IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v135y2014icp407-414.html
   My bibliography  Save this article

The fluctuations of China’s energy intensity: Biased technical change

Author

Listed:
  • Wang, Ce
  • Liao, Hua
  • Pan, Su-Yan
  • Zhao, Lu-Tao
  • Wei, Yi-Ming

Abstract

The fluctuations of China’s energy intensity have attracted the attention of many scholars, but fewer studies consider the data quality of official input–output tables. This paper conducts a decomposition model by using the Divisia method based on the input–output tables. Because of the problems with input–output tables and price deflators, we first produce constant prices to deflate the input–output tables. And then we consider different levels of biased technical change for different sectors in the adjusting the input–output table. Finally, we use RAS technique to adjust input–output matrix. Then the decomposition model is employed to empirically analyze the change of China’s energy intensity. We compare the decomposition results with and without biased technical change and do sensitive analysis on the level of biased technical change. The decomposition results are that during 2002–2007, the energy intensity of coal and electricity increased, the changes were mostly attributed to the structural change and the contribution was 594.08%, 73.88%, respectively; as for crude oil and refined oil, the energy intensity decreased, the changes were mostly attributed to the changes in the production technology and the contribution was 978.89%, 246.95%, respectively. And the results of sensitive analysis shows that 1% variation of the level of biased technical change will cause at most 0.6% change of decomposition results. Therefore, we can draw our conclusions: compared to the decomposition without biased technical change, decomposition results are sensitive to the level of biased technical change; the level of biased technical change can be determined by the difference in the change rate of total factor productivity and energy efficiency.

Suggested Citation

  • Wang, Ce & Liao, Hua & Pan, Su-Yan & Zhao, Lu-Tao & Wei, Yi-Ming, 2014. "The fluctuations of China’s energy intensity: Biased technical change," Applied Energy, Elsevier, vol. 135(C), pages 407-414.
  • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:407-414
    DOI: 10.1016/j.apenergy.2014.06.088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914008976
    Download Restriction: Full text for ScienceDirect subscribers only

