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Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review

descriptionPublicationkeyboard_double_arrow_right Article , Journal 10 Jul 2020Publisher:MDPI AGJournal:Energies, volume 13, page 3,548 (eissn: 1996-1073,

Authors: Tao Xu; Jianxin You; Hui Li; Luning Shao;

doi: 10.3390/en13143548

Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review

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Abstract

The importance and urgency of energy efficiency in sustainable development are increasing. Accurate assessment of energy efficiency is of considerable significance and necessity. The data envelopment analysis (DEA) method has been widely used to study energy efficiency as a total factor efficiency assessment method. In order to summarize the latest research on DEA in the field of energy efficiency, this article first analyzes the overall situation of related literature published in 2011–2019. Subsequently, the definition, measurement and evaluation variables of energy efficiency are introduced. After that, this article reviews the current DEA model and its extension models and applications based on different scenarios. Finally, considering the shortcomings of the existing DEA model, possible future research topics are proposed.

Related Organizations

Tongji University
China (People's Republic of)

Keywords

future research, Technology, literature review, T, data envelopment analysis, energy efficiency

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	86
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%