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

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  • 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
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    1. Chen, Chien-Ming, 2013. "A critique of non-parametric efficiency analysis in energy economics studies," Energy Economics, Elsevier, vol. 38(C), pages 146-152.
    2. 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.
    3. 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.
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    Cited by:

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    3. Dong, Kangyin & Hochman, Gal & Timilsina, Govinda R., 2020. "Do drivers of CO2 emission growth alter overtime and by the stage of economic development?," Energy Policy, Elsevier, vol. 140(C).
    4. Zhou, Xun & Kuosmanen, Timo, 2020. "What drives decarbonization of new passenger cars?," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1043-1057.
    5. Shiraki, Hiroto & Matsumoto, Ken'ichi & Shigetomi, Yosuke & Ehara, Tomoki & Ochi, Yuki & Ogawa, Yuki, 2020. "Factors affecting CO2 emissions from private automobiles in Japan: The impact of vehicle occupancy," Applied Energy, Elsevier, vol. 259(C).
    6. 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).
    7. Lizhan Cao & Hui Wang, 2022. "The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
    8. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    9. 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.
    10. Haobo Chen & Shangyu Liu & Yaoqiu Kuang & Jie Shu & Zetao Ma, 2023. "Decomposition Analysis of Regional Electricity Consumption Drivers Considering Carbon Emission Constraints: A Comparison of Guangdong and Yunnan Provinces in China," Energies, MDPI, vol. 16(24), pages 1-25, December.
    11. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    12. Morakinyo O. Adetutu & Kayode A. Odusany & Thomas G. Weyman-Jones, 2020. "Carbon Tax and Energy Intensity: Assessing the Channels of Impact using UK Microdata," The Energy Journal, , vol. 41(2), pages 143-166, March.
    13. Wang, Miao & Feng, Chao, 2018. "Investigating the drivers of energy-related CO2 emissions in China’s industrial sector: From regional and provincial perspectives," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 136-147.
    14. Feng Dong & Xinqi Gao & Jingyun Li & Yuanqing Zhang & Yajie Liu, 2018. "Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches," IJERPH, MDPI, vol. 15(12), pages 1-28, December.
    15. 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).
    16. Zhang, Danyang & Wang, Hui & Löschel, Andreas & Zhou, Peng, 2021. "The changing role of global value chains in CO2 emission intensity in 2000–2014," Energy Economics, Elsevier, vol. 93(C).
    17. Wang, H. & Pan, Chen & Wang, Qunwei & Zhou, P., 2020. "Assessing sustainability performance of global supply chains: An input-output modeling approach," European Journal of Operational Research, Elsevier, vol. 285(1), pages 393-404.
    18. H. Wang & Chen Pan & P. Zhou, 2019. "Assessing the Role of Domestic Value Chains in China’s CO2 Emission Intensity: A Multi-Region Structural Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(2), pages 865-890, October.
    19. Yun-Hsun Huang & Jung-Hua Wu & Hao-Syuan Huang, 2021. "Analyzing the Driving Forces behind CO 2 Emissions in Energy-Resource-Poor and Fossil-Fuel-Centered Economies: Case Studies from Taiwan, Japan, and South Korea," Energies, MDPI, vol. 14(17), pages 1-14, August.
    20. Wang, H. & Zhou, P., 2018. "Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis," Ecological Economics, Elsevier, vol. 154(C), pages 257-271.
    21. 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.
    22. Huang, Yun-Hsun, 2020. "Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data," Energy, Elsevier, vol. 213(C).
    23. 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.
    24. Chen, Xiaodong & Guo, Anda & Miao, Zhuang & Zhu, Pengyu, 2024. "Assessing the performance of the transport sector within the global supply chain context: Decomposition of energy and environmental productivity," Applied Energy, Elsevier, vol. 358(C).
    25. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2018. "The driving forces and potential mitigation of energy-related CO2 emissions in China's metal industry," Resources Policy, Elsevier, vol. 59(C), pages 487-494.

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    More about this item

    Keywords

    Production-theoretical decomposition analysis; Non-parametric frontier; Index number; Heterogeneity; China;
    All these keywords.

    JEL classification:

    • F0 - International Economics - - General

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