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

Author

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

    (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology)

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 conclusions are that during 2002-2007, except crude oil and refined oil, the energy intensity increased and the changes were mostly attributed to the structural change, while the changes in the production technology actually decreased the energy intensity. Furthermore, compared to the decomposition without biased technical change, the degree of the influence from structural change on the changes in energy intensity depends on the level of biased technical change.

Suggested Citation

  • Ce Wang & Hua Liao & Su-Yan Pan & Lu-Tao Zhao & Yi-Ming Wei, 2014. "The fluctuations of China's energy intensity: Biased technical change," CEEP-BIT Working Papers 56, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  • Handle: RePEc:biw:wpaper:56
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    Cited by:

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    5. 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.
    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, vol. 11(3), pages 1-21, January.
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    9. Zhang, Wei & Zhang, Ting & Li, Hangyu & Zhang, Han, 2022. "Dynamic spillover capacity of R&D and digital investments in China's manufacturing industry under long-term technological progress based on the industry chain perspective," Technology in Society, Elsevier, vol. 71(C).
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    11. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    12. Li, Meng & Gao, Yuning & Liu, Shenglong, 2020. "China’s energy intensity change in 1997–2015: Non-vertical adjusted structural decomposition analysis based on input-output tables," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 222-236.
    13. 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.
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    More about this item

    Keywords

    Biased Technical Change; Divisia Decomposition; Input-Output Analysis; Energy Intensity; China; RAS Technique;
    All these keywords.

    JEL classification:

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

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