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How does information and communication technology affect China's energy intensity? A three-tier structural decomposition analysis

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  • Zhou, Xiaoyong
  • Zhou, Dequn
  • Wang, Qunwei

Abstract

The continuous diffusion of information and communication technology (ICT) has exerted growing influences on the production process and energy use. This paper analyzes the major drivers behind changes in China's energy intensity with emphasis on ICT and production structure using a three-tier structural decomposition analysis (SDA) approach. This approach could thoroughly quantify the various effects of ICT on energy intensity and investigate the mechanisms of ICT input change through which it affects sectoral energy consumption. The main results indicate that: (a) production structure exerted a rising negative effect on China's energy intensity change from 2002 to 2012; (b) ICT contributed to a 4.54% increment in energy intensity, yet ICT input substitution was conducive to reduce energy use in production; (c) the ICT effects were more significant in the service sector and technology-intensive sectors. The study suggests that the Chinese government should formulate measures for adjusting production structure, promoting sustainable development of ICT and harmonizing inter-sectoral ICT input level.

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  • 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.
  • Handle: RePEc:eee:energy:v:151:y:2018:i:c:p:748-759
    DOI: 10.1016/j.energy.2018.03.115
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    References listed on IDEAS

    as
    1. Botang Han & Dong Wang & Weina Ding & Lei Han, 2016. "Effect of information and communication technology on energy consumption in China," 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 297-315, November.
    2. Kakali Mukhopadhyay & Debesh Chakraborty, 1999. "India's Energy Consumption Changes during 1973/74 to 1991/92," Economic Systems Research, Taylor & Francis Journals, vol. 11(4), pages 423-438.
    3. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    4. 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.
    5. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues," Energy, Elsevier, vol. 123(C), pages 47-63.
    6. Paul De Boer, 2008. "Additive Structural Decomposition Analysis and Index Number Theory: An Empirical Application of the Montgomery Decomposition," Economic Systems Research, Taylor & Francis Journals, vol. 20(1), pages 97-109.
    7. Su, Bin & Huang, H.C. & Ang, B.W. & Zhou, P., 2010. "Input-output analysis of CO2 emissions embodied in trade: The effects of sector aggregation," Energy Economics, Elsevier, vol. 32(1), pages 166-175, January.
    8. Su, Bin & Ang, B.W., 2017. "Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities," Energy Economics, Elsevier, vol. 65(C), pages 137-147.
    9. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    10. Zeng, Lin & Xu, Ming & Liang, Sai & Zeng, Siyu & Zhang, Tianzhu, 2014. "Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis," Energy Policy, Elsevier, vol. 67(C), pages 640-647.
    11. Lim, Hea-Jin & Yoo, Seung-Hoon & Kwak, Seung-Jun, 2009. "Industrial CO2 emissions from energy use in Korea: A structural decomposition analysis," Energy Policy, Elsevier, vol. 37(2), pages 686-698, February.
    12. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, Open Access Journal, vol. 9(5), pages 1-13, April.
    13. Erik Dietzenbacher & Bart Los, 1998. "Structural Decomposition Techniques: Sense and Sensitivity," Economic Systems Research, Taylor & Francis Journals, vol. 10(4), pages 307-324.
    14. Chen, Chia-Yon & Wu, Rong-Hwa, 1994. "Sources of change in industrial electricity use in the Taiwan economy, 1976-1986," Energy Economics, Elsevier, vol. 16(2), pages 115-120, April.
    15. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    16. Chen, Xavier, 1994. "Substitution of information for energy: Conceptual background, realities and limits," Energy Policy, Elsevier, vol. 22(1), pages 15-27, January.
    17. Llop, Maria, 2017. "Changes in energy output in a regional economy: A structural decomposition analysis," Energy, Elsevier, vol. 128(C), pages 145-151.
    18. Ronald Bernstein & Reinhard Madlener, 2010. "Impact of disaggregated ICT capital on electricity intensity in European manufacturing," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1691-1695.
    19. Walker, William, 1986. "Information technology and energy supply," Energy Policy, Elsevier, vol. 14(6), pages 466-488, December.
    20. Sadorsky, Perry, 2012. "Information communication technology and electricity consumption in emerging economies," Energy Policy, Elsevier, vol. 48(C), pages 130-136.
