Assessing Environmental and Technical Efficiency of Power Plants: Insights for Policy and Sustainability

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Although many power plants exhibit impressive efficiency, the study emphasizes that there is still much space for improvement in technical operations and environmental effect reduction. In contrast, we find 15 environmentally efficient power plants and six power plants that are technically efficient. We further find 53 percent of wasting resources on average for all power plants, considering technical performance, and 57 percent of wasting resources on average, considering environmental performance. The findings emphasize the need for targeted interventions to optimize energy production processes. Policymakers are encouraged to leverage these insights to design strategies that enhance operational efficiency, reduce emissions, and promote sustainable energy production. Power Plants Energy Supply Efficiency Eco-efficiency Environmental Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1. 1 Background The electricity industry is one of the most essential factors in developing industries, along with other ‎manufacturing and transformation industries. It has a significant strategic ‎role in promoting a country's economic and industrial goals. Since these resources are limited, efficient use is inevitable (Emami, Ravanshadnia, & Rahimi, 2019 ). Therefore, in the country's development programs, the governments try to free the price of energy carriers and remove the related subsidies on their agenda, which increases the cost of products and services. The energy industry is the foundation of any country's infrastructure, propelling economic prosperity and social well-being. In Iran, a nation affluent in energy resources, the allocation and utilization of power plant assets have significant strategic weight. Nowadays, energy supply is one of the most crucial concerns for emerging nations. Meanwhile, power facilities play a significant role in generating and distributing electrical energy. Examining the density of resources in power plants may help better understand these energy-producing units' efficiency and productivity. Energy production and supply are vital issues for all societies from different points of view. Nowadays, any interruption in this sector is like a nightmare and may have a non-compostable effect on other parts of human life. Energy production, specifically electricity production, usually involves producing some emissions as a by-product that affects human life from another angle. Therefore, having green electricity, namely, electricity with no emissions, is also vital and essential. Environmental issues are becoming a more significant concern regarding expanding energy use in different sectors. 1.2. Problem The growing demand for electricity can be attributed to the economic structure, climate change, and technological structure. One of these key strategies and practices is identifying and measuring the efficiency of the environmental performance of electricity production firms (Ederer, 2015 ). One of the most critical issues and concerns is achieving maximum energy production from a particular energy source. The demand for electricity has been growing, and different factors might be affecting this. On the other side, we are constrained by limited resources. Thus, efficiency plays an important role not only in the production process but also in the process of electricity distribution. The performance of distribution firms is critical in the sense that their performance is a link between electricity suppliers, which are different types of power plants, and electricity consumers, which could be said to be the whole society, including households, firms, industries, economic sectors, etc. 1.3. Research gap Based on the latest search of authors, there is no study to analyze Iranian power plants' technical and environmental aspects using advanced frontier approaches. Thus, the current article analyzes the necessity of investigating the complex resource density of Iran's power plants using a potent and critical analytical framework for conscious energy policy-making and strategic planning. This approach enables us to evaluate the efficiency of power facilities and analyze their resource density. Previous research on measuring the efficiency of power facilities in Iran has been chiefly done using standard data coverage analysis techniques. Still, thus far, no study has been done to assess the resource density of these units at the same time. Meanwhile, the research on resource density may give significant information to decision-makers and energy administrators to implement suitable solutions to increase the efficiency and productivity of power facilities. The density approach in data envelopment analysis (DEA) will be used in the current study to examine the amount of resource density in Iran's power facilities. Using the DEA approach, a mathematical technique that examines the relative efficiency of decision-making units (DMU) by measuring their resource density, it conducts a technical and environmental analysis for 46 power plants. 1.4. Literature review Using the DEA approach, studies examined the country's electricity production at thermal power plants. The results show that the average efficiency of thermal power plants in the country under the premise of variable efficiency is 78 percent; in other words, with the same existing scenario and optimal utilization of the facilities, in the first instance, it is possible to produce 36 percent, and in the second case, 23 percent. Promote electricity in the country (Pourkazemi & Heydari, 2002 ). (Abbott, 2006 ) examined productivity and efficiency in the Australian electricity industry. The article has analyzed the changes that have occurred in the last 30 years in the Australian electricity industry using the data coverage analysis approach and the Malmquist index during the years 1969–1999. The results show that a significant improvement in the industry started in the mid-1980s; this improvement was more at the beginning of 1990 and until the end of 1999. Meanwhile, in the same direction, (Vaninsky, 2006 ) estimated the efficiency of electricity generation in the United States for 1991–2004 using data envelopment analysis. The results show that the relative stability in productivity from 1994–2004 at the levels of 0.99–100 is associated with a sharp decrease to 0.95 in the following years. This research predicts that the efficiency for 2010 is equal to 0.80–0.98, which means that the efficiency will decrease more than in the early years. Sadjadi & Omrani ( 2008 ) presented a DEA model with uncertain data for the performance assessment of electricity distribution companies. DEA has been widely used to benchmark electricity distribution companies for the past two decades. However, there is no study among many existing DEA approaches where the uncertainty in data is allowed and, at the same time, the distribution of the random data is permitted to be unknown. Further inquiries in an article on measuring productivity changes in electricity production management companies using data coverage analysis and the Malmquist index to evaluate the changes in the total productivity factor in electricity management companies during 2002–2014 have been paid. The evaluation results indicate that it has grown at an average rate of 1.023 during the years in question (Hoseini, 2012 ). On the other hand, (Wang, Wei, & Huang, 2018 ) investigated the environmental efficiency and minimization efficiency measurement of China's thermal power industry. It has used a DEA-based approach to evaluate environmental efficiency and reduce costs in China's thermal power industry. It is presented based on the results of new efficiency scales that help identify the potential of reducing the pollution of power plants. A cross-efficiency network DEA model for efficiency measurement of China's electricity generation-transmission system was proposed by Zhang, Wei, Li, & Ren (. It is possible to use the DEA approach to find parameters of reward and penalty schemes for electricity companies in Iran. (Simab & Haghifam, 2012 ). To diminish our environmental effect, methods for the environmental assessment of human activities are truly needed. However, different kinds of tools and methods are accessible, such as human and environmental risk assessment, the ecological footprint, material flow analysis, substance flow analysis, the physical input-output table, ecological network analysis, and energy or life cycle assessment (Loiseau, Junqua, Roux, & Bellon-Maurel, 2012 ). (Çelen, 2013 )presented a two-stage (DEA & Tobit) analysis to take into account the business environment variables that are beyond the control of distribution companies. In the first step, with the help of DEA, he determined the efficiency status of electricity distribution companies. Then, in the second step, using these performance scores calculated as dependent variables, he used the Tobit model to determine the business environment variables that may explain the performance scores. The performance of Iranian electricity distribution units was analyzed using a stochastic DEA approach (Azadeh, Haghighi, Zarrin, & Khaefi, 2015 ). Also, a corporate game DEA approach and principal component analysis were integrated for performance assessment of Iranian electricity distribution companies (Omrani, Beiragh, & Kaleibari, 2015 ). In a study, which is based on three stations in Bushehr, Bandar Abbas, and Chabahar, Roshan, Arab, & Klimenko, ( 2019 ) aimed to quantify the model climate change effects of the upcoming decades on the energy demand and carbon dioxide emissions of a dominating building brigade under hot and humid climates on the southern coast of Iran. Mirzaei & Bekri ( 2017 ) created a dynamic system model to simulate Iran's energy consumption and CO2 emission patterns from 2000 to 2025. Analysing the effects of various energy consumption factors on environmental quality considers energy policy factors. In another study by Tavassoli, Faramarzi, & Saen ( 2015 ), the slack-based DEA method was used to rank Iran's electricity distribution units. Two stages of the DEA-Tobit regression model for efficiency analysis of 39 wind power plants were utilized in the US. They found that more than half of the power plants are working efficiently, and newly installed power plants are more expensive and less productive than the currently installed power plants (Sağlam, 2017 ). Zurano-Cervelló, Pozo, Mateo-Sanz, Jiménez, & Guillén-Gosálbez ( 2019 ) analysed a representative sample of 102 smaller distributors in Spain using DEA methodology. Tenente, Carla Henriques, & da Silva (2020) utilized the DEA models for eco-efficiency evaluation of the electricity sector's consumption and production supply chains. In order to perform the multi-objective optimization of an energy, exergy, and economic analysis, Sukpancharoen & Prasartkaew ( 2021 ) studied combined heat and power plant systems. To meet the increasing demands for energy, CHPPs must operate