Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

Mardani, Abbas; Štreimikienė, Dalia; Baležentis, Tomas; Saman, Muhamad Zameri Mat; Nor, Khalil Md; Khoshnava, Seyed Meysam

doi:10.3390/en11082002

Cited by 84 publications

(50 citation statements)

References 164 publications

(177 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the Data Envelopment Analysis (DEA) model are used to compare the relative efficiency between decision units. If DEA methods are integrated with a different technique, it can solve problems and measurethe relative efficiency in different application areas [25]. Han proposed that the fuzzy DEA interaction model can be used to improve the energy efficiency of chemical products [26].…”

Section: Dea Model For Measuring Efficiencymentioning

confidence: 99%

“…Pérez-López used the DEA model to analyze the evolution of economies of scale and propose strategies for achieving long-term economies of scale based on management practices [27]. DEA analysis also is a common method of evaluating energy efficiency [25]. Since there is no assumption about the function form or the distribution of the error term of the production function [28], it can better reflect the information and characteristics of the evaluation object itself.…”

Section: Dea Model For Measuring Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Driving Factors of Photovoltaic Power Generation Efficiency: A Case Study in China

et al. 2019

View full text Add to dashboard Cite

With the increasing consumption of fossil energy and changes in the ecological environment, meeting the energy demands required for industrial and economic development with clean and efficient power generation is a major challenge of our society. Solar energy is considered to be one of the most renewable and sustainable energy sources, and photovoltaic power generation has become an important research topic. This study combines data envelopment analysis (DEA) with Tobit regression analysis to assess the efficiency of photovoltaic power generation in China and analyze factors affecting efficiency to improve the efficiency of photovoltaic power generation. The results show that there are obvious regional differences in photovoltaic power generation efficiency in China. The phenomenon of focusing on economic development at the expense of the use of solar power generation still exists. The establishment of photovoltaic demonstration projects, the implementation of differential electricity price policies, and the promotion of photovoltaic precision poverty alleviation can alleviate economic pressure and effectively improve the efficiency of photovoltaic power generation.

show abstract

Section: Dea Model For Measuring Efficiencymentioning

confidence: 99%

Section: Dea Model For Measuring Efficiencymentioning

confidence: 99%

Analysis of Driving Factors of Photovoltaic Power Generation Efficiency: A Case Study in China

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In DEA practices, the variable selection of input and output is a very important issue, where the purpose is to measure the efficiency of each DMU [19][20][21][22]. A relatively comprehensive obtained dataset can provide us with a more reasonable way to represent the evaluation problem.…”

Section: Variables and Data For Unified Efficiencymentioning

confidence: 99%

Natural and Managerial Disposability Based DEA Model for China’s Regional Environmental Efficiency Assessment

et al. 2019

View full text Add to dashboard Cite

With the acceleration of industrialization, a large amount of energy consumption has brought tremendous pressure to the natural environment. In order to prevent environmental pollution and promote sustainable development, the environmental efficiency assessment as an effective way to provide decision-making basis has been given wide attention. This study measures the environmental efficiency of 30 provinces in China from 2006 to 2015 based on the Data Envelopment Analysis (DEA) environmental assessment radial model both under natural disposability and managerial disposability that considered the constant variable return to scale (RTS) and the damage to scale (DTS). In addition, the scale efficiency under the two kinds of disposability of China’s 30 provinces were also measured. We found that the environmental efficiencies of different provinces in China showed regional disparities. Provinces such as Beijing, Shanghai, and Guangdong had a good performance in unified environmental efficiency and scale efficiency both under natural disposability and managerial disposability. Generally speaking, the eastern regions always performed better than the central and western regions in unified environmental efficiency during the observed years. Therefore, policies should be established to distribute the resources in balance between the east, center, and west to further promote environmental efficiency.

show abstract

“…Furthermore, one study [11] states that "DEA showed great promise to be a good evaluative tool for future analysis on energy efficiency" as a conclusion. One of the reasons for this great promise is the facility of multiple inputs and multiple outputs of the DEA model.…”

Section: Dea Bcc Model and Non-decreasing Returns To Scalementioning

confidence: 99%

Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps

et al. 2018

View full text Add to dashboard Cite

This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.

show abstract

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

Cited by 84 publications

References 164 publications

Analysis of Driving Factors of Photovoltaic Power Generation Efficiency: A Case Study in China

Analysis of Driving Factors of Photovoltaic Power Generation Efficiency: A Case Study in China

Natural and Managerial Disposability Based DEA Model for China’s Regional Environmental Efficiency Assessment

Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps

Contact Info

Product

Resources

About