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.
Highlights
Energy efficiency is a major global issue that plays an essential role in achieving sustainable development
Zhang, Sun a large number of carbon emissions, wastewater, and waste gas generated by the input of traditional and Huang [5] used carbon emissions as undesirable output and gross domestic product (GDP) as a desirable output when energy have a serious impact on the environment
The CCR model assumes that the scale effect of production technology is maintained, but not all decision-making units (DMUs) are in the optimal production scale state
Summary
Energy efficiency is a major global issue that plays an essential role in achieving sustainable development. Patterson [3] first proposed the concept of energy efficiency, considering that it means using fewer resources at the same output, and gave four indicators of energy efficiency measurement. The proposal of TFEE effectively makes up for the shortcomings of traditional single-factor energy efficiency evaluation and has significant enlightening effects on subsequent research. Et al [9] pointed out that DEA is a data-oriented method for evaluating the efficiency of a set of homogeneous DMUs. Compared with previous efficiency evaluation methods, DEA does not need to build a production function, which means that it can better deal with the efficiency of DMUs. In the existing research, a large number of studies are conducted from the perspective of theory and application based on the data of countries, regions, industries and enterprises. All the acronyms mentioned in this paper are listed in the nomenclatures
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