under optimal conditions, which necessitates plant design and equipment improvements. To achieve this aim, they combined the DEA and input-output analysis and the eco-efficiency of the production and supply chains of the Electricity Sector measured in the European Union (Henriques, Gouveia, Tenente, & da Silva, 2022 ). Junsittiwate, Srinophakun, & Sukpancharoen ( 2022 ) concurrently simulated a bio-oil upgrading facility, a complete pyrolysis process model, and electricity production. Compared to the non-heat exchanger network design, implementing a heat exchanger network enhances the maximum heat recovery and the minimum capital cost implications. A network DEA approach for assessing the sustainability of the Iranian electricity distribution network was developed by Tavassoli, Ketabi, & Ghandehari ( 2020 ). They found the best and worst performances to be 0.85 and 0.47, respectively. The Iranian electricity distribution network's sustainability was studied using a fuzzy DEA model (Tavassoli, Ketabi, & Ghandehari, 2022 ). In this study, some of the publications have been analyzed in diverse domains of the DEA method in distinct countries such as the US, China, Iran, etc. According to our issue, the multiple DEA method has different aspects presented in the second stage. Then, the study continued with the environmental assessment method to optimize with numerical outcomes. Based on the authors' best knowledge, there is no investigation into the technical and environmental performance of Iranian power plants using non-parametric methods. Thus, the article analyzes using a widely used method, DEA. The rest of the paper is organized as follows. Section 1 provides the introduction, including the literature review. Section 2 describes the research method used in the analysis, and Section 3 performs the technical and environmental efficiency analysis for Iranian power plants. The last section concludes the study and provides the policy implications. 2. Research Methodology The current article aims to study the performance of Iranian power plants not only in terms of technical efficiency but also from an environmental view, using advanced DEA approaches already in the literature. Analyzing technological and environmental issues is a critical prerequisite for reaching sustainable economic success in today's society. With increased globalization and the ever-growing demand for resources, companies and organizations must discover methods to function effectively while limiting their environmental impact. A renowned method for monitoring performance improvement presents a significant answer to this difficulty. DEA operates by establishing an efficient frontier based on the observable input and output data of the DMUs being examined. This frontier represents the highest possible output levels that can be achieved given the inputs used by the best-performing DMUs. This method has been widely used in the input-output analysis of different sectors, including the energy sector. See, for instance, Mardani, Zavadskas, Streimikiene, Jusoh, & Khoshnoudi ( 2017 ) and Xu, You, Li, & Shao ( 2020 ) for some recent literature review of application of DEA methods in energy economics and energy sectors including power plants. Specifically see Eguchi, Takayabu, & Lin, ( 2021 ), N. Zhang, Zhao, & Wang, ( 2022 ), C. Zhang & Wang, ( 2023 ) and Nakamoto & Eguchi, ( 2024 ), for power plants efficiency analysis. By measuring each DMU's performance at this frontier, DEA may discover inefficiencies and gain insights into areas for improvement. This information may then be utilized to improve resource allocation, boost productivity, and minimize waste, eventually leading to more sustainable and eco-friendly operations. In this inquiry, we aim to evaluate the traditional DEA model in the second phase in the scenario of additions and unfavorable outputs of the density model. Finally, we will determine the resource density level of power plants in Iran using a suitable method and examine the results. 2.1. Performance assessment In a production system, we usually desire the maximum impact and best performance by entering the lowest input and the largest output. As mentioned before, we have just input and output there, so the final model is presented as follows, which is the multiplier form of the DEA model: $$\:{min}{\varvec{v}}^{T}{\varvec{x}}_{0}-{\varvec{u}}^{T}{\varvec{y}}_{0}+\sigma\:s.t.\left\{\begin{array}{c}{\varvec{v}}^{T}{\varvec{x}}_{j}-{\varvec{u}}^{T}{\varvec{y}}_{j}+\sigma\:\ge\:0\\\:{\varvec{v}}^{T}{\varvec{x}}_{0}-{\varvec{u}}^{T}{\varvec{y}}_{0}=1\\\:v\ge\:0\:,u\ge\:0\end{array}\right\}$$ 1 where, the input-output vector of j-th unit includes m inputs \(\:{\varvec{x}}_{j}\in\:{\mathbb{R}}_{+}^{m}\) and s favorable outputs \(\:{\varvec{y}}_{j}\in\:{\mathbb{R}}_{+}^{s}\) . The variables "v" and "u" are supplied as associated variables for input(s) and output(s) respectively; moreover, "σ" the parameter determining the returns to scale of the production unit. It is important to point out that model (1) is a joint-oriented assessment model meaning that it seeks for the lowest input level for producing the highest output level of DMU o , that is, the unit under evaluation. Please note the objective function which is a minimization objective, positive sign of the input vector and negative sign of the output vector. In this setting, a production unit with a zero optimal value is efficient and the rest of them are inefficient. Regarding the number of power plants (46), number of inputs (2), and number of favorable outputs (1) we have n =46, m =2 and s =2, in our case study. 2.2. Environmental performance assessment The examination of technical and environmental issues evaluates the optimum output obtained during the manufacturing process, alongside negative results like pollution and waste byproducts. This is significant because unfavorable outputs are characteristics that are predicted but often appear during the actual manufacturing process along with desirable outputs. This approach has found significant worldwide application in examining environmental assessment difficulties (Sueyoshi & Goto, 2012 ). The multiplier form of DEA model for dealing with both favorable and unfavorable output can be found by the following linear programming, where \(\:{\varvec{z}}_{j}\in\:{\mathbb{R}}_{+}^{p}\) is the vector of unfavorable output. $$\:{min}{\varvec{v}}^{T}{\varvec{x}}_{0}-{\varvec{u}}^{T}{\varvec{y}}_{0}+{\varvec{w}}^{T}{\varvec{b}}_{0}+\sigma\:s.t.\left\{\begin{array}{c}{\varvec{v}}^{T}{\varvec{x}}_{j}-{\varvec{u}}^{T}{\varvec{y}}_{j}+{\varvec{w}}^{T}{\varvec{b}}_{j}+\sigma\:\ge\:0\\\:{\varvec{u}}^{T}{\varvec{y}}_{0}+{\varvec{w}}^{T}{\varvec{b}}_{0}=1\\\:v\ge\:0\:,u\ge\:0\:,\:w\ge\:0\end{array}\right\}$$ 2 Compared with model (1), we have an unfavorable output (p = 1) and its associated variable in model (2). As we indicated above, specific manufacturing processes include unfavorable outputs that cannot be analyzed by the classical multiplier DEA model. The above model aims to find the minimum input level to produce the maximum output level for DMU o . Please observe the sign of the input vector and output vector in the model's objective function (2). Since pollution cannot be ignored but controlled, the sign of the variable associated with this factor is fee, compared with the sign variables of inputs and outputs, which are non-negative. Comparing the programming model (1) and model (2) that are for the technical and environmental assessment, respectively, we see that the former does not care about the environment issues of undesirable output and treats them just as a normal output that may not be rational, at least in an environmental view. On the other side, the later model, namely, model (2), is concerned with the environmental aspects. Despite model (1), which wishes more outputs, including undesirable ones, model (2) can reduce undesirable outputs, including pollution. It is important to point out that any study based on the methodology in the current section is based on quantities and data. This approach is useless in the absence of data and may not be able to handle qualitative data. 3. Technical and environmental Performance assessment of Iranian power plant It has been estimated that roughly 18.5 percent of the electricity generated in Iran is squandered before it reaches consumers, owing to technological issues. Iran is among the top ten makers of gas turbines with a capacity of 160 megawatts (IBP, 2013 ). It is not only self-sufficient in power plant development but has also secured a number of contracts for constructing projects in adjacent states. The electricity sector is similarly extensively subsidized, and largely state-owned businesses manage power distribution, transmission, and generation. To meet the demands of the electrical sector, however, Iran is beginning to look into private investment. As of 2012, Iran had about 400 power plant units and 38 electrical distribution organizations that buy electricity from producers. Iran has approximately 100 companies that require more than 20MW of electricity annually. In 2021, Iran's energy generation was predominantly sourced from natural gas, accounting for 81% of total production. Oil supplied 14%, followed by hydropower at 4% and nuclear power at 1%. Both coal and non-hydro renewable sources each made up less than 1% of the energy generation mix. Mentioning a rise of 9,000 megawatts (MW) in the capacity of the country's nuclear power plants over the previous two years, officials claimed thermal power plants today account for 92 percent of Iran’s overall power production capacity. It is noticed that, according to the latest information on electricity production in 2022, Iran stands in 12th place with 353TWh in the world. In this study, we examine the efficiency of power plants with inputs such as oil and gas, with corresponding outputs like pollutant that contains CO 2 , heat, and production like electricity. In this study, we analyze the performance of 46 power plants in Iran based on the available input-output data at the time of the study. The data are extracted from the energy balance sheet of the Ministry of Energy of Iran. The main criteria of input and output selection are based on the production economic theory according to the rationality assumption in the economic theory. This means a rational decision maker desires more goods that are better and less goods that are worse. The former is input, and the latter is output in the DEA literature. Studying the literature review and considering available data, we yield two inputs (human resource and nameplate capacity) and two outputs (electricity generation and pollution). It is essential to point out that although pollution is an output of a power plant, it is not a desirable output, and we consider this in our analysis. DEA method is a well-known data-based mathematical programming approach based on the production economics for the performance and frontier analysis. Pollution-generating technologies are also dealt with in this method, conspiring available input-output data. As