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Energy Efficiency in Electricity Production: A Data Envelopment Analysis (DEA) Approach for the G-20 Countries

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  • Nuri Ozgur DOGAN

    (Nevsehir Hac Bekta Veli University, Faculty of Economics and Administrative Sciences, 50300, Nevsehir, Turkey.)

  • Can Tansel TUGCU

    (Nevsehir Hac Bekta Veli University, Faculty of Economics and Administrative Sciences, 50300, Nevsehir, Turkey.)

Abstract

Factors such as global warming, increased energy prices, decreased security of energy supply and the vision of sustainable development have inspired researchers to focus on energy efficiency. In this context, this study adopts input oriented DEA based on the Charnes, Cooper and Rhodes (CCR) model and estimates technical and super efficiency scores of G-20 countries in terms of electricity production for the periods 1990, 1995, 2000, 2005 and 2011. Findings reveal that China and Russia appear at the top of energy efficiency rankings. On the other hand, France and the European Union are inefficient in four of five periods. Besides, the way that the United States follows for recent electricity production seems inefficient. This implies that the world has been experiencing an important transformation in terms of efficient electricity production and policy makers should be aware of this progress in order to avoid unexpected outcomes for the energy future.

Suggested Citation

  • Nuri Ozgur DOGAN & Can Tansel TUGCU, 2015. "Energy Efficiency in Electricity Production: A Data Envelopment Analysis (DEA) Approach for the G-20 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 246-252.
    • Handle: RePEc:eco:journ2:2015-01-19
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    References listed on IDEAS

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    Cited by:

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    2. Huang, Beijia & Zhang, Long & Ma, Linmao & Bai, Wuliyasu & Ren, Jingzheng, 2021. "Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index," Energy, Elsevier, vol. 228(C).
    3. Sebastian Cuadros & Yeny E. Rodríguez & Javier Contreras, 2020. "Dynamic Data Envelopment Analysis Model Involving Undesirable Outputs in the Electricity Power Generation Sector: The Case of Latin America and the Caribbean Countries," Energies, MDPI, vol. 13(24), pages 1-20, December.
    4. Gianpaolo Iazzolino & Rossella Gabriele, 2016. "Energy Efficiency and Sustainable Development: An Analysis of Financial Reliability in Energy Service Companies Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 222-233.
    5. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    6. L sara Fabr cia Rodrigues & Matheus Alves Madeira de Souza & Thamara Paula dos Santos Dias, 2017. "Performance Assessment of Brazilian Power Transmission and Distribution Segments using Data Envelopment Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 7(3), pages 14-23.
    7. Borozan, Djula, 2018. "Technical and total factor energy efficiency of European regions: A two-stage approach," Energy, Elsevier, vol. 152(C), pages 521-532.
    8. France Krizanic & Zan Jan Oplotnik & Vasja Kolsek & Alenka Kavkler, 2015. "Production Factors Use in the European Electricity Producing Companies During the Last Financial Crisis," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 725-730.
    9. Liang-Han Ma & Jin-Chi Hsieh & Yung-Ho Chiu, 2020. "Comparing regional differences in global energy performance," Energy & Environment, , vol. 31(6), pages 943-960, September.
    10. Jin-chi Hsieh & Ching-cheng Lu & Ying Li & Yung-ho Chiu & Ya-sue Xu, 2019. "Environmental Assessment of European Union Countries," Energies, MDPI, vol. 12(2), pages 1-18, January.

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    More about this item

    Keywords

    Energy efficiency; Data Envelopment Analysis; G-20 countries;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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