Three-Stage Environmental Efficiency Evaluation of Iran’s Power Industry: Network Data Envelopment Analysis Approach

Document Type : Research Paper

Authors

1 . Assistant Professor, Faculty of Economics, Kharazmi University, Tehran, Iran

2 kharazmi

Abstract

The power industry of Iran needs to increase environmental efficiency through reducing pollution emissions and losses to achieve sustainable development and improving technical and economic performance. So considering undesirable outputs beside desirable outputs has important role on the power industry performance. Data envelopment analysis (DEA) widely used in evaluating the efficiency of the electricity industry. In traditional DEA relative efficiency of Decision Making Units (DMUs) are calculated with multiple inputs and outputs but ignoring the internal structure or links between organization or manufacturing process divisions is a big problem of traditional models. Network models can deal with this big disadvantage and consider inefficiency more accurately. In this study environmental efficiency of 15 Iranian electric power companies has been evaluated during the period (2010-2014) using non-radial Slack Based Measure (SBM) with three stage network structure. Power grid in the country has been made in three parts namely "production", "transmission" and "distribution". They are dependent to each other by two links namely power generated and power transmitted and overall efficiency of power industry is determined by them. Results of the study indicate that generation division effects on overall efficiency more than two others and reduces companies efficiency significantly. Khuzestan company has the highest efficiency and Gharb company belongs to the worst performance. Results of this study can recognize overall condition of electric power companies and provide policy for improving their performance.
JEL Classification: Q43, Q53, C67, C61, Q57

Keywords

Main Subjects

References

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History

  • Receive Date: 18 February 2017
  • Revise Date: 25 September 2017
  • Accept Date: 26 December 2017

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