a powerful approach, it has been widely used in many real-world applications in the energy and environmental sectors. We used this method for both technical efficiency and, furthermore, environmental analysis. We consider two inputs and two outputs as described above for the technical efficiency analysis and then deal with pollution for the environmental efficiency analysis of Iranian power plants. Table 1 Table 1 Descriptive Statistics Variable Mean Min Max SD Human Resource 44.76087 11 89 19.87262 Nameplate capacity 760.696 42 2043 508.72 Production 4532730 133367 12885126 3267758.7 Pollutant 754238 2622.33 2936547 664628 Note: The abbreviation "SD" refers to the Standard deviation Table 1 contains descriptive data for four main factors that we employed in our investigation. Table 1 reports the mean, minimum, maximum, and standard deviation for each of the using data that are human resources, nameplate capacity, production, and pollutants. These statistics give essential insights into the core inclination and range of values for each variable. Iran is a nation with a variable climate. As we all know, climate change has an influence on gas and electricity usage, as well as total energy use. For example, during the summer season, the average air temperature in northern Iran is approximately 30°C, in the plateau region about 36°C, and in the southern part it is calculated at an average of 41°C. As seen in the table above, we are confronting a rather big standard deviation, which signifies the extent to which power plants are spread in different locations. As the air temperature rises, energy consumption and gas emissions also increase, and as a result, the nominal capacity and energy output increase too, which demands longer hours of labor, human power, and production capacity. In the same manner, with more production, more primary resources such as natural gas, oil, fossil fuels, gasoline, diesel, etc. will be consumed, and environmental pollution will also grow dramatically. According to the statistics presented in the table above, the average nameplate capacity and production are 760.696 and 4532730 megawatts, respectively. It seems that the large numbers should be beneficial but considering the average human resources and pollution, which represent 45 persons in order and 754238, the greater numbers are costly for us. The more we produce, the greater the expense of human resources and the lack of quality of product, which are crucial indications, which is opposed to the aims of power plants and the country. Table 2 reports the technical efficiency level of 46 power plants as follows: Table 2 Technical efficiency & pollution of power plants Power plant Efficiency Pollution P1 0.1081 406425.19 P2 0.16 41317.44 P3 0.4181 480115.10 P4 0.706 1507405.31 P5 0.3989 1160838.89 P6 0.743 7910498.10 P7 0.4264 1544761.92 P8 0.7521 125602.96 P9 0.6104 418525.03 P10 0.7613 4120571.27 P11 0.6195 596742.05 P12 0.7124 3032533.63 P13* 0.7817 10072302.99 P14 0.7412 1506224.39 P15 0.7341 6697595.12 P16 0.7527 230496.31 P17 0.5947 2932959.30 P18 0.4626 61695.57 P19 0.5881 1853051.35 P20 0.7632 4394505.60 P21* 0 0.00 P22 0.323 1699626.00 P23 0.4417 1661051.72 P24 0.5206 1322972.15 P25* 0 0.00 P26 0.1195 303786.45 P27 0.7786 6150256.39 P28 0.7371 8413888.88 P29* 0 0.00 P30 0.6702 3675459.23 P31 0.1063 20053.71 P32* 0 0.00 P33 0.4498 966450.18 P34 0.2778 1622700.36 P35 0.5098 3798780.82 P36 0.5796 3180528.92 P37 0.6759 3306190.53 P38* 0 0.00 P39 0.6985 4230349.30 P40 0.6337 4172605.25 P41* 0 0.00 P42 0.7332 6196607.54 P43 0.3566 195600.09 P44 0.6454 2213931.76 P45 0.2801 333426.56 P46 0.5416 3497777.91 The second column of Table 2 reports the technical efficiency of power plants and the third column reports the possible pollution reduction of each power plant considering only technical efficiencies. We observe that those power plants with zero efficiency levels that means they are efficient; we have no possibility of pollution reduction. This means these power plants are performing well, and there is no room for improvement in terms of pollution reduction. Please note that this does not mean that these power plants have no pollution, and remember that we estimate the relative performance of power plants compared with each other. From the table above, we can extract vital information regarding maximum and minimum efficiency (specified with *). Using an in-depth review, it can be noticed that simply one of the power plants, P13, has the maximum inefficiency, which stands at 10072302.99. In addition, it is evident that if there is a maximum share of inefficiency, there is also a minimum value. It can be said that the efficient power plants are 21, 25, 29, 32, 38, and 41, which stand by zero. Also, the average efficiency within 46 power plants stands at 2305569.81. Overall, there is the possibility of improving the consumption of 35 percent of the inputs that have been wasted, and this leads to not using all resources and capacity with stability or decreasing costs that we continue to optimize and efficient outputs. Please see Fig. 1 , which illustrates the possible production enlargement of electricity for all power plants. Observe P13, which has the highest possible production enlargement of electricity, and then P28 and P6. These power plants could have improved their performance and consequently provided more levels of electricity for the system. In the second analysis, another output was considered a pollutant, giving the model an environmental performance assessment. If pollution is inserted, lots of changes will be visible; hence, as a consequence, modifying all outcomes such as human resources, nameplate capacity, production, pollutants, returns to scale, and efficiency can be noticed. In the previous analysis, we assess only the technical performance of power plants. In the next run, the environmental performance of those power plants is assessed and Table 3 reports the results. Table 3 Environmental efficiency of power plants Power plant Efficiency Pollution P1 0.5683 461538.5937 P2 0.5984 75590.4864 P3 0.9680 418754.8640 P4 0.9907 768087.7286 P5 0.7718 416667.0352 P6 0.1723 505967.0481 P7 0.7314 597219.5502 P8 0.9975 1049831.8425 P9 0.8695 135845.4630 P10* 0.0000 0.0000 P11 0.9918 349218.7308 P12 0.7790 619669.5720 P13* 0.0000 0.0000 P14 0.9900 564148.5300 P15 0.2072 356864.9112 P16 0.9955 584093.6970 P17 0.6632 751104.5072 P18 0.7233 78329.0502 P19 0.8092 385611.3128 P20 0.4270 286013.5670 P21* 0.0000 0.0000 P22 0.6144 998632.2432 P23 0.7773 774608.2101 P24 0.8981 591319.8172 P25* 0.0000 0.0000 P26* 0.0000 0.0000 P27 0.3033 465644.6613 P28* 0.0000 0.0000 P29* 0.0000 0.0000 P30* 0.6287 1084946.9613 P31* 0.0000 0.0000 P32* 0.0000 0.0000 P33* 0.0000 0.0000 P34* 0.0000 0.0000 P35* 0.0000 0.0000 P36 0.4703 316781.8522 P37 0.6495 648834.9120 P38* 0.0000 0.0000 P39 0.3074 126747.4754 P40 0.1912 62219.3480 P41* 0.0000 0.0000 P42 0.1171 115391.7452 P43 0.5695 70950.5880 P44 0.5605 74283.6255 P45* 0.0000 0.0000 P46 0.5147 1057913.3506 Taking pollutants into consideration as an environmental issue, we observe more potential improvement possibilities for all power plants, reported in the third column of Table 3 . This column shows the potential reduction of pollutants in power plants, and of course, higher values show the lower environmental performance of a power plant. It is clear that adding pollutants as the second output will cause the efficiency to be worse. It is noticed in these combinational models that those power plants with a zero optimal value are efficient, and the rest are inefficient. In the tables above, power plant 30 is maximum and closer to inefficient, whereas power plants 10, 13, 21, 25, 26, 28,29, 31, 32, 33,34,35, 38, 41, and 45 stand low, indicating efficiency. To compare these two tables, it can be observed that P13 from the first model (Table 2 ) is closer to inefficiency than the second model (Table 3 ), or P26 is the efficient power plant in the second model. Still, it is not in the first one, which signifies a direr outcome than before. Figure 2 illustrates the possible pollution reduction of power plants, and we see P8, P30, and P45 stand in the first places for possible pollution reduction among all power plants. While the worst technical performance shows 78 percent of wasted input, we see 99 percent of wasted resources in terms of environmental performance. The former is associated with P13 the latter is related to P8. Although P8 has a relatively acceptable technical performance but it shows a very bad environmental presentation. Thus, it is a managerial trade-off to keep this power plant operating or close it down due to environmental issues. When we search for a general average picture of all power plants, we observe 53 percent of wasting resources on average for all power plants, considering technical performance, and 57 percent of wasting resources on average, considering environmental performance. This is an environmental alarm for policymakers to pay more attention to the performance of power plants, especially their environmental performance. We observed that pollution may affect the technical and environmental performance of power plants. The difference is that in the technical performance assessment, the pollution is considered a normal output that can be increased in the improvement path while in the environmental performance assessment, there is the possibility of reducing pollution too. The above findings and discussion can be helpful for policymakers in different aspects. In the first place, this gives a comprehensive picture of the performance of Iranian power plants not only from a technical view but also in an environmental sense. The first step of any improvement planning is measuring the system's current performance. The improvement planning can be performed in two different lines. One is a technical improvement, and the other is an environmental improvement. It is essential to note that technical improvement does not necessarily imply environmental improvement. Secondly, the current performance gives the appropriate direction for reducing production waste from a technical point of view and the reduction path for undesirable outputs like pollution. The primary important sources of inefficiencies may be the age of the operating systems and technologies in some power plants. Thus, a renovation or overhaul is necessary for those Iranian power plants that are very old. 4. Conclusion and policy implications Any misspecifications or non-optimal allocation of resources may yield low performance for production units. This issue becomes more vital when the production system involves pollution and environmental issues. This study evaluates the technical and environmental performance of power plants in Iran, offering critical insights into the country’s electricity production. According to the report, there are substantial chances to improve the technical and environmental elements of Iranian power plants, even if they operate well overall. This study emphasizes the necessity of all-encompassing approaches to increase the sustainability and