Measuring the performance of thermal power firms in China via fuzzy Enhanced Russell measure model with undesirable outputs

Wu, Jie; Xiong, Beibei; An, Qingxian; Zhu, Qingyuan; Liang, Liang

doi:10.1016/j.jclepro.2015.04.095

Cited by 48 publications

(18 citation statements)

References 49 publications

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“…Olatubi and Dismukes considered the influences of burning technology and operation efficiency from the cost perspective [4]. Then, more research factors from new perspectives were considered in the DEA framework, such as emissions, pollutants and factor input level [5][6][7]. However, constrained by the limits of DEA, the analysis framework did not consider the undesired output factors comprehensively.…”

Section: Literature Reviewsmentioning

confidence: 99%

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Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Liu

Yang

2017

Energies

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Abstract:Under the background of a new round of power market reform, realizing the goals of energy saving and emission reduction, reducing the coal consumption and ensuring the sustainable development are the key issues for thermal power industry. With the biggest economy and energy consumption scales in the world, China should promote the energy efficiency of thermal power industry to solve these problems. Therefore, from multiple perspectives, the factors influential to the energy efficiency of thermal power industry were identified. Based on the economic, social and environmental factors, a combination model with Data Envelopment Analysis (DEA) and Malmquist index was constructed to evaluate the total-factor energy efficiency (TFEE) in thermal power industry. With the empirical studies from national and provincial levels, the TFEE index can be factorized into the technical efficiency index (TECH), the technical progress index (TPCH), the pure efficiency index (PECH) and the scale efficiency index (SECH). The analysis showed that the TFEE was mainly determined by TECH and PECH. Meanwhile, by panel data regression model, unit coal consumption, talents and government supervision were selected as important indexes to have positive effects on TFEE in thermal power industry. In addition, the negative indexes, such as energy price and installed capacity, were also analyzed to control their undesired effects. Finally, considering the analysis results, measures for improving energy efficiency of thermal power industry were discussed widely, such as strengthening technology research and design (R&D), enforcing pollutant and emission reduction, distributing capital and labor rationally and improving the government supervision. Relative study results and suggestions can provide references for Chinese government and enterprises to enhance the energy efficiency level.

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Section: Literature Reviewsmentioning

confidence: 99%

“…Then, the DEA model can be described by the non-Archimedes infinitesimal exponent ε, as shown in Equation (6).…”

Section: Ccr Modelmentioning

confidence: 99%

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Liu

Yang

2017

Energies

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“…In fact, the fuzzy DEA approach is similar to the interval DEA approach. The difference between the two approaches is that the fuzzy DEA approach is based on the fuzzy theory while the interval DEA approach uses deterministic techniques [31][32][33][34][35][36]. Zha et al developed a Halo DEA approach (Halo effect is a psychological term) to impute missing data [37].…”

Section: Introductionmentioning

confidence: 99%

Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

et al. 2020

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Traditional data envelopment analysis (DEA) models assume that all the inputs and outputs data are available. However, missing data is a common problem in data analysis. Although several scholars have developed techniques to conduct DEA with missing data, these techniques have some disadvantages. A multi-criteria evaluation approach is proposed to measure the efficiency of decision making units (DMUs) with missing data. In this approach, analysts first estimate the upper and lower bounds of DMUs' efficiency using the proposed I-addIDEA-U models (interval additive integer-valued DEA models with undesirable outputs) that can be applied to address integer-valued variables and undesirable outputs. Then, DMUs' "relative" efficiency is evaluated using the proposed "Halo + Hot deck" DEA method (if there is no correlation between variables) or regression DEA techniques (if there is a correlation between variables). Finally, the multi-index comprehensive evaluation method is applied to determine which scenario (the lower bound of efficiency, the "relative" efficiency, or the upper bound of efficiency) should be selected. With a case study, it is shown that the proposed multicriteria evaluation approach is more effective than traditional approaches such as the mean imputation DEA method, the deletion DEA method, and the dummy entries DEA method.

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“…While such studies may provide structured ways of thinking about the various issues, whether the aggregate relationships are consistent with the principles of microeconomics has been less explored. A few exceptions are the studies by Fisher-Vanden et al (2004, Wu et al (2015) and Wei et al (2015), where firmlevel data are used for empirical analysis. Based on 2500 observations of China's most energy-intensive firms, Fisher-Vanden et al (2004) investigate the determinants of the decline of energy intensity in China and conclude that the decline contributed about 50% to efficiency improvement a the firm level and 50% to sectoral changes.…”

Section: Introductionmentioning

confidence: 99%

“…In their 2006 study, the authors utilize an extended dataset with firm characteristics and technological innovation activities to identify the key determinants of rising energy productivity within China's industrial sector. Wu et al (2015) apply an integrated enhanced Russell model to evaluate the environmental performance of 30 thermal power firms in China in 2010. Using a non-parametric technique, Wei et al (2015) examine the energy saving and CO 2 abatement potential of coal-fired power generating firms in China.…”

Section: Introductionmentioning

confidence: 99%

Resource misallocation in Chinese manufacturing enterprises: evidence from firm-level data

Wei

2017

Journal of Cleaner Production

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Measuring the performance of thermal power firms in China via fuzzy Enhanced Russell measure model with undesirable outputs

Cited by 48 publications

References 49 publications

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

Resource misallocation in Chinese manufacturing enterprises: evidence from firm-level data

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