Stochastic data envelopment analysis with reducible outputs and expandable inputs: An application in power plants

Abstract

Abstract Clean production of electricity is not only cost-effective but also effective in reducing pollutants. Toward this end, the use of clean fuels is strongly recommended by environmentalists. Benchmarking techniques, especially data envelopment analysis, are an appropriate tool for measuring the relative efficiency of firms with environmental pollutants. In classic data envelopment analysis models, decision-makers are faced with production processes in which reducible inputs are used to produce expandable outputs. In this contribution, we consider production processes in which some throughputs are reducible and some others are expandable. This paper focuses on the efficiency evaluation of power plants when the input and output data are given in stochastic form and some inputs and outputs need to be reduced and some others must be increased. A stochastic directional distance function model is proposed to calculate the relative technical efficiency of firms. To demonstrate the applicability of the procedure, we present an empirical application wherein we employ Iranian power plants. An interesting finding in this analysis is that the direction we choose significantly affects the prioritization of the plants.