efficiency of power plants for Iranian and other developing country governments. The primary improvement strategy for the power plant in the study is the modernization of Infrastructure. Investment from both public and private sectors in the modernization of power plant technologies aims to improve operational efficiency and minimize environmental effects. The government should encourage private sector investment in power plant projects by enhancing the return on investment and minimizing associated risks. An important policy-making suggestion is the transition to renewable energy. To diversify energy production and lessen reliance on fossil fuels, support renewable energy sources like hydropower, wind, and solar. Another policy-making suggestion is an implementation of Environmental Regulations by encouraging environmental regulations to reduce power plant emissions and contaminants. Thinking at the micro level, namely, internal operation of power plants, capacity building, and training, is also essential. This could be done by developing technical expertise and training programs to optimize plant operations and integrate sustainable practices. From a global point of view, regional cooperation can benefit international energy and environmental issues, which can be done by working together with other developing and developed countries to exchange technologies and best practices for sustainably producing energy. To create a more sustainable and effective energy sector, future studies could examine the viability of incorporating renewable energy sources and the long-term advantages of lowering dependency on gas, oil, and nuclear power. Declarations Funding: The research was funded by the Grant-in-Aid for Young Scientists (No. 22K13432) of the Japan Society for the Promotion of Science (JSPS). The authors are grateful to Tokai University for supporting the article processing charge, which enabled open access. Disclosure statement : The authors reported no potential conflict of interest. Inclusion and Diversity : While citing references scientifically relevant to this work, we also actively worked to promote gender balance in our reference list. The author list of this paper includes contributors from the location where the research was conducted who participated in the data collection, design, analysis, and interpretation of the work. Clinical trial number : not applicable. Consent to Participate and Consent to Publish declarations : not applicable. Ethics Declaration : Not applicable. Author Contribution Mojtaba Ghiyasi designed the research framework, conducted the primary analysis, and drafted the manuscript.Farhad Taghizadeh-Hesary performed validation, secured funding, and contributed to the review and editing of the manuscript.Nastaran Nazemianpour was responsible for data curation, data analysis, software, and manuscript writing.All authors have read and approved the final manuscript. Data Availability The data is available upon request. References Abbott M. The productivity and efficiency of the Australian electricity supply industry. Energy Econ. 2006;28(4):444–54. Arcos-Vargas A, Núñez-Hernández F, Villa-Caro G. A DEA analysis of electricity distribution in Spain: An industrial policy recommendation. Energy Policy. 2017;102:583–92. Azadeh A, Haghighi SM, Zarrin M, Khaefi S. Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis. Int J Electr Power Energy Syst. 2015;73:919–31. Çelen A. Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy. 2013;63:300–10. Ederer N. Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach. Renew Sustainable Energy Reviews. 2015;42:1034–46. Eguchi S, Takayabu H, Lin C. Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach. Renew Sustainable Energy Reviews. 2021;138:110562. Emami SM, Ravanshadnia M, Rahimi M. Analysis and Modeling of Energy Demand System in Iran’s Buildings and the Industry. J Appl Eng Sci. 2019;9(1):53–62. Henriques CO, Gouveia CM, Tenente M, da Silva PP. Employing Value-Based DEA in the eco-efficiency assessment of the electricity sector. Econ Anal Policy. 2022;73:826–44. Hoseini MH. (2012). Measuring productivity change in electricity generation management companies using DEA model and malmquist index %J. J Industrial Manage Perspective 2(2), 129–50. IBP I. Iran Country Study Guide Volume 1 Strategic Information and Developments. USA: Int'l Business, USA, Lulu Press; 2013. Junsittiwate R, Srinophakun TR, Sukpancharoen S. Techno-economic, environmental, and heat integration of palm empty fruit bunch upgrading for power generation. Energy Sustain Dev. 2022;66:140–50. Loiseau E, Junqua G, Roux P, Bellon-Maurel V. Environmental assessment of a territory: An overview of existing tools and methods. J Environ Manage. 2012;112:213–25. Mardani A, Zavadskas EK, Streimikiene D, Jusoh A, Khoshnoudi M. A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew sustainable energy reviews. 2017;70:1298–322. Mirzaei M, Bekri M. Energy consumption and CO2 emissions in Iran, 2025. Environ Res. 2017;154:345–51. Nakamoto Y, Eguchi S. How do seasonal and technical factors affect generation efficiency of photovoltaic power plants? Renew Sustainable Energy Reviews. 2024;199:114441. Omrani H, Beiragh RG, Kaleibari SS. Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. Int J Electr Power Energy Syst. 2015;64:617–25. Pourkazemi M, Heydari K. Data envelopment analysis and its application in evaluating the efficiency of power plants in iran. Modarres Hum Sci. 2002;6(1):35–54. Roshan G, Arab M, Klimenko V. Modeling the impact of climate change on energy consumption and carbon dioxide emissions of buildings in Iran. J Environ Health Sci Eng. 2019;17:889–906. Sadjadi SJ, Omrani H. Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy. 2008;36(11):4247–54. Sağlam Ü. A two-stage data envelopment analysis model for efficiency assessments of 39 state’s wind power in the United States. Energy Convers Manage Sci. 2017;146:52–67. Simab M, Haghifam MR. Quality performance based regulation through designing reward and penalty scheme for electric distribution companies. Int J Electr Power Energy Syst. 2012;43(1):539–45. Sueyoshi T, Goto M. Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: comparison between Japanese electric power industry and manufacturing industries. Energy Econ. 2012;34(3):686–99. Sukpancharoen S, Prasartkaew B. (2021). Combined heat and power plant using a multi-objective Henry gas solubility optimization algorithm: A thermodynamic investigation of energy, exergy, and economic (3E) analysis. Heliyon, 7 (9). Tavassoli M, Faramarzi GR, Saen RF. Ranking electricity distribution units using slacks-based measure, strong complementary slackness condition, and discriminant analysis. Int J Electr Power Energy Syst. 2015;64:1214–20. Tavassoli M, Ketabi S, Ghandehari M. Developing a network DEA model for sustainability analysis of Iran’s electricity distribution network. Int J Electr Power Energy Syst. 2020;122:106187. Tavassoli M, Ketabi S, Ghandehari M. (2022). A novel fuzzy network DEA model to evaluate efficiency of Iran’s electricity distribution network with sustainability considerations. Sustainable Energy Technologies and Assessments, 52, Part C , 102269. Tenente M, Henriques C, C., da Silva PP. Eco-efficiency assessment of the electricity sector: Evidence from 28 European Union countries. Econ Anal Policy. 2020;66:293–314. Vaninsky A. Efficiency of electric power generation in the United States: analysis and forecast based on data envelopment analysis. Energy Econ. 2006;28(3):326–38. Wang K, Wei Y-M, Huang Z. Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach. Eur J Oper Res. 2018;269(1):35–50. Xu T, You J, Li H, Shao L. Energy efficiency evaluation based on data envelopment analysis: A literature review. Energies. 2020;13(14):3548. Zhang C, Wang Z. Comprehensive energy efficiency analysis of ultra-supercritical thermal power units. Appl Therm Eng. 2023;235:121365. Zhang N, Zhao Y, Wang N. Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets. Energy Econ. 2022;112:106143. Zhang R, Wei Q, Li A, Ren L. Measuring efficiency and technology inequality of China's electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models. Energy. 2022;246:123274. Zurano-Cervelló P, Pozo C, Mateo-Sanz JM, Jiménez L, Guillén-Gosálbez G. Sustainability efficiency assessment of the electricity mix of the 28 EU member countries combining data envelopment analysis and optimized projections. Energy Policy. 2019;134:110921. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5849821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444435578,"identity":"735830ff-2c90-4c3e-9ed6-fdecd35599ab","order_by":0,"name":"Mojtaba Ghiyasi","email":"","orcid":"","institution":"Shahrood University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mojtaba","middleName":"","lastName":"Ghiyasi","suffix":""},{"id":444435580,"identity":"82c704a0-f2bc-45ea-86bd-56d203c53a7a","order_by":1,"name":"Farhad Taghizadeh-Hesary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBAC9gYGNgYGNhu4gAEDM5hmw6mF5wBYS5oEhJtAvJbDSFoIAR72s88e/Cg7XyfffoBNgvGHjTF/OwPjhx8MfHk4tfCkmxv2nLstYXAmgU2CISHNTOIwA7NkDwNbMS4t9gxpbBK8bUAtEgxs0n8SDtswHGZgkAa6NbEBly38z9gk/7adk5CfwQCy5bCNPNCW33i1SKSxSfO2HZBguAHRYmZwGGgdfi3P2KRlziVLbjiT2GzBkJZmbHiYsc2yxwC3X3j409gk35TZ8cu3Hz54g8HGxnDe+cOHb/yoOIYzxJAAYwMSw+BYAhFaUEEN6VpGwSgYBaNguAIAuBFGjUIb3s0AAAAASUVORK5CYII=","orcid":"","institution":"Tokai Research Institute for Environment and Sustainability (TRIES), Tokai University","correspondingAuthor":true,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Taghizadeh-Hesary","suffix":""},{"id":444435582,"identity":"e0fa0216-6110-4508-8ac8-f5f5d413e34b","order_by":2,"name":"Nastaran Nazemianpour","email":"","orcid":"","institution":"Shahrood University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nastaran","middleName":"","lastName":"Nazemianpour","suffix":""}],"badges":[],"createdAt":"2025-01-17 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5849821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5849821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80898133,"identity":"1f0e35cb-9f78-4528-b072-49a24e678ae2","added_by":"auto","created_at":"2025-04-18 12:39:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6227,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart of efficiency related to Table 2.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5849821/v1/9a0a1aede0cbe4b91f6fdfab.png"},{"id":80898129,"identity":"71ae481c-3e75-436b-ae2e-52ff2530fdba","added_by":"auto","created_at":"2025-04-18 12:39:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6585,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart of Possible production enlargement related to Table 2.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5849821/v1/770faf15c4f2531ea2ac2152.png"},{"id":80897760,"identity":"b28a4e74-5195-45ee-aad9-c3e772d97414","added_by":"auto","created_at":"2025-04-18 12:31:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5477,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart of technical efficiency related to Table 3.