%, while the labor-energy substitution is the greatest obstacle to the energy intensity reduction. As to the provincial contribution, except for Xinjiang and Inner Mongolia, all the other provinces make positive contributions to the energy intensity reduction in China’s metallurgical industry. Therein, Hebei, Hunan and Shannxi provinces rank the top one contributor in Eastern, Central, and Western China, respectively. Based on the empirical results, some targeted recommendations to formulate energy-saving policies for China’s metallurgical industry are put forward.

Suggested Citation

  • Lin, Boqiang & Xu, Mengmeng, 2019. "Quantitative assessment of factors affecting energy intensity from sector, region and time perspectives using decomposition method: A case of China’s metallurgical industry," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319759
    DOI: 10.1016/j.energy.2019.116280
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219319759
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116280?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. Lin, Boqiang & Xu, Mengmeng, 2019. "Good subsidies or bad subsidies? Evidence from low-carbon transition in China's metallurgical industry," Energy Economics, Elsevier, vol. 83(C), pages 52-60.
    2. Xiao, Hao & Sun, Ke-Juan & Bi, Hui-Min & Xue, Jin-Jun, 2019. "Changes in carbon intensity globally and in countries: Attribution and decomposition analysis," Applied Energy, Elsevier, vol. 235(C), pages 1492-1504.
    3. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    4. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    5. Liyun Chen & Qi Duan, 2016. "Decomposition analysis of factors driving CO2 emissions in Chinese provinces based on production-theoretical decomposition analysis," 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. 84(1), pages 267-277, November.
    6. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    7. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    8. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei, 2018. "How does information and communication technology affect China's energy intensity? A three-tier structural decomposition analysis," Energy, Elsevier, vol. 151(C), pages 748-759.
    9. Baležentis, Alvydas & Baležentis, Tomas & Streimikiene, Dalia, 2011. "The energy intensity in Lithuania during 1995–2009: A LMDI approach," Energy Policy, Elsevier, vol. 39(11), pages 7322-7334.
    10. Lin, Boqiang & Du, Zhili, 2017. "Promoting energy conservation in China's metallurgy industry," Energy Policy, Elsevier, vol. 104(C), pages 285-294.
    11. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    12. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    13. Chong, ChinHao & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou & Li, Xu & Song, Shizhong, 2017. "LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram," Energy, Elsevier, vol. 133(C), pages 525-544.
    14. Lin, Boqiang & Xu, Mengmeng, 2018. "Regional differences on CO2 emission efficiency in metallurgical industry of China," Energy Policy, Elsevier, vol. 120(C), pages 302-311.
    15. Zha, Donglan & Zhou, Dequn & Ding, Ning, 2009. "The contribution degree of sub-sectors to structure effect and intensity effects on industry energy intensity in China from 1993 to 2003," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 895-902, May.
    16. Wang, W.W. & Zhang, M. & Zhou, M., 2011. "Using LMDI method to analyze transport sector CO2 emissions in China," Energy, Elsevier, vol. 36(10), pages 5909-5915.
    17. Xu, Jin-Hua & Fleiter, Tobias & Eichhammer, Wolfgang & Fan, Ying, 2012. "Energy consumption and CO2 emissions in China's cement industry: A perspective from LMDI decomposition analysis," Energy Policy, Elsevier, vol. 50(C), pages 821-832.
    18. Wachsmann, Ulrike & Wood, Richard & Lenzen, Manfred & Schaeffer, Roberto, 2009. "Structural decomposition of energy use in Brazil from 1970 to 1996," Applied Energy, Elsevier, vol. 86(4), pages 578-587, April.
    19. Cansino, José M. & Sánchez-Braza, Antonio & Rodríguez-Arévalo, María L., 2015. "Driving forces of Spain׳s CO2 emissions: A LMDI decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 749-759.
    20. Wang, Chunhua, 2007. "Decomposing energy productivity change: A distance function approach," Energy, Elsevier, vol. 32(8), pages 1326-1333.
    21. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    22. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    23. 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.
    24. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    25. Huang, Junbing & Du, Dan & Hao, Yu, 2017. "The driving forces of the change in China's energy intensity: An empirical research using DEA-Malmquist and spatial panel estimations," Economic Modelling, Elsevier, vol. 65(C), pages 41-50.
    26. Wang, Wenwen & Liu, Xiao & Zhang, Ming & Song, Xuefeng, 2014. "Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China's energy consumption," Energy, Elsevier, vol. 67(C), pages 617-622.
    27. Xie, Xuan & Lin, Boqiang, 2019. "Understanding the energy intensity change in China's food industry: A comprehensive decomposition method," Energy Policy, Elsevier, vol. 129(C), pages 53-68.
    28. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    29. Wang, Chunhua, 2011. "Sources of energy productivity growth and its distribution dynamics in China," Resource and Energy Economics, Elsevier, vol. 33(1), pages 279-292, January.
    30. Lin, Boqiang & Moubarak, Mohamed, 2014. "Estimation of energy saving potential in China's paper industry," Energy, Elsevier, vol. 65(C), pages 182-189.
