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Inequality in Energy Intensity in the EU-28: Evidence from a New Decomposition Method

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Listed:
  • Luigi Grossi
  • Mauro Mussini

Abstract

This paper investigates inequality in energy intensity between EU-28 member countries over the 2007-2012 period. Inequality in energy intensity is measured by using the Zenga inequality index. The analysis is carried out by measuring inequality from the bottom of the energy intensity distribution to the top. This approach enables to identify the most unequal portions of the energy intensity distribution. To provide information on the causes of inequality at every point of the distribution, we show that inequality can be broken down into three components explaining the roles played by energy transformation, final energy intensity and their interaction in determining inequality in energy intensity. This decomposition reveals the impact of each component of inequality from the bottom of energy intensity distribution to the top. Results show that final energy intensity plays a major role in explaining inequality in the energy intensity distribution. The interaction component explains that EU-28 countries with low energy intensity are more efficient in energy transformation and less energy-intensive in enduse sectors than EU-28 countries with high energy intensity. The energy transformation component is higher when measuring inequality between the countries at the bottom of the distribution and those in the rest of the distribution, suggesting that disparities in energy transformation efficiency play an important role in determining inequality in energy intensity between the least energy-intensive countries and the other countries. The high inequality at the top of the distribution is due to the lower efficiency in energy transformation in the most energy-intensive countries, which reinforces the effect of disparity in final energy intensity between the countries at the top of the distribution and the other countries.

Suggested Citation

  • Luigi Grossi & Mauro Mussini, 2017. "Inequality in Energy Intensity in the EU-28: Evidence from a New Decomposition Method," The Energy Journal, , vol. 38(4), pages 1-18, July.
  • Handle: RePEc:sae:enejou:v:38:y:2017:i:4:p:1-18
    DOI: 10.5547/01956574.38.4.lgro
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    References listed on IDEAS

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    1. Matti Langel & Yves Tillé, 2012. "Inference by linearization for Zenga’s new inequality index: a comparison with the Gini index," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1093-1110, November.
    2. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    3. Duro, Juan Antonio, 2013. "Weighting vectors and international inequality changes in environmental indicators: An analysis of CO2 per capita emissions and Kaya factors," Energy Economics, Elsevier, vol. 39(C), pages 122-127.
    4. Mussini, Mauro & Grossi, Luigi, 2015. "Decomposing changes in CO2 emission inequality over time: The roles of re-ranking and changes in per capita CO2 emission disparities," Energy Economics, Elsevier, vol. 49(C), pages 274-281.
    5. repec:aen:journl:2008v29-03-a01 is not listed on IDEAS
    6. Markandya, Anil & Pedroso-Galinato, Suzette & Streimikiene, Dalia, 2006. "Energy intensity in transition economies: Is there convergence towards the EU average?," Energy Economics, Elsevier, vol. 28(1), pages 121-145, January.
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