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Properties and linkages of some index decomposition analysis methods

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  • Ang, B.W.
  • Huang, H.C.
  • Mu, A.R.
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    Abstract

    We study the properties and linkages of some popular index decomposition analysis (IDA) methods in energy and carbon emission analyses. Specifically, we introduce a simple relationship between the arithmetic mean Divisia index (AMDI) method and the logarithmic mean Divisia index method I (LMDI I), and show that such a relationship can be extended to cover most IDA methods linked to the Divisia index. We also formalize the relationship between the Laspeyres index method and the Shapley value in the IDA context. Similarly, such a relationship can be extended to cover other IDA methods linked to the Laspeyres index through defining the characteristic function in the Shapley value. It is found that these properties and linkages apply to decomposition of changes conducted additively. Similar properties and linkages cannot be established in the multiplicative case. The implications of the findings on IDA studies are discussed.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 37 (2009)
    Issue (Month): 11 (November)
    Pages: 4624-4632

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    Handle: RePEc:eee:enepol:v:37:y:2009:i:11:p:4624-4632

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    Web page: http://www.elsevier.com/locate/enpol

    Related research

    Keywords: Index decomposition analysis LMDI Shapley value;

    References

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    Citations

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    Cited by:
    1. Hwang, In Chang, 2013. "Anthropogenic drivers of carbon emissions: scale and counteracting effects," MPRA Paper 52224, University Library of Munich, Germany.
    2. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    3. 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.
    4. Ma, Chunbo, 2014. "A multi-fuel, multi-sector and multi-region approach to index decomposition: An application to China's energy consumption 1995–2010," Energy Economics, Elsevier, vol. 42(C), pages 9-16.
    5. Hwang, In Chang, 2013. "Stochastic Kaya model and its applications," MPRA Paper 55099, University Library of Munich, Germany.
    6. Li, Fangyi & Song, Zhouying & Liu, Weidong, 2014. "China's energy consumption under the global economic crisis: Decomposition and sectoral analysis," Energy Policy, Elsevier, vol. 64(C), pages 193-202.
    7. Åsa Löfgren & Adrian Muller, 2010. "Swedish CO 2 Emissions 1993–2006: An Application of Decomposition Analysis and Some Methodological Insights," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 47(2), pages 221-239, October.
    8. Ang, B.W. & Mu, A.R. & Zhou, P., 2010. "Accounting frameworks for tracking energy efficiency trends," Energy Economics, Elsevier, vol. 32(5), pages 1209-1219, September.
    9. Jimenez, Raul & Mercado, Jorge, 2014. "Energy intensity: A decomposition and counterfactual exercise for Latin American countries," Energy Economics, Elsevier, vol. 42(C), pages 161-171.
    10. Xu, X.Y. & Ang, B.W., 2013. "Index decomposition analysis applied to CO2 emission studies," Ecological Economics, Elsevier, vol. 93(C), pages 313-329.
    11. Baležentis, Alvydas & Baležentis, Tomas & Streimikiene, Dalia, 2011. "The energy intensity in Lithuania during 1995–2009: A LMDI approach," Energy Policy, Elsevier, vol. 39(11), pages 7322-7334.

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