    31. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    32. Su, Bin & Thomson, Elspeth, 2016. "China's carbon emissions embodied in (normal and processing) exports and their driving forces, 2006–2012," Energy Economics, Elsevier, vol. 59(C), pages 414-422.
    33. Deng, Guangyao & Xu, Yan, 2017. "Accounting and structure decomposition analysis of embodied carbon trade: A global perspective," Energy, Elsevier, vol. 137(C), pages 140-151.
    34. Wood, Richard & Lenzen, Manfred, 2006. "Zero-value problems of the logarithmic mean divisia index decomposition method," Energy Policy, Elsevier, vol. 34(12), pages 1326-1331, August.
    35. Wang, Juan & Hu, Mingming & Rodrigues, João F.D., 2018. "The evolution and driving forces of industrial aggregate energy intensity in China: An extended decomposition analysis," Applied Energy, Elsevier, vol. 228(C), pages 2195-2206.
    36. Wang, Yanqiu & Zhu, Zhiwei & Zhu, Zhaoge & Liu, Zhenbin, 2019. "Analysis of China's energy consumption changing using the Mean Rate of Change Index and the logarithmic mean divisia index," Energy, Elsevier, vol. 167(C), pages 275-282.
    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. Hong, Junjie & Shi, Fangyuan & Zheng, Yuhan, 2023. "Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    3. Xu, Mengmeng & Tan, Ruipeng, 2021. "Removing energy allocation distortion to increase economic output and energy efficiency in China," Energy Policy, Elsevier, vol. 150(C).
    4. Zheng, Huiling & Zhou, Jinsheng & Gao, Xiangyun & Xi, Xian & Liu, Donghui & Zhao, Yiran, 2021. "Global impacts of the topological structure of industrial driving networks on energy intensity," Energy, Elsevier, vol. 225(C).
    5. Zhang, Chi & Su, Bin & Zhou, Kaile & Sun, Yuan, 2020. "A multi-dimensional analysis on microeconomic factors of China's industrial energy intensity (2000–2017)," Energy Policy, Elsevier, vol. 147(C).
    6. Rafael Alvarado & Cristian Ortiz & Lizeth Cuesta & Brayan Tillaguango, 2023. "Spillovers impact of institutional and economic factors in energy intensity," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1805-1823, June.
    7. Jain, Princy & Goswami, Binoy, 2021. "Energy efficiency in South Asia: Trends and determinants," Energy, Elsevier, vol. 221(C).
    8. Kunkel, S. & Neuhäusler, P. & Matthess, M. & Dachrodt, M.F., 2023. "Industry 4.0 and energy in manufacturing sectors in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(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. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    2. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    3. Wu, Shu & Ding, Song, 2021. "Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector," Energy Economics, Elsevier, vol. 99(C).
    4. Zhang, Chi & Su, Bin & Zhou, Kaile & Sun, Yuan, 2020. "A multi-dimensional analysis on microeconomic factors of China's industrial energy intensity (2000–2017)," Energy Policy, Elsevier, vol. 147(C).
    5. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    6. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    7. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    8. Vaninsky, Alexander, 2014. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach," Energy Economics, Elsevier, vol. 45(C), pages 389-400.
    9. 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).
    10. Shichun Xu & Yongmei Miao & Yiwen Li & Yifeng Zhou & Xiaoxue Ma & Zhengxia He & Bin Zhao & Shuxiao Wang, 2019. "What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    11. Wang, Miao & Feng, Chao, 2018. "Using an extended logarithmic mean Divisia index approach to assess the roles of economic factors on industrial CO2 emissions of China," Energy Economics, Elsevier, vol. 76(C), pages 101-114.
    12. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    13. Bingquan Liu & Yue Wang & Xuran Chang & Boyang Nie & Lingqi Meng & Yongqing Li, 2022. "Does Land Urbanization Affect the Catch-Up Effect of Carbon Emissions Reduction in China’s Logistics?," Land, MDPI, vol. 11(9), pages 1-18, September.
    14. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    15. Boqiang Lin, & Wang, Miao, 2019. "Possibilities of decoupling for China’s energy consumption from economic growth: A temporal-spatial analysis," Energy, Elsevier, vol. 185(C), pages 951-960.
    16. Hongyun Han & Shu Wu, 2018. "Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China," Sustainability, MDPI, vol. 10(12), pages 1-23, December.
    17. Lin Boqiang & Kui Liu, 2017. "Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry," Sustainability, MDPI, vol. 9(7), pages 1-16, July.
    18. Yang, Xue & Xu, He & Su, Bin, 2022. "Factor decomposition for global and national aggregate energy intensity change during 2000–2014," Energy, Elsevier, vol. 254(PB).
    19. Yongyi Cheng & Liheng Lu & Tianyuan Shao & Manhong Shen & Laiqun Jin, 2018. "Decomposition Analysis of Factors Affecting Changes in Industrial Wastewater Emission Intensity in China: Based on a SSBM-GMI Approach," IJERPH, MDPI, vol. 15(12), pages 1-23, December.
    20. Linwei Ma & Chinhao Chong & Xi Zhang & Pei Liu & Weiqi Li & Zheng Li & Weidou Ni, 2018. "LMDI Decomposition of Energy-Related CO 2 Emissions Based on Energy and CO 2 Allocation Sankey Diagrams: The Method and an Application to China," Sustainability, MDPI, vol. 10(2), pages 1-37, January.

    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:189:y:2019:i:c:s0360544219319759. 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.