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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 (2005- 2010). 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, International Association for Energy Economics, vol. 0(Number 1).
    • Handle: RePEc:aen:journl:ej39-1-pengzhou
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    1. 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.
    2. 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.
    3. Chen, Chien-Ming, 2013. "A critique of non-parametric efficiency analysis in energy economics studies," Energy Economics, Elsevier, vol. 38(C), pages 146-152.
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    • F0 - International Economics - - General

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