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Variance analysis of global CO2 emission – A management accounting approach for decomposition study

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  • Pani, Ratnakar
  • Mukhopadhyay, Ujjaini

Abstract

In the context of the present worldwide concern and desperate search for policies to curtail carbon dioxide emission, the paper aims to determine the roles of major driving forces in aggravating emission and examine the possibility of emission cut without compromising economic growth. Variance analysis method, in the line of management accounting, is used to decompose the changes in emission of 156 sample countries over the period 1993–2007. The major findings suggest that in aggregate, rising per capita GDP has been about seven times more responsible than that of population in accentuating emission; decline in energy intensity has been instrumental in offsetting nearly half of their potential effects, while inter-fuel substitution and change in emission intensities have meager roles. However, wide disparities in structural composition of energy intensity and emission intensity of fuels among countries over the period, point towards the crucial role of proper energy management in lowering emission concomitant with high economic growth. Management accounting control, particularly variance analysis, at the national level can be an effective tool in identifying the weaknesses and exploring the areas where emission reduction can be possible.

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  • Pani, Ratnakar & Mukhopadhyay, Ujjaini, 2011. "Variance analysis of global CO2 emission – A management accounting approach for decomposition study," Energy, Elsevier, vol. 36(1), pages 486-499.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:1:p:486-499
    DOI: 10.1016/j.energy.2010.10.015
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    4. Román-Collado, Rocío & Morales-Carrión, Any Viviana, 2018. "Towards a sustainable growth in Latin America: A multiregional spatial decomposition analysis of the driving forces behind CO2 emissions changes," Energy Policy, Elsevier, vol. 115(C), pages 273-280.
    5. Adewuyi, Adeolu O., 2016. "Effects of public and private expenditures on environmental pollution: A dynamic heterogeneous panel data analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 489-506.

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