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5849821/v1/2f9102e829fba25698a3fd9b.png"},{"id":80898681,"identity":"20fb402d-2d53-419d-93c6-edc68c39a079","added_by":"auto","created_at":"2025-04-18 12:47:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6014,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart of Possible production enlargement related to Table 3.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5849821/v1/4429457c746b346e9a2551ef.png"},{"id":90005221,"identity":"427e032a-ab6b-46cc-a98a-eb7db910e6a0","added_by":"auto","created_at":"2025-08-27 09:25:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":869304,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5849821/v1/6637923f-692a-45d7-8290-f8f4adb3be9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Environmental and Technical Efficiency of Power Plants: Insights for Policy and Sustainability","fulltext":[{"header":"1. Introduction","content":"\u003ch2\u003e1. 1 Background\u003c/h2\u003e\u003cp\u003eThe electricity industry is one of the most essential factors in developing industries, along with other \u0026lrm;manufacturing and transformation industries. It has a significant strategic \u0026lrm;role in promoting a country's economic and industrial goals. Since these resources are limited, efficient use is inevitable (Emami, Ravanshadnia, \u0026amp; Rahimi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, in the country's development programs, the governments try to free the price of energy carriers and remove the related subsidies on their agenda, which increases the cost of products and services. The energy industry is the foundation of any country's infrastructure, propelling economic prosperity and social well-being. In Iran, a nation affluent in energy resources, the allocation and utilization of power plant assets have significant strategic weight. Nowadays, energy supply is one of the most crucial concerns for emerging nations. Meanwhile, power facilities play a significant role in generating and distributing electrical energy. Examining the density of resources in power plants may help better understand these energy-producing units' efficiency and productivity. Energy production and supply are vital issues for all societies from different points of view. Nowadays, any interruption in this sector is like a nightmare and may have a non-compostable effect on other parts of human life. Energy production, specifically electricity production, usually involves producing some emissions as a by-product that affects human life from another angle. Therefore, having green electricity, namely, electricity with no emissions, is also vital and essential. Environmental issues are becoming a more significant concern regarding expanding energy use in different sectors.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Problem\u003c/h2\u003e \u003cp\u003eThe growing demand for electricity can be attributed to the economic structure, climate change, and technological structure. One of these key strategies and practices is identifying and measuring the efficiency of the environmental performance of electricity production firms (Ederer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). One of the most critical issues and concerns is achieving maximum energy production from a particular energy source. The demand for electricity has been growing, and different factors might be affecting this. On the other side, we are constrained by limited resources. Thus, efficiency plays an important role not only in the production process but also in the process of electricity distribution. The performance of distribution firms is critical in the sense that their performance is a link between electricity suppliers, which are different types of power plants, and electricity consumers, which could be said to be the whole society, including households, firms, industries, economic sectors, etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Research gap\u003c/h2\u003e \u003cp\u003eBased on the latest search of authors, there is no study to analyze Iranian power plants' technical and environmental aspects using advanced frontier approaches. Thus, the current article analyzes the necessity of investigating the complex resource density of Iran's power plants using a potent and critical analytical framework for conscious energy policy-making and strategic planning.\u003c/p\u003e \u003cp\u003eThis approach enables us to evaluate the efficiency of power facilities and analyze their resource density. Previous research on measuring the efficiency of power facilities in Iran has been chiefly done using standard data coverage analysis techniques. Still, thus far, no study has been done to assess the resource density of these units at the same time. Meanwhile, the research on resource density may give significant information to decision-makers and energy administrators to implement suitable solutions to increase the efficiency and productivity of power facilities. The density approach in data envelopment analysis (DEA) will be used in the current study to examine the amount of resource density in Iran's power facilities. Using the DEA approach, a mathematical technique that examines the relative efficiency of decision-making units (DMU) by measuring their resource density, it conducts a technical and environmental analysis for 46 power plants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Literature review\u003c/h2\u003e \u003cp\u003eUsing the DEA approach, studies examined the country's electricity production at thermal power plants. The results show that the average efficiency of thermal power plants in the country under the premise of variable efficiency is 78 percent; in other words, with the same existing scenario and optimal utilization of the facilities, in the first instance, it is possible to produce 36 percent, and in the second case, 23 percent. Promote electricity in the country (Pourkazemi \u0026amp; Heydari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). (Abbott, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) examined productivity and efficiency in the Australian electricity industry. The article has analyzed the changes that have occurred in the last 30 years in the Australian electricity industry using the data coverage analysis approach and the Malmquist index during the years 1969\u0026ndash;1999. The results show that a significant improvement in the industry started in the mid-1980s; this improvement was more at the beginning of 1990 and until the end of 1999. Meanwhile, in the same direction, (Vaninsky, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) estimated the efficiency of electricity generation in the United States for 1991\u0026ndash;2004 using data envelopment analysis. The results show that the relative stability in productivity from 1994\u0026ndash;2004 at the levels of 0.99\u0026ndash;100 is associated with a sharp decrease to 0.95 in the following years. This research predicts that the efficiency for 2010 is equal to 0.80\u0026ndash;0.98, which means that the efficiency will decrease more than in the early years. Sadjadi \u0026amp; Omrani (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) presented a DEA model with uncertain data for the performance assessment of electricity distribution companies. DEA has been widely used to benchmark electricity distribution companies for the past two decades. However, there is no study among many existing DEA approaches where the uncertainty in data is allowed and, at the same time, the distribution of the random data is permitted to be unknown. Further inquiries in an article on measuring productivity changes in electricity production management companies using data coverage analysis and the Malmquist index to evaluate the changes in the total productivity factor in electricity management companies during 2002\u0026ndash;2014 have been paid. The evaluation results indicate that it has grown at an average rate of 1.023 during the years in question (Hoseini, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). On the other hand, (Wang, Wei, \u0026amp; Huang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) investigated the environmental efficiency and minimization efficiency measurement of China's thermal power industry. It has used a DEA-based approach to evaluate environmental efficiency and reduce costs in China's thermal power industry. It is presented based on the results of new efficiency scales that help identify the potential of reducing the pollution of power plants. A cross-efficiency network DEA model for efficiency measurement of China's electricity generation-transmission system was proposed by Zhang, Wei, Li, \u0026amp; Ren (. It is possible to use the DEA approach to find parameters of reward and penalty schemes for electricity companies in Iran. (Simab \u0026amp; Haghifam, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To diminish our environmental effect, methods for the environmental assessment of human activities are truly needed. However, different kinds of tools and methods are accessible, such as human and environmental risk assessment, the ecological footprint, material flow analysis, substance flow analysis, the physical input-output table, ecological network analysis, and energy or life cycle assessment (Loiseau, Junqua, Roux, \u0026amp; Bellon-Maurel, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). (\u0026Ccedil;elen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)presented a two-stage (DEA \u0026amp; Tobit) analysis to take into account the business environment variables that are beyond the control of distribution companies. In the first step, with the help of DEA, he determined the efficiency status of electricity distribution companies. Then, in the second step, using these performance scores calculated as dependent variables, he used the Tobit model to determine the business environment variables that may explain the performance scores.