Technical and environmental performance assessment of the Iranian power plants: a semi-disposal DEA approach

    Masod Mokari Affiliation
    ; Mojtaba Ghiyasi Affiliation
    ; Ali Emami Meybodi Affiliation


One of the most important issues is to achieve maximum production of energy from a particular energy source, which ensures the complete protection of the environment. The current paper as the first application of flexible and powerful semi-disposability approach, performs an empirical technical and environmental efficiency analysis for 39 natural gas and gasoline power plants, including governmental and private property, during the years 2011–2016. Different scenarios for environmental analysis, namely, weak disposability, strong disposability and semi-disposability with different returns to scale assumptions are performed in the analysis. The primary results of multivariate assessment based on constant returns to the scale shows that 7 power plants with state ownership and 8 power plants with private ownership were among the most efficient power plants from the technical-environmental perspective. Parametric and non-parametric tests are performed and the result shows better performance of private power plants compared with governmental power plants. 

Keyword : technical-environmental efficiency, private and governmental power plant, semi-disposability, return to scale, data envelopment analysis

How to Cite
Mokari, M., Ghiyasi, M., & Emami Meybodi, A. (2022). Technical and environmental performance assessment of the Iranian power plants: a semi-disposal DEA approach. Journal of Environmental Engineering and Landscape Management, 30(2), 235-248.
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May 23, 2022
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Abdulwakil, M. M., Abdul-Rahim, A. S., & Alsaleh, M. (2020). Bioenergy efficiency change and its determinants in EU-28 region: Evidence using Least Square Dummy Variable corrected estimation. Biomass Bioenergy, 137, 105569.

Ahmad, A., Yasin, N. M., Derek, C., & Lim, J. (2011). Microalgae as a sustainable energy source for biodiesel production: A review. Renewable and Sustainable Energy Reviews, 15(1), 584–593.

Alsaleh, M., & Abdul-Rahim, A. (2018). Determinants of cost efficiency of bioenergy industry: Evidence from EU28 countries. Renewable Energy, 127, 746–762.

Alsaleh, M., Abdul-Rahim, A., & Mohd-Shahwahid, H. (2017). Determinants of technical efficiency in the bioenergy industry in the EU28 region. Renewable Sustainable Energy Reviews, 78, 1331–1349.

Bongo, M. F., Ocampo, L. A., Magallano, Y. A. D., Manaban, G. A., & Ramos, E. K. F. (2018). Input–output performance efficiency measurement of an electricity distribution utility using super-efficiency data envelopment analysis. Soft Computing, 22(22), 7339–7353.

Bonilla, J., Coria, J., & Sterner, T. (2018). Technical synergies and trade-offs between abatement of global and local air pollution. Environmental Resource Economics, 70(1), 191–221.

Çelen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy, 63, 300–310.

Chen, L., Wang, Y.-M., & Lai, F. (2017). Semi-disposability of undesirable outputs in data envelopment analysis for environmental assessments. European Journal of Operational Research, 260(2), 655–664.

Cheng, Y., Lv, K., Wang, J., & Xu, H. (2018). Energy efficiency, carbon dioxide emission efficiency, and related abatement costs in regional China: A synthesis of input–output analysis and DEA. Energy Efficiency, 1–15.

Emami Meybodi, A., & Mokari, M. (2016). Optimization of Iranian gas refineries with environmental approach in Data Envelopment Analysis framework. Economic Strategy, 5(16), 211–240.

Emrouznejad, A., Yang, G.-l., & Amin, G. R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7), 1079–1090.

Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90–98.

Ghiyasi, M. (2017). Industrial sector environmental planning and energy efficiency of Iranian provinces. Journal of Cleaner Production, 142, 2328–2339.

Ghiyasi, M. (2019). Emission utilization permission based on environmental efficiency analysis. Environmental Science Pollution Research, 26(21), 21295–21303.

International Energy Agency. (2018). Global energy & CO2 status report 2017.

Miao, Z., Baležentis, T., Shao, S., & Chang, D. (2019). Energy use, industrial soot and vehicle exhaust pollution—China’s regional air pollution recognition, performance decomposition and governance. Energy Economics, 83, 501–514.

Rácz, V. J., & Vestergaard, N. (2016). Productivity and efficiency measurement of the Danish centralized biogas power sector. Renewable Energy, 92, 397–404.

Riccardi, R., Oggioni, G., & Toninelli, R. (2012). Efficiency analysis of world cement industry in presence of undesirable output: Application of data envelopment analysis and directional distance function. Energy Policy, 44, 140–152.

Rozali, N. E. M., Alwi, S. R. W., Ho, W. S., Manan, Z. A., & Klemeš, J. J. (2016). Integration of diesel plant into a hybrid power system using power pinch analysis. Applied Thermal Engineering, 105, 792–798.

Sueyoshi, T., & Goto, M. (2015). Japanese fuel mix strategy after disaster of Fukushima Daiichi nuclear power plant: Lessons from international comparison among industrial nations measured by DEA environmental assessment in time horizon. Energy Economics, 52, 87–103.

Sueyoshi, T., & Goto, M. (2017). World trend in energy: An extension to DEA applied to energy and environment. Journal of Economic Structures, 6(1), 13.

Sueyoshi, T., Yuan, Y., & Goto, M. (2017). A literature study for DEA applied to energy and environment. Energy Economics, 62, 104–124.

Tone, K., & Tsutsui, M. (2011). Applying an efficiency measure of desirable and undesirable outputs in DEA to US electric utilities. Journal of CENTRUM Cathedra: The Business and Economics Research Journal, 4(2), 236–249.

Vaninsky, A. (2006). Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis. Energy Economics, 28(3), 326–338.

von Geymueller, P. (2009). Static versus dynamic DEA in electricity regulation: The case of US transmission system operators. Central European Journal of Operations Research, 17(4), 397.

Wegener, M., & Amin, G. R. (2019). Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas. Expert Systems with Applications, 122, 369–375.

Wu, J., Xia, P., Zhu, Q., & Chu, J. (2019). Measuring environmental efficiency of thermoelectric power plants: A common equilibrium efficient frontier DEA approach with fixed-sum undesirable output. Annals of Operations Research, 275, 731–749.

Wu, X., Chen, Y., Guo, J., & Gao, G. (2018). Inputs optimization to reduce the undesirable outputs by environmental hazards: A DEA model with data of PM 2.5 in China. Natural Hazards, 90(1), 1–25.

Yang, H., & Pollitt, M. (2010). The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants. Energy Policy, 38(8), 4440–4444.

Zhou, H., Yang, Y., Chen, Y., & Zhu, J. (2018). Data envelopment analysis application in sustainability: The origins, development and future directions. European Journal of Operational Research, 264(1), 1–16.

Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.