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hulten, Charles R, 1973. "Divisia Index Numbers," Econometrica, Econometric Society, vol. 41(6), pages 1017-1025, November.
    2. Cellura, Maurizio & Longo, Sonia & Mistretta, Marina, 2012. "Application of the Structural Decomposition Analysis to assess the indirect energy consumption and air emission changes related to Italian households consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1135-1145.
    3. Hua Liao, . "China Country Report," Chapters, in: Shigeru Kimura (ed.),Analysis on Energy Saving Potential in East Asia, chapter 5, pages 115-130, Economic Research Institute for ASEAN and East Asia (ERIA).
    4. Cong, Rong-Gang & Wei, Yi-Ming & Jiao, Jian-Lin & Fan, Ying, 2008. "Relationships between oil price shocks and stock market: An empirical analysis from China," Energy Policy, Elsevier, vol. 36(9), pages 3544-3553, September.
    5. Ma, Chunbo, 2014. "A multi-fuel, multi-sector and multi-region approach to index decomposition: An application to China's energy consumption 1995–2010," Energy Economics, Elsevier, vol. 42(C), pages 9-16.
    6. Richard F. Garbaccio & Mun S. Ho & Dale W. Jorgenson, 1999. "Why Has the Energy-Output Ratio Fallen in China?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 63-91.
    7. Ma, Chunbo, 2010. "Account for sector heterogeneity in China's energy consumption: Sector price indices vs. GDP deflator," Energy Economics, Elsevier, vol. 32(1), pages 24-29, January.
    8. Li, Raymond & Leung, Guy C.K., 2012. "Coal consumption and economic growth in China," Energy Policy, Elsevier, vol. 40(C), pages 438-443.
    9. Cao, Shuyan & Xie, Gaodi & Zhen, Lin, 2010. "Total embodied energy requirements and its decomposition in China's agricultural sector," Ecological Economics, Elsevier, vol. 69(7), pages 1396-1404, May.
    10. Hua Liao & Ce Wang & Zhi-Shuang Zhu & Xiao-Wei Ma, 2012. "Structural decomposition analysis on energy intensity changes at regional level," CEEP-BIT Working Papers 40, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    11. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    12. Chung, Whan-Sam & Tohno, Susumu & Shim, Sang Yul, 2009. "An estimation of energy and GHG emission intensity caused by energy consumption in Korea: An energy IO approach," Applied Energy, Elsevier, vol. 86(10), pages 1902-1914, October.
    13. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    14. G. Boyd & J. F. McDonald & M. Ross & D. A. Hansont, 1987. "Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 77-96.
    15. Herreras Martínez, Sara & van Eijck, Janske & Pereira da Cunha, Marcelo & Guilhoto, Joaquim J.M. & Walter, Arnaldo & Faaij, Andre, 2013. "Analysis of socio-economic impacts of sustainable sugarcane–ethanol production by means of inter-regional Input–Output analysis: Demonstrated for Northeast Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 290-316.
    16. Fan, Jing-Li & Liao, Hua & Liang, Qiao-Mei & Tatano, Hirokazu & Liu, Chun-Feng & Wei, Yi-Ming, 2013. "Residential carbon emission evolutions in urban–rural divided China: An end-use and behavior analysis," Applied Energy, Elsevier, vol. 101(C), pages 323-332.
    17. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    18. Kahrl, Fredrich & Roland-Holst, David, 2009. "Growth and structural change in China's energy economy," Energy, Elsevier, vol. 34(7), pages 894-903.
    19. Chung, Whan-Sam & Tohno, Susumu & Choi, Ki-Hong, 2011. "Socio-technological impact analysis using an energy IO approach to GHG emissions issues in South Korea," Applied Energy, Elsevier, vol. 88(11), pages 3747-3758.
    20. Ma, Chunbo & Stern, David I., 2008. "Biomass and China's carbon emissions: A missing piece of carbon decomposition," Energy Policy, Elsevier, vol. 36(7), pages 2517-2526, July.
    21. Limmeechokchai, Bundit & Suksuntornsiri, Pawinee, 2007. "Embedded energy and total greenhouse gas emissions in final consumptions within Thailand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(2), pages 259-281, February.
    22. Zhao, Xiaoli & Ma, Chunbo & Hong, Dongyue, 2010. "Why did China's energy intensity increase during 1998-2006: Decomposition and policy analysis," Energy Policy, Elsevier, vol. 38(3), pages 1379-1388, March.
    23. Cansino, J.M. & Cardenete, M.A. & Ordóñez, M. & Román, R., 2012. "Economic analysis of greenhouse gas emissions in the Spanish economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6032-6039.
    24. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, 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. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
    2. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
    3. Azlina Abdullah & Hussain Ali Bekhet, 2019. "Investigating the Driving Forces of Energy Intensity Change in Malaysia 1991-2010: A Structural Decomposition Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 121-130.
    4. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2018. "Energy-biased technical change in the Chinese industrial sector with CES production functions," Energy, Elsevier, vol. 148(C), pages 896-903.
    5. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    6. Chao Bi & Minna Jia & Jingjing Zeng, 2019. "Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach," Sustainability, MDPI, Open Access Journal, vol. 11(3), pages 1-21, January.
    7. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2017. "Energy biased technology change: Focused on Chinese energy-intensive industries," Applied Energy, Elsevier, vol. 190(C), pages 1081-1089.
    8. Zhang, Dayong & Cao, Hong & Wei, Yi-Ming, 2016. "Identifying the determinants of energy intensity in China: A Bayesian averaging approach," Applied Energy, Elsevier, vol. 168(C), pages 672-682.

    More about this item

    Keywords

    Biased technical change; Divisia decomposition; Input–output analysis; Energy intensity; China; RAS technique;

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    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:appene:v:135:y:2014:i:c:p:407-414. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.