Data envelopment analysis application in sustainability: The origins, development and future directions

Zhou, Haibo; Yang, Yi; Chen, Yao; Zhu, Joe

doi:10.1016/j.ejor.2017.06.023

Cited by 277 publications

(123 citation statements)

References 162 publications

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“…The DEA, proposed by Charnes et al, is a nonparametric statistical method to evaluate the relative efficiency of multi‐inputs and multi‐outputs system by mathematical programming . This method is constantly enriched and perfected by Farrell et al In this method, a set of sample data is used as a DMU, and a number of DMUs constitute an efficiency evaluation group . Then, the energy efficiency is determined by the ratio of inputs to outputs of this evaluation group.…”

Section: Evaluation Methods Of Energy Efficiency In the Isimentioning

confidence: 99%

“…59 This method is constantly enriched and perfected by Farrell et al 59 In this method, a set of sample data is used as a DMU, and a number of DMUs constitute an efficiency evaluation group. 59,60 Then, the energy efficiency is determined by the ratio of inputs to outputs of this evaluation group. At present, the Charnes, Copper and Rhodes model (CCR), 61 Banker, Charnes and Cooper model (BCC), 61,62 and slack-based model 62 are widely used to evaluate the energy efficiency of the ISI.…”

Section: Data Envelopment Analysismentioning

confidence: 99%

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Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry

Sun

et al. 2019

Int J Energy Res

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Summary To promote the sustainable development of the iron and steel industry (ISI), evaluation methods and influencing factors of energy efficiency have been studied in the past decades. A systematic review of these methodologies and influencing factors is presented in this paper. Initially, this paper states the development, current situation, and energy consumption of the world's ISI. Then, the definition of energy efficiency is described. After that, the evaluation methods are categorized as single‐factor methods (including these indicators of thermodynamic, thermos‐physical, thermo‐economic, and economic) and multifactor methods (including parametric and nonparametric methods). Furthermore, typical evaluation methods are highlighted. Thermal analysis method and exergy analysis method are routinely combined to optimize the energy utilization. Data envelopment analysis (DEA) and stochastic frontier analysis (SFA) are widely concerned and well developed to gain a better economic benefit. Malmquist index method is often associated with DEA to analyze the technical process and return to scale. Some emerging methods are proposed, for example, the three‐stage DEA method together with DEA and SFA. Finally, the influencing factors of energy efficiency in the ISI are discussed, including industrial structure, technological level, energy consumption structure, price, and so on. It is suggested that optimizing energy structure and improving technology level are effective measures to improve the energy efficiency in the ISI.

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Section: Evaluation Methods Of Energy Efficiency In the Isimentioning

confidence: 99%

Section: Data Envelopment Analysismentioning

confidence: 99%

Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry

Sun

et al. 2019

Int J Energy Res

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“…It is often defined as

D false(x, y, b; g false) = \sup \{β : (y, b) + β g \in P (x)\}

In Eqn , suppose a vector of inputs x to produce a vector of desirable output y and undesirable output

b \cdot g = (g_{y}, g_{b})

is output adjustment vector for outputs. Many researchers have adopted EPM framework to evaluate production efficiency for decision‐making units (DMUs) in order to supply low‐carbon production information . Parametric and nonparametric DDF are chosen to construct the evaluation model.…”

Section: Literature Reviewmentioning

confidence: 99%

Low‐carbon economy transformation performance evaluation and spatial trends in China: a provincial aspect

