DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants

Liu, Xiaohong; Chu, Junfei; Yin, Pengzhen; Sun, Jiasen

doi:10.1016/j.jclepro.2016.04.069

Cited by 134 publications

(77 citation statements)

References 47 publications

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“…Therefore, it is important to maintain sustainable development of banks. In this section, we provide a numerical example for evaluating operation performance of sustainable development of 15 15 ), respectively. Owing to operating similar business, these banks compete with each other.…”

Section: Example and Discussionmentioning

confidence: 99%

“…In the following, we will accomplish Step 2 to 6. In Step 2, we use the optimal attribute weights of each bank to calculate the cross-efficiency of the 15 listed banks by utilizing Formula (15) and the results are presented in Table 5. In Table 5, E k j (k = 1, 2, · · · , 15) denotes the peer evaluation of DMU k to DMU j .…”

Section: Example and Discussionmentioning

confidence: 99%

“…In this subsection, we present the following specific procedures of construction process. First, we can obtain the efficiency scores E kk , E k j , E jk , E jj (k, j = 1, 2, · · · , n) by solving model (13) and calculating Equation (15). Then, we construct corresponding fuzzy preference relations (FPRs)R = r k j n×n , the element of R is defined as follows:…”

Section: Constructing the Consistent Fuzzy Preference Relations For Rmentioning

confidence: 99%

“…Nevertheless, attribute values are known, so we have to obtain attribute weights by the aforementioned approaches. However, DEA is a nonparametric programming efficiency rating technique for evaluating the relative efficiency of DMUs with multiple inputs and outputs, whose evaluation results come from input and output data [15][16][17]. Compared with other methods, attribute weights derived by DEA is relatively objective.…”

mentioning

confidence: 99%

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Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

et al. 2020

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Multi-attribute decision making (MADM) is a cognitive process for evaluating data with different attributes in order to select the optimal alternative from a finite number of alternatives. In the real world, a lot of MADM problems involve some random and ordinal variables. Therefore, in this paper, a MADM method based on stochastic data envelopment analysis (DEA) cross-efficiency with ordinal variable is proposed. First, we develop a stochastic DEA model with ordinal variable, which can derive self-efficiency and the optimal weight of each attribute for all decision making units (DMUs). To further improve its discrimination power, cross-efficiency as a significant extension is proposed, which utilizes peer DMUs' optimal weight to evaluate the relative efficiency of each alternative. Then, based on self-efficiency and cross-efficiency of all DMUs, we construct corresponding fuzzy preference relations (FPRs) and consistent fuzzy preference relations (FPRs). In addition, we obtain the priority weight vector of all DMUs by utilizing the row wise summation technique according to the consistent FPRs. Finally, we provide a numerical example for evaluating operation performance of sustainable development of 15 listed banks in China, which illustrates the feasibility and applicability of the proposed MADM method based on stochastic DEA cross-efficiency with ordinal variable.Sustainability 2020, 12, 2375 2 of 15 two parts: classifying and ranking. Classifying can be considered as the grouping of the alternatives based on the similarities of attributes. Ranking is defined as the rank of alternatives from the optimal to the worst [8]. In recent years, some methods have been proposed to handle MADM problems, such as total sum (TS) method [9], simple additive weighting (SAW) method [10], the analytic hierarchy process (AHP) method [11], multiplicative analytic hierarchy process (MAHP) method [12], the technique for order preference by similarity to ideal solution (TOPSIS) method [13], and data envelopment analysis (DEA) method [14].In MADM problems, we need some decision making information including attribute values and attribute weights, which denote the characteristics of alternatives and relative importance of attributes, respectively [7]. Nevertheless, attribute values are known, so we have to obtain attribute weights by the aforementioned approaches. However, DEA is a nonparametric programming efficiency rating technique for evaluating the relative efficiency of DMUs with multiple inputs and outputs, whose evaluation results come from input and output data [15][16][17]. Compared with other methods, attribute weights derived by DEA is relatively objective. Therefore, DEA has been widely applied in many fields for different purposes [18][19][20], such as assessment of environmental sustainability [21,22], supplier selection [23][24][25], and evaluation of the influence of E-marketing on hotel performance [26].Due to the inherent complexity and competition of the real world, MADM problems often involve some random and ordina...

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Section: Example and Discussionmentioning

confidence: 99%

Section: Example and Discussionmentioning

confidence: 99%

Section: Constructing the Consistent Fuzzy Preference Relations For Rmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

et al. 2020

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“…Entani and Tanaka [9] have already proposed the interval DEA model to obtain the efficiency interval. The efficiency interval is represented by its upper and lower bounds.…”

Section: Interval Dea Modelsmentioning

confidence: 99%

Hybrid cluster analyzing and data envelopment analysis with interval data

et al. 2017

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Data envelope analysis (DEA) is an approach to estimate the relative efficiency of decision making units (DMUs). Several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (CEM) were presented. Besides, a number of DEA models with interval data have been developed and ranking DMUs with such data was solved. However, presenting an obtained crisp data derived interval data is a critical problem, so that many researches were implemented so as to compute weights and averaging the interval data. In this paper we propose the new algorithm to find more suitable weight applying a data mining approach of DMU's data. For this purpose, we employed clustering 2 and pair-wise comparison matrix on given relative efficiency from CEM. Results indicate there is meaningful different between efficiency of DMUs with lower bound and that of DMUs with upper bound.

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Is the Green Total Factor of Energy Efficiency Rising or Falling? Evidence from the Belt and Road Initiative Countries

Ma,

Guo,

Xiao

et al. 2024

Energy Tech

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“The Belt and Road Initiative” (BRI) seeks to attain high‐quality development and sustainability. This study examines the green total factor energy efficiency (GTFEE) of 58 BRI countries from 2005 to 2019 and its influence factors using the Super slacked‐based measurement and Tobit models. The findings indicate that: 1) Singapore and the other seven countries have GTFEEs above 0.9, which is better than other countries; seven out of 58 countries have GTFEEs below 0.5. 2) Due to regional disparities, Southeast Asia, Central, and Eastern Europe have the greatest GTFEE, followed by South Asia, West Asia, and the Commonwealth of Independent States, and the lowest is in Central Asia, China, and Mongolia. 3) Compared to developing economies, developed economies have a higher GTFEE. 4) Between 2005 and 2019, the overall BRI energy efficiency dropped from 0.75 to 0.56. 5) According to Tobit results, for every 1% increase in economic development and energy structure, GTFEE increases by 0.08% and 0.31% points, respectively, and for every 1% increase in trade and land area, GTFEE decreases by 0.04 and 0.05, respectively. Therefore, BRI countries should strengthen international cooperation, develop advanced technologies, and increase the proportion of renewable energy utilization.

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DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants

Cited by 134 publications

References 47 publications

Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

Hybrid cluster analyzing and data envelopment analysis with interval data

Is the Green Total Factor of Energy Efficiency Rising or Falling? Evidence from the Belt and Road Initiative Countries

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