IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v39y2018i1p59-80.html

Decomposing Aggregate CO2 Emission Changes with Heterogeneity: An Extended Production-theoretical Approach

Author

Listed:
  • H. Wang
  • B.W. Ang
  • P. Zhou

Abstract

Quantifying the driving forces behind changes in aggregate CO2 emissions provides valuable information for supporting policy making in addressing climate change. We study this issue using the production-theoretical decomposition analysis (PDA) technique. Within a production theory framework, PDA examines CO2 emission changes from the perspective of productive efficiency. Although regional and sectoral heterogeneities in energy consumption and emission patterns prevail, they have not been taken into account in the PDA literature. By incorporating relevant decomposition methods, this study proposes an extended PDA approach to resolving the heterogeneity issue. The approach is applied to examine China's aggregate CO2 emission changes in its 11th five-year plan period (20052010). By accounting for the heterogeneities, detailed results at the regional and sectoral levels are generated and further discussions presented.

Suggested Citation

  • H. Wang & B.W. Ang & P. Zhou, 2018. "Decomposing Aggregate CO2 Emission Changes with Heterogeneity: An Extended Production-theoretical Approach," The Energy Journal, , vol. 39(1), pages 59-80, January.
    • Handle: RePEc:sae:enejou:v:39:y:2018:i:1:p:59-80
    DOI: 10.5547/01956574.39.1.hwan
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.39.1.hwan
    Download Restriction: no
    File URL: https://libkey.io/10.5547/01956574.39.1.hwan?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
    ---><---

    References listed on IDEAS

    as
    1. Wang, Chunhua, 2013. "Changing energy intensity of economies in the world and its decomposition," Energy Economics, Elsevier, vol. 40(C), pages 637-644.
    2. 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.
    3. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    4. Erik Dietzenbacher & Alex R. Hoen & Bart Los, 2000. "Labor Productivity in Western Europe 1975–1985: An Intercountry, Interindustry Analysis," Journal of Regional Science, Wiley Blackwell, vol. 40(3), pages 425-452, August.
    5. Cooper, W. W. & Deng, Honghui & Gu, Bisheng & Li, Shanling & Thrall, R. M., 2001. "Using DEA to improve the management of congestion in Chinese industries (1981-1997)," Socio-Economic Planning Sciences, Elsevier, vol. 35(4), pages 227-242, December.
    6. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    7. Wang, H. & Zhou, P. & Zhou, D.Q., 2013. "Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis," Energy Economics, Elsevier, vol. 40(C), pages 795-803.
    8. Chen, Chien-Ming, 2013. "A critique of non-parametric efficiency analysis in energy economics studies," Energy Economics, Elsevier, vol. 38(C), pages 146-152.
    9. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    10. 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.
    11. Ang, B.W. & Wang, H., 2015. "Index decomposition analysis with multidimensional and multilevel energy data," Energy Economics, Elsevier, vol. 51(C), pages 67-76.
    12. Xu, Jin-Hua & Fan, Ying & Yu, Song-Min, 2014. "Energy conservation and CO2 emission reduction in China's 11th Five-Year Plan: A performance evaluation," Energy Economics, Elsevier, vol. 46(C), pages 348-359.
    13. 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.
    14. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
    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. Wang, Hui & Yu, Shasha & Yang, Yafei & Wang, Meiyue & Zhou, Peng, 2025. "Assessing carbon emissions along global supply chains from technology perspective: A network production decomposition analysis," Ecological Economics, Elsevier, vol. 233(C).
    2. Xu, Chong & Qin, Zengqiang & Chen, Jiandong & Zhang, Jiangxue, 2024. "Heterogeneous technology-induced global CO2 emission reduction and emission forecasting since the Kyoto era," Applied Energy, Elsevier, vol. 371(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. Wang, H. & Zhou, P. & Xie, Bai-Chen & Zhang, N., 2019. "Assessing drivers of CO2 emissions in China's electricity sector: A metafrontier production-theoretical decomposition analysis," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1096-1107.
    2. 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.
    3. 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.
    4. 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).
    5. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    6. Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.
    7. Wang, H. & Ang, B.W. & Wang, Q.W. & Zhou, P., 2017. "Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach," Energy Economics, Elsevier, vol. 62(C), pages 70-78.
    8. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    9. Wang, Ke & Wei, Yi-Ming, 2016. "Sources of energy productivity change in China during 1997–2012: A decomposition analysis based on the Luenberger productivity indicator," Energy Economics, Elsevier, vol. 54(C), pages 50-59.
    10. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    11. 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).
    12. Zhou, P. & Sun, Z.R. & Zhou, D.Q., 2014. "Optimal path for controlling CO2 emissions in China: A perspective of efficiency analysis," Energy Economics, Elsevier, vol. 45(C), pages 99-110.
    13. Yanni Yu & Yongrok Choi, 2015. "Measuring Environmental Performance Under Regional Heterogeneity in China: A Metafrontier Efficiency Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 375-388, October.
    14. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    15. Lin, Boqiang & Du, Kerui, 2015. "Energy and CO2 emissions performance in China's regional economies: Do market-oriented reforms matter?," Energy Policy, Elsevier, vol. 78(C), pages 113-124.
    16. Chen, Chien-Ming, 2013. "A critique of non-parametric efficiency analysis in energy economics studies," Energy Economics, Elsevier, vol. 38(C), pages 146-152.
    17. Wei, Chu & Löschel, Andreas & Liu, Bing, 2015. "Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis," Energy Economics, Elsevier, vol. 49(C), pages 33-43.
    18. Wang, Chunhua & Cao, Xiaoyong & Mao, Jie & Qin, Ping, 2019. "The changes in coal intensity of electricity generation in Chinese coal-fired power plants," Energy Economics, Elsevier, vol. 80(C), pages 491-501.
    19. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    20. Liu, Guangtian & Wang, Bing & Zhang, Ning, 2016. "A coin has two sides: Which one is driving China’s green TFP growth?," Economic Systems, Elsevier, vol. 40(3), pages 481-498.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:sae:enejou:v:39:y:2018:i:1:p:59-80. 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: SAGE Publications (email available below). General contact details of provider: .

    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.