Document Type : applicative
Authors
1
PhD Student in Economics, Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran.
2
Professor of Economics, Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran.
3
Associate Professor of Economics, Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran.
10.30473/jier.2026.78880.1558
Abstract
This study evaluates the technical efficiency and total factor productivity (TFP) changes of Iranian thermal power plants using both parametric and nonparametric approaches, with a particular focus on their implications for electricity sector regulation. To this end, an output-oriented distance function was estimated using the Generalized Maximum Entropy (GME) method with a translog functional form, alongside an output-oriented Data Envelopment Analysis (DEA) model under the assumption of variable returns to scale (VRS). The analysis is based on an 11-year panel dataset of Iranian thermal power plants comprising steam, gas, and combined-cycle with different ownership structures.
The results indicate that the average technical efficiency during the study period was 0.833 under the DEA model and 0.607 under the GME model. This difference confirms the sensitivity of efficiency estimates to methodological choice and functional assumptions. Combined-cycle power plants achieved the highest efficiency under the DEA model (0.940), whereas efficiency differences across ownership structures were negligible. The Malmquist Productivity Index reveals a slight decline in total factor productivity over the study period, primarily driven by technological regress (technical change, TC), while efficiency change (EC) remained relatively stable. This study recommends that policymakers focus on technology modernization, fuel optimization, and benchmarking-based regulation, and that future research employ the combination of DEA and GME as a more robust foundation for decision-making.
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