\u003c/p\u003e \u003cp\u003eThe performance of Iranian electricity distribution units was analyzed using a stochastic DEA approach (Azadeh, Haghighi, Zarrin, \u0026amp; Khaefi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Also, a corporate game DEA approach and principal component analysis were integrated for performance assessment of Iranian electricity distribution companies (Omrani, Beiragh, \u0026amp; Kaleibari, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In a study, which is based on three stations in Bushehr, Bandar Abbas, and Chabahar, Roshan, Arab, \u0026amp; Klimenko, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) aimed to quantify the model climate change effects of the upcoming decades on the energy demand and carbon dioxide emissions of a dominating building brigade under hot and humid climates on the southern coast of Iran. Mirzaei \u0026amp; Bekri (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) created a dynamic system model to simulate Iran's energy consumption and CO2 emission patterns from 2000 to 2025. Analysing the effects of various energy consumption factors on environmental quality considers energy policy factors. In another study by Tavassoli, Faramarzi, \u0026amp; Saen (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the slack-based DEA method was used to rank Iran's electricity distribution units. Two stages of the DEA-Tobit regression model for efficiency analysis of 39 wind power plants were utilized in the US. They found that more than half of the power plants are working efficiently, and newly installed power plants are more expensive and less productive than the currently installed power plants (Sağlam, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Zurano-Cervell\u0026oacute;, Pozo, Mateo-Sanz, Jim\u0026eacute;nez, \u0026amp; Guill\u0026eacute;n-Gos\u0026aacute;lbez (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analysed a representative sample of 102 smaller distributors in Spain using DEA methodology. Tenente, Carla Henriques, \u0026amp; da Silva (2020) utilized the DEA models for eco-efficiency evaluation of the electricity sector's consumption and production supply chains. In order to perform the multi-objective optimization of an energy, exergy, and economic analysis, Sukpancharoen \u0026amp; Prasartkaew (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied combined heat and power plant systems. To meet the increasing demands for energy, CHPPs must operate under optimal conditions, which necessitates plant design and equipment improvements. To achieve this aim, they combined the DEA and input-output analysis and the eco-efficiency of the production and supply chains of the Electricity Sector measured in the European Union (Henriques, Gouveia, Tenente, \u0026amp; da Silva, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Junsittiwate, Srinophakun, \u0026amp; Sukpancharoen (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) concurrently simulated a bio-oil upgrading facility, a complete pyrolysis process model, and electricity production. Compared to the non-heat exchanger network design, implementing a heat exchanger network enhances the maximum heat recovery and the minimum capital cost implications. A network DEA approach for assessing the sustainability of the Iranian electricity distribution network was developed by Tavassoli, Ketabi, \u0026amp; Ghandehari (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They found the best and worst performances to be 0.85 and 0.47, respectively. The Iranian electricity distribution network's sustainability was studied using a fuzzy DEA model (Tavassoli, Ketabi, \u0026amp; Ghandehari, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, some of the publications have been analyzed in diverse domains of the DEA method in distinct countries such as the US, China, Iran, etc. According to our issue, the multiple DEA method has different aspects presented in the second stage. Then, the study continued with the environmental assessment method to optimize with numerical outcomes. Based on the authors' best knowledge, there is no investigation into the technical and environmental performance of Iranian power plants using non-parametric methods. Thus, the article analyzes using a widely used method, DEA. The rest of the paper is organized as follows. Section 1 provides the introduction, including the literature review. Section 2 describes the research method used in the analysis, and Section 3 performs the technical and environmental efficiency analysis for Iranian power plants. The last section concludes the study and provides the policy implications.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Research Methodology","content":"\u003cp\u003eThe current article aims to study the performance of Iranian power plants not only in terms of technical efficiency but also from an environmental view, using advanced DEA approaches already in the literature. Analyzing technological and environmental issues is a critical prerequisite for reaching sustainable economic success in today's society. With increased globalization and the ever-growing demand for resources, companies and organizations must discover methods to function effectively while limiting their environmental impact. A renowned method for monitoring performance improvement presents a significant answer to this difficulty.\u003c/p\u003e \u003cp\u003eDEA operates by establishing an efficient frontier based on the observable input and output data of the DMUs being examined. This frontier represents the highest possible output levels that can be achieved given the inputs used by the best-performing DMUs. This method has been widely used in the input-output analysis of different sectors, including the energy sector. See, for instance, Mardani, Zavadskas, Streimikiene, Jusoh, \u0026amp; Khoshnoudi (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Xu, You, Li, \u0026amp; Shao (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for some recent literature review of application of DEA methods in energy economics and energy sectors including power plants. Specifically see Eguchi, Takayabu, \u0026amp; Lin, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), N. Zhang, Zhao, \u0026amp; Wang, (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), C. Zhang \u0026amp; Wang, (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Nakamoto \u0026amp; Eguchi, (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), for power plants efficiency analysis. By measuring each DMU's performance at this frontier, DEA may discover inefficiencies and gain insights into areas for improvement. This information may then be utilized to improve resource allocation, boost productivity, and minimize waste, eventually leading to more sustainable and eco-friendly operations.\u003c/p\u003e \u003cp\u003eIn this inquiry, we aim to evaluate the traditional DEA model in the second phase in the scenario of additions and unfavorable outputs of the density model. Finally, we will determine the resource density level of power plants in Iran using a suitable method and examine the results.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Performance assessment\u003c/h2\u003e \u003cp\u003eIn a production system, we usually desire the maximum impact and best performance by entering the lowest input and the largest output. As mentioned before, we have just input and output there, so the final model is presented as follows, which is the multiplier form of the DEA model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{min}{\\varvec{v}}^{T}{\\varvec{x}}_{0}-{\\varvec{u}}^{T}{\\varvec{y}}_{0}+\\sigma\\:s.t.\\left\\{\\begin{array}{c}{\\varvec{v}}^{T}{\\varvec{x}}_{j}-{\\varvec{u}}^{T}{\\varvec{y}}_{j}+\\sigma\\:\\ge\\:0\\\\\\:{\\varvec{v}}^{T}{\\varvec{x}}_{0}-{\\varvec{u}}^{T}{\\varvec{y}}_{0}=1\\\\\\:v\\ge\\:0\\:,u\\ge\\:0\\end{array}\\right\\}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, the input-output vector of j-th unit includes \u003cem\u003em\u003c/em\u003e inputs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{x}}_{j}\\in\\:{\\mathbb{R}}_{+}^{m}\\)\u003c/span\u003e\u003c/span\u003eand \u003cem\u003es\u003c/em\u003e favorable outputs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{y}}_{j}\\in\\:{\\mathbb{R}}_{+}^{s}\\)\u003c/span\u003e\u003c/span\u003e. The variables \"v\" and \"u\" are supplied as associated variables for input(s) and output(s) respectively; moreover, \"σ\" the parameter determining the returns to scale of the production unit. It is important to point out that model (1) is a joint-oriented assessment model meaning that it seeks for the lowest input level for producing the highest output level of DMU\u003csub\u003eo\u003c/sub\u003e, that is, the unit under evaluation. Please note the objective function which is a minimization objective, positive sign of the input vector and negative sign of the output vector. In this setting, a production unit with a zero optimal value is efficient and the rest of them are inefficient. Regarding the number of power plants (46), number of inputs (2), and number of favorable outputs (1) we have \u003cem\u003en\u003c/em\u003e=46, \u003cem\u003em\u003c/em\u003e=2 and \u003cem\u003es\u003c/em\u003e=2, in our case study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Environmental performance assessment\u003c/h2\u003e \u003cp\u003eThe examination of technical and environmental issues evaluates the optimum output obtained during the manufacturing process, alongside negative results like pollution and waste byproducts. This is significant because unfavorable outputs are characteristics that are predicted but often appear during the actual manufacturing process along with desirable outputs. This approach has found significant worldwide application in examining environmental assessment difficulties (Sueyoshi \u0026amp; Goto, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The multiplier form of DEA model for dealing with both favorable and unfavorable output can be found by the following linear programming, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{z}}_{j}\\in\\:{\\mathbb{R}}_{+}^{p}\\)\u003c/span\u003e\u003c/span\u003e is the vector of unfavorable output.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{min}{\\varvec{v}}^{T}{\\varvec{x}}_{0}-{\\varvec{u}}^{T}{\\varvec{y}}_{0}+{\\varvec{w}}^{T}{\\varvec{b}}_{0}+\\sigma\\:s.t.\\left\\{\\begin{array}{c}{\\varvec{v}}^{T}{\\varvec{x}}_{j}-{\\varvec{u}}^{T}{\\varvec{y}}_{j}+{\\varvec{w}}^{T}{\\varvec{b}}_{j}+\\sigma\\:\\ge\\:0\\\\\\:{\\varvec{u}}^{T}{\\varvec{y}}_{0}+{\\varvec{w}}^{T}{\\varvec{b}}_{0}=1\\\\\\:v\\ge\\:0\\:,u\\ge\\:0\\:,\\:w\\ge\\:0\\end{array}\\right\\}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCompared with model (1), we have an unfavorable output (p\u0026thinsp;=\u0026thinsp;1) and its associated variable in model (2). As we indicated above, specific manufacturing processes include unfavorable outputs that cannot be analyzed by the classical multiplier DEA model. The above model aims to find the minimum input level to produce the maximum output level for DMU\u003csub\u003eo\u003c/sub\u003e. Please observe the sign of the input vector and output vector in the model's objective function (2). Since pollution cannot be ignored but controlled, the sign of the variable associated with this factor is fee, compared with the sign variables of inputs and outputs, which are non-negative. Comparing the programming model (1) and model (2) that are for the technical and environmental assessment, respectively, we see that the former does not care about the environment issues of undesirable output and treats them just as a normal output that may not be rational, at least in an environmental view. On the other side, the later model, namely, model (2), is concerned with the environmental aspects. Despite model (1), which wishes more outputs, including undesirable ones, model (2) can reduce undesirable outputs, including pollution.