    21. Paul De Boer, 2009. "Multiplicative Decomposition And Index Number Theory: An Empirical Application Of The Sato-Vartia Decomposition," Economic Systems Research, Taylor & Francis Journals, vol. 21(2), pages 163-174.
    22. Liao, Hua & Fan, Ying & Wei, Yi-Ming, 2007. "What induced China's energy intensity to fluctuate: 1997-2006?," Energy Policy, Elsevier, vol. 35(9), pages 4640-4649, September.
    23. Wood, Richard, 2009. "Structural decomposition analysis of Australia's greenhouse gas emissions," Energy Policy, Elsevier, vol. 37(11), pages 4943-4948, November.
    24. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    25. Walker, William, 1985. "Information technology and the use of energy," Energy Policy, Elsevier, vol. 13(5), pages 458-476, October.
    26. Vu, Khuong M., 2011. "ICT as a source of economic growth in the information age: Empirical evidence from the 1996-2005 period," Telecommunications Policy, Elsevier, vol. 35(4), pages 357-372, May.
    27. Takase, Kae & Murota, Yasuhiro, 2004. "The impact of IT investment on energy: Japan and US comparison in 2010," Energy Policy, Elsevier, vol. 32(11), pages 1291-1301, July.
    28. Weber, Christopher L., 2009. "Measuring structural change and energy use: Decomposition of the US economy from 1997 to 2002," Energy Policy, Elsevier, vol. 37(4), pages 1561-1570, April.
    29. Wu, Jung-Hua & Chen, Yen-Yin & Huang, Yun-Hsun, 2007. "Trade pattern change impact on industrial CO2 emissions in Taiwan," Energy Policy, Elsevier, vol. 35(11), pages 5436-5446, November.
    30. Galvin, Ray, 2015. "The ICT/electronics question: Structural change and the rebound effect," Ecological Economics, Elsevier, vol. 120(C), pages 23-31.
    31. Haiyan Zhang & Michael L. Lahr, 2014. "Can The Carbonizing Dragon Be Domesticated? Insights From A Decomposition Of Energy Consumption And Intensity In China, 1987--2007," Economic Systems Research, Taylor & Francis Journals, vol. 26(2), pages 119-140, June.
    32. Zha, DongLan & Zhou, DeQun & Ding, Ning, 2012. "The determinants of aggregated electricity intensity in China," Applied Energy, Elsevier, vol. 97(C), pages 150-156.
    33. Cho, Youngsang & Lee, Jongsu & Kim, Tai-Yoo, 2007. "The impact of ICT investment and energy price on industrial electricity demand: Dynamic growth model approach," Energy Policy, Elsevier, vol. 35(9), pages 4730-4738, September.
    34. Masayo Wakabayashi & Geoffrey J. D. Hewings, 2007. "Life-Cycle Changes In Consumption Behavior: Age-Specific And Regional Variations," Journal of Regional Science, Wiley Blackwell, vol. 47(2), pages 315-337, May.
    35. 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.
    36. Rose, A. & Chen, C. Y., 1991. "Sources of change in energy use in the U.S. economy, 1972-1982 : A structural decomposition analysis," Resources and Energy, Elsevier, vol. 13(1), pages 1-21, April.
    37. Huang, Yun-Hsun & Wu, Jung-Hua, 2013. "Analyzing the driving forces behind CO2 emissions and reduction strategies for energy-intensive sectors in Taiwan, 1996–2006," Energy, Elsevier, vol. 57(C), pages 402-411.
    38. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    39. Bin Su & B. W. Ang, 2012. "Structural Decomposition Analysis Applied To Energy And Emissions: Aggregation Issues," Economic Systems Research, Taylor & Francis Journals, vol. 24(3), pages 299-317, March.
    40. Fan, Ying & Xia, Yan, 2012. "Exploring energy consumption and demand in China," Energy, Elsevier, vol. 40(1), pages 23-30.
    41. Collard, Fabrice & Feve, Patrick & Portier, Franck, 2005. "Electricity consumption and ICT in the French service sector," Energy Economics, Elsevier, vol. 27(3), pages 541-550, May.
    42. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    43. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
    44. Su, Bin & Ang, B.W., 2010. "Input-output analysis of CO2 emissions embodied in trade: The effects of spatial aggregation," Ecological Economics, Elsevier, vol. 70(1), pages 10-18, November.
    45. de Boer, Paul, 2009. "Generalized Fisher index or Siegel-Shapley decomposition?," Energy Economics, Elsevier, vol. 31(5), pages 810-814, September.
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    2. Su, Bin & Ang, B.W. & Li, Yingzhu, 2019. "Structural path and decomposition analysis of aggregate embodied energy and emission intensities," Energy Economics, Elsevier, vol. 83(C), pages 345-360.
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    14. Zhu, Junpeng & Lin, Boqiang, 2020. "Convergence analysis of city-level energy intensity in China," Energy Policy, Elsevier, vol. 139(C).
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    16. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2019. "How information and communication technology drives carbon emissions: A sector-level analysis for China," Energy Economics, Elsevier, vol. 81(C), pages 380-392.

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