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A global level analysis of environmental energy efficiency: an application of data envelopment analysis

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Abstract

The objective of this study is to estimate environmental energy efficiency (EEE)—defined as energy efficiency measures incorporating undesirable output in the production process—for high-income, middle-income and low-income economies as well as at the global level for the period 1993–2013. Under the broad framework of Data Envelopment Analysis, the traditional input-oriented measures of efficiency, the joint production approach and the latest by-production approach have been used to assess EEE. The empirical results confirm a gradual improvement in EEE level except in the years 1998–1999 and 2009. The high-income economies spearheaded this improvement in EEE followed by the middle-income and low-income economies. This might be explained by the use of a relatively greater share of renewable energy in high-income economies. Moreover, the middle-income economies appear to converge to global EEE levels while low-income economies have lagged behind. Policy implications of our results are also discussed.

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Notes

  1. For the 2019 fiscal year, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $995 or less in 2017; middle-income economies with GNI per capita of between $995 and $12,055; high-income economies with GNI per capita of $12,056 or more.

  2. As defined by U.S. Energy Information Administration (EIA).

  3. See the file of ‘User Guide to PWT 9.0 data files’ for more details of the description and calculation of different variables.

  4. Wilcoxon rank-sum test is a nonparametric alternative to the parametric two sample t test. This test is based solely on the order in which the observations from the two samples fall. The current work utilizes this nonparametric alternative for t test since DEA efficiency scores are estimated from the nonparametric linear programming model.

  5. Kruskal–Wallis test is a nonparametric alternative to the one-way analysis of variance (ANOVA). This test extends the two-samples Wilcoxon test and is used to compare more than two independent samples. The current work utilizes this nonparametric alternative for t test since DEA efficiency scores are estimated from the nonparametric linear programming model.

  6. Cushing, OK WTI Spot Price FOB (Dollars per Barrel), from U.S. Energy Information Administration http://tonto.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D

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  1. Department of Humanities and Social Sciences, Indian Institute of Technology Madras, Chennai, TN, 600036, India

    Ipsita Rakshit & Sabuj Kumar Mandal

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  1. Ipsita Rakshit
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Correspondence to Sabuj Kumar Mandal.

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Rakshit, I., Mandal, S.K. A global level analysis of environmental energy efficiency: an application of data envelopment analysis. Energy Efficiency 13, 889–909 (2020). https://doi.org/10.1007/s12053-020-09857-5

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