Operational and non-operational performance evaluation of thermal power plants in Iran: A game theory approach

Using Shapley value in multi-objective data envelopment analysis: Power plants evaluation with multiple frontiers

“…The various coalitions have been presented in the second column of Table 3. According to model (10) and Eqs. (8) and (9) of Table 3.…”

“…The inputs and outputs for observed DMUs are known but the planned DMUs correspond to operation points whose desired outputs levels are known and for which the minimum inputs costs need to be computed. Jahangoshai Rezaee et al [10] used the bargaining game for measuring the operational and non-operational performance of thermal power plants in Iran. The case study used in this paper is to explore how two categories of measures are integrated for measuring performance.…”

“…PP 1 Weakly efficient High Very low High Moderate PP 2 Moderately efficient Efficient Very low Efficient Efficient PP 3 Weakly efficient Moderate Very low High Moderate PP 4 Fully efficient Efficient Efficient Efficient Efficient PP 5 Moderately efficient Moderate High Efficient Moderate PP 6 Moderately efficient Moderate Low High Efficient PP 7 Moderately efficient Moderate High High Efficient PP 8 Fully efficient Efficient Efficient Efficient Efficient PP 9 Strongly efficient Moderate Efficient Efficient Efficient PP 10 Strongly efficient Efficient High Efficient Efficient PP 11 Fully efficient Efficient Efficient Efficient Efficient PP 12 Weakly efficient Efficient Very low Moderate High PP 13 Fully efficient Efficient Efficient Efficient Efficient PP 14 Strongly efficient High Efficient Efficient Efficient PP 15 Weakly efficient Efficient Very low Low Low PP 16 Weakly efficient Low Very low Low Low PP 17 Strongly efficient Efficient Efficient High Efficient PP 18 Weakly efficient Low Moderate Moderate Moderate PP 19 Strongly efficient High High High Efficient PP 20 Moderately efficient Low Efficient Efficient Efficient…”

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

“…In recent years the applications of DEA have increased in field of environmental and energy economics, in areas, for example; energy performance [56][57][58][59][60][61][62], energy savings [63][64][65][66][67][68][69], and energy efficiency [70][71][72][73][74][75][76][77][78][79], Shi et al [80][81][82][83][84][85][86][87][88][89]. In this regard, various DEA models were employed in different industries and sectors such as non-radial DEA, bootstrap DEA, CCR and BCC models, DEA window analysis, non-radial and constant returns to scale (CRS), DEA frontier, (VRS), directional distance function (DDF), DEA-Malmquist, slacks-based DEA, DEA-bargaining game, DEA-MBP model, network DEA, stochastic DEA, stochastic network DEA, double-bootstrap DEA, dynamic environmental DEA, stochastic frontier analysis (SFA), radial stochastic DEA, network range adjusted environmental DEA, fuzzy dynamic network-DEA, constant returns to scale (CRTS), variable returns to scale (VRTS), DEA discriminant analysis (DEA-DA), fuzzy network slack-based measure (SBM) model, interval DEA-CCR, super-efficient DEA (SE-DEA) and other DEA models.…”

“…Welch and Barnum [72] used a DEA-MBP model for the efficiency of electricity generation. Rezaee et al [70] integrated the DEA-bargaining game models for thermal power plants. Wu et al [67] used a two-stage network DEA to evaluate emission reduction efficiency and energy saving in the 30 municipalities, provinces, and autonomous of China's regional.…”