\u003c/p\u003e \u003cp\u003eIt is important to point out that any study based on the methodology in the current section is based on quantities and data. This approach is useless in the absence of data and may not be able to handle qualitative data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Technical and environmental Performance assessment of Iranian power plant","content":"\u003cp\u003eIt has been estimated that roughly 18.5 percent of the electricity generated in Iran is squandered before it reaches consumers, owing to technological issues. Iran is among the top ten makers of gas turbines with a capacity of 160 megawatts (IBP, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is not only self-sufficient in power plant development but has also secured a number of contracts for constructing projects in adjacent states. The electricity sector is similarly extensively subsidized, and largely state-owned businesses manage power distribution, transmission, and generation. To meet the demands of the electrical sector, however, Iran is beginning to look into private investment. As of 2012, Iran had about 400 power plant units and 38 electrical distribution organizations that buy electricity from producers. Iran has approximately 100 companies that require more than 20MW of electricity annually. In 2021, Iran's energy generation was predominantly sourced from natural gas, accounting for 81% of total production. Oil supplied 14%, followed by hydropower at 4% and nuclear power at 1%. Both coal and non-hydro renewable sources each made up less than 1% of the energy generation mix. Mentioning a rise of 9,000 megawatts (MW) in the capacity of the country's nuclear power plants over the previous two years, officials claimed thermal power plants today account for 92 percent of Iran\u0026rsquo;s overall power production capacity. It is noticed that, according to the latest information on electricity production in 2022, Iran stands in 12th place with 353TWh in the world. In this study, we examine the efficiency of power plants with inputs such as oil and gas, with corresponding outputs like pollutant that contains CO\u003csub\u003e2\u003c/sub\u003e, heat, and production like electricity.\u003c/p\u003e \u003cp\u003eIn this study, we analyze the performance of 46 power plants in Iran based on the available input-output data at the time of the study. The data are extracted from the energy balance sheet of the Ministry of Energy of Iran. The main criteria of input and output selection are based on the production economic theory according to the rationality assumption in the economic theory. This means a rational decision maker desires more goods that are better and less goods that are worse. The former is input, and the latter is output in the DEA literature. Studying the literature review and considering available data, we yield two inputs (human resource and nameplate capacity) and two outputs (electricity generation and pollution). It is essential to point out that although pollution is an output of a power plant, it is not a desirable output, and we consider this in our analysis.\u003c/p\u003e \u003cp\u003eDEA method is a well-known data-based mathematical programming approach based on the production economics for the performance and frontier analysis. Pollution-generating technologies are also dealt with in this method, conspiring available input-output data. As a powerful approach, it has been widely used in many real-world applications in the energy and environmental sectors. We used this method for both technical efficiency and, furthermore, environmental analysis. We consider two inputs and two outputs as described above for the technical efficiency analysis and then deal with pollution for the environmental efficiency analysis of Iranian power plants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.76087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e19.87262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNameplate capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e760.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e508.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4532730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12885126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e3267758.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e754238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2622.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2936547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e664628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote: The abbreviation \"SD\" refers to the Standard deviation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable 1 contains descriptive data for four main factors that we employed in our investigation. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the mean, minimum, maximum, and standard deviation for each of the using data that are human resources, nameplate capacity, production, and pollutants. These statistics give essential insights into the core inclination and range of values for each variable.\u003c/p\u003e \u003cp\u003eIran is a nation with a variable climate. As we all know, climate change has an influence on gas and electricity usage, as well as total energy use. For example, during the summer season, the average air temperature in northern Iran is approximately 30\u0026deg;C, in the plateau region about 36\u0026deg;C, and in the southern part it is calculated at an average of 41\u0026deg;C. As seen in the table above, we are confronting a rather big standard deviation, which signifies the extent to which power plants are spread in different locations. As the air temperature rises, energy consumption and gas emissions also increase, and as a result, the nominal capacity and energy output increase too, which demands longer hours of labor, human power, and production capacity. In the same manner, with more production, more primary resources such as natural gas, oil, fossil fuels, gasoline, diesel, etc. will be consumed, and environmental pollution will also grow dramatically. According to the statistics presented in the table above, the average nameplate capacity and production are 760.696 and 4532730 megawatts, respectively. It seems that the large numbers should be beneficial but considering the average human resources and pollution, which represent 45 persons in order and 754238, the greater numbers are costly for us. The more we produce, the greater the expense of human resources and the lack of quality of product, which are crucial indications, which is opposed to the aims of power plants and the country.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the technical efficiency level of 46 power plants as follows:\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTechnical efficiency \u0026amp; pollution of power plants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePower plant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEfficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePollution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406425.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41317.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e480115.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1507405.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1160838.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7910498.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1544761.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125602.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418525.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4120571.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e596742.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3032533.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10072302.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1506224.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6697595.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230496.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2932959.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61695.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1853051.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4394505.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1699626.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1661051.72\u003c/p\u003e \u003c/td\u003e 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colname=\"c1\"\u003e \u003cp\u003eP27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6150256.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8413888.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP30\u003c/p\u003e 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\u003cp\u003e4230349.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4172605.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6196607.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195600.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2213931.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333426.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3497777.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe second column of Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the technical efficiency of power plants and the third column reports the possible pollution reduction of each power plant considering only technical efficiencies. We observe that those power plants with zero efficiency levels that means they are efficient; we have no possibility of pollution reduction. This means these power plants are performing well, and there is no room for improvement in terms of pollution reduction. Please note that this does not mean that these power plants have no pollution, and remember that we estimate the \u003cem\u003erelative\u003c/em\u003e performance of power plants compared with each other.