et al. 2019

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Regional heterogeneity during low‐carbon economy development among provinces in China should be considered with more concerns by central government. Spatial coordination can bring more opportunities for underdeveloped provinces. Under this background, provincial low‐carbon economy transformation performance is evaluated during 2000–2016 and spatial characters are analyzed to supply detailed development information. Based on parametric input–output evaluation model and linear programming method, provincial low‐carbon economy transformation performance is evaluated. Spatial analysis methods such as Moran index, Moran scatter diagram, and Markov chain are implemented to analyze their spatial characters and dynamic trends. Main results are as follows: First, linear programing supplies reliable parameter results to evaluate the transformation performance. Mean value rises during 2000−2011 and there is a slight downward trend during 2013−2016. Economy transformation performance is still at lower medium level for most provinces nowadays and there is a long way to go further. Second, according to Markov chain results, more than 90% provinces exist as state self‐locking and less than 10% may exist as state jumping. Third, spatial correlation exists among provinces and ‘lower−lower’ type dominates with respect to low‐carbon economy transformation performance. They are mainly underdeveloped provinces in northwestern China. Absorbing greener production technology is the best choice for them. Yangtze River Delta provinces such as Shanghai, Jiangsu, and Zhejiang are of ‘higher–higher’ type. Regional cooperatives can exert lots of potentials and are beneficial to stimulate transformation performance. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…In the literature, researchers applied different methodologies to measure corporate sustainability such as balanced scorecard method (Hsu, Hu, Chiou, & Chen, 2011;Junior, de Oliveira, & Helleno, 2018;Kang, Cgiang, Huangthanapan, & Downing, 2015; Medel-Gonzalez, Garcia-Avila, Salomon, Marx-Gomez, & Hernandez, 2016;Nikolaou & Tsalis, 2013;Nurcahyo, Pustiwari, & Gabriel, 2018), life cycle assessment (Guinee et al, 2008;Hu, Kleijn, Bozhilova-Kisheva, & Di Maio, 2013;Iacovidou et al, 2017), and data envelopment analysis (Zhou, Yang, Chen, & Zhu, 2018;Yu, Wang, & Ho, 2016;Sueyoshi & Wang, 2014;Chang, Zhang, Danao, & Zhang, 2013;Lee & Saen, 2012). In the literature, researchers applied different methodologies to measure corporate sustainability such as balanced scorecard method (Hsu, Hu, Chiou, & Chen, 2011;Junior, de Oliveira, & Helleno, 2018;Kang, Cgiang, Huangthanapan, & Downing, 2015; Medel-Gonzalez, Garcia-Avila, Salomon, Marx-Gomez, & Hernandez, 2016;Nikolaou & Tsalis, 2013;Nurcahyo, Pustiwari, & Gabriel, 2018), life cycle assessment (Guinee et al, 2008;Hu, Kleijn, Bozhilova-Kisheva, & Di Maio, 2013;Iacovidou et al, 2017), and data envelopment analysis (Zhou, Yang, Chen, & Zhu, 2018;Yu, Wang, & Ho, 2016;Sueyoshi & Wang, 2014;Chang, Zhang, Danao, & Zhang, 2013;Lee & Saen, 2012).…”

Section: Introductionmentioning

confidence: 99%

Corporate sustainability performance measurement based on a new multicriteria sorting method

Küçükbay

Sürücü

2019

Corp Soc Responsibility Env

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The concept of sustainability deals with protecting and sustaining society and environment for future generations while trying to meet the maximization of market capitalization objective. Despite the increased awareness of corporate sustainability, questions remain on how to measure performance. The objective of this paper is to propose a new measurement method, which is Multimoora Sort, for corporate sustainability performance and make an empirical case with the 25 companies listed in Fortune 500 USA. We chose four economic subcriteria, two environmental subcriteria, and four social subcriteria for the methodology. The results of the study show that the proposed methodology is a practical and efficient decision method.The most important contribution of the study to the literature is to present a new evaluation method in the measurement of sustainability performance as well as the results of the empirical case are supported by the resource dependency theory, institutional theory, and stakeholder theory.

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Data envelopment analysis application in sustainability: The origins, development and future directions

Cited by 277 publications

References 162 publications

Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry

Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry

Low‐carbon economy transformation performance evaluation and spatial trends in China: a provincial aspect

Corporate sustainability performance measurement based on a new multicriteria sorting method

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