\u003c/p\u003e \u003cp\u003eFrom the table above, we can extract vital information regarding maximum and minimum efficiency (specified with *). Using an in-depth review, it can be noticed that simply one of the power plants, P13, has the maximum inefficiency, which stands at 10072302.99. In addition, it is evident that if there is a maximum share of inefficiency, there is also a minimum value. It can be said that the efficient power plants are 21, 25, 29, 32, 38, and 41, which stand by zero. Also, the average efficiency within 46 power plants stands at 2305569.81. Overall, there is the possibility of improving the consumption of 35 percent of the inputs that have been wasted, and this leads to not using all resources and capacity with stability or decreasing costs that we continue to optimize and efficient outputs. Please see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which illustrates the possible production enlargement of electricity for all power plants. Observe P13, which has the highest possible production enlargement of electricity, and then P28 and P6. These power plants could have improved their performance and consequently provided more levels of electricity for the system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the second analysis, another output was considered a pollutant, giving the model an environmental performance assessment. If pollution is inserted, lots of changes will be visible; hence, as a consequence, modifying all outcomes such as human resources, nameplate capacity, production, pollutants, returns to scale, and efficiency can be noticed. In the previous analysis, we assess only the technical performance of power plants. In the next run, the environmental performance of those power plants is assessed and Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEnvironmental efficiency of power plants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePower plant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEfficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePollution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461538.5937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75590.4864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418754.8640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e768087.7286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416667.0352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e505967.0481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e597219.5502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1049831.8425\u003c/p\u003e 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\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e619669.5720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e564148.5300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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\u003cp\u003e465644.6613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP30*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1084946.9613\u003c/p\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP35*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316781.8522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62219.3480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115391.7452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70950.5880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74283.6255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057913.3506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTaking pollutants into consideration as an environmental issue, we observe more potential improvement possibilities for all power plants, reported in the third column of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This column shows the potential reduction of pollutants in power plants, and of course, higher values show the lower environmental performance of a power plant.\u003c/p\u003e \u003cp\u003eIt is clear that adding pollutants as the second output will cause the efficiency to be worse. It is noticed in these combinational models that those power plants with a zero optimal value are efficient, and the rest are inefficient. In the tables above, power plant 30 is maximum and closer to inefficient, whereas power plants 10, 13, 21, 25, 26, 28,29, 31, 32, 33,34,35, 38, 41, and 45 stand low, indicating efficiency. To compare these two tables, it can be observed that P13 from the first model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) is closer to inefficiency than the second model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), or P26 is the efficient power plant in the second model. Still, it is not in the first one, which signifies a direr outcome than before. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the possible pollution reduction of power plants, and we see P8, P30, and P45 stand in the first places for possible pollution reduction among all power plants.\u003c/p\u003e \u003cp\u003eWhile the worst technical performance shows 78 percent of wasted input, we see 99 percent of wasted resources in terms of environmental performance. The former is associated with P13 the latter is related to P8. Although P8 has a relatively acceptable technical performance but it shows a very bad environmental presentation. Thus, it is a managerial trade-off to keep this power plant operating or close it down due to environmental issues. When we search for a general average picture of all power plants, we observe 53 percent of wasting resources on average for all power plants, considering technical performance, and 57 percent of wasting resources on average, considering environmental performance. This is an environmental alarm for policymakers to pay more attention to the performance of power plants, especially their environmental performance. We observed that pollution may affect the technical and environmental performance of power plants. The difference is that in the technical performance assessment, the pollution is considered a normal output that can be increased in the improvement path while in the environmental performance assessment, there is the possibility of reducing pollution too.\u003c/p\u003e \u003cp\u003eThe above findings and discussion can be helpful for policymakers in different aspects. In the first place, this gives a comprehensive picture of the performance of Iranian power plants not only from a technical view but also in an environmental sense. The first step of any improvement planning is measuring the system's current performance. The improvement planning can be performed in two different lines. One is a technical improvement, and the other is an environmental improvement. It is essential to note that technical improvement does not necessarily imply environmental improvement. Secondly, the current performance gives the appropriate direction for reducing production waste from a technical point of view and the reduction path for undesirable outputs like pollution. The primary important sources of inefficiencies may be the age of the operating systems and technologies in some power plants. Thus, a renovation or overhaul is necessary for those Iranian power plants that are very old.\u003c/p\u003e"},{"header":"4. Conclusion and policy implications","content":"\u003cp\u003eAny misspecifications or non-optimal allocation of resources may yield low performance for production units. This issue becomes more vital when the production system involves pollution and environmental issues. This study evaluates the technical and environmental performance of power plants in Iran, offering critical insights into the country\u0026rsquo;s electricity production. According to the report, there are substantial chances to improve the technical and environmental elements of Iranian power plants, even if they operate well overall. This study emphasizes the necessity of all-encompassing approaches to increase the sustainability and efficiency of power plants for Iranian and other developing country governments. The primary improvement strategy for the power plant in the study is the modernization of Infrastructure. Investment from both public and private sectors in the modernization of power plant technologies aims to improve operational efficiency and minimize environmental effects. The government should encourage private sector investment in power plant projects by enhancing the return on investment and minimizing associated risks. An important policy-making suggestion is the transition to renewable energy. To diversify energy production and lessen reliance on fossil fuels, support renewable energy sources like hydropower, wind, and solar. Another policy-making suggestion is an implementation of Environmental Regulations by encouraging environmental regulations to reduce power plant emissions and contaminants. Thinking at the micro level, namely, internal operation of power plants, capacity building, and training, is also essential. This could be done by developing technical expertise and training programs to optimize plant operations and integrate sustainable practices. From a global point of view, regional cooperation can benefit international energy and environmental issues, which can be done by working together with other developing and developed countries to exchange technologies and best practices for sustainably producing energy. To create a more sustainable and effective energy sector, future studies could examine the viability of incorporating renewable energy sources and the long-term advantages of lowering dependency on gas, oil, and nuclear power.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The research was funded by the Grant-in-Aid for Young Scientists (No. 22K13432) of the Japan Society for the Promotion of Science (JSPS). The authors are grateful to Tokai University for supporting the article processing charge, which enabled open access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e: The authors reported no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Diversity\u003c/strong\u003e: While citing references scientifically relevant to this work, we also actively worked to promote gender balance in our reference list. The author list of this paper includes contributors from the location where the research was conducted who participated in the data collection, design, analysis, and interpretation of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate and Consent to Publish declarations\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMojtaba Ghiyasi designed the research framework, conducted the primary analysis, and drafted the manuscript.Farhad Taghizadeh-Hesary performed validation, secured funding, and contributed to the review and editing of the manuscript.Nastaran Nazemianpour was responsible for data curation, data analysis, software, and manuscript writing.All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbott M. 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Energy Policy. 2019;134:110921.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Power Plants, Energy Supply, Efficiency, Eco-efficiency, Environmental Assessment","lastPublishedDoi":"10.21203/rs.3.rs-5849821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5849821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eData envelopment analysis (DEA), a reliable technique for evaluating performance and spotting inefficiencies, is used in this study to examine the technical and environmental efficiency of power plants in Iran. Although many power plants exhibit impressive efficiency, the study emphasizes that there is still much space for improvement in technical operations and environmental effect reduction. In contrast, we find 15 environmentally efficient power plants and six power plants that are technically efficient. We further find 53 percent of wasting resources on average for all power plants, considering technical performance, and 57 percent of wasting resources on average, considering environmental performance. The findings emphasize the need for targeted interventions to optimize energy production processes. Policymakers are encouraged to leverage these insights to design strategies that enhance operational efficiency, reduce emissions, and promote sustainable energy production.\u003c/p\u003e","manuscriptTitle":"Assessing Environmental and Technical Efficiency of Power Plants: Insights for Policy and Sustainability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 12:30:57","doi":"10.21203/rs.3.rs-5849821/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b7bbf79-6833-4efa-af0f-5850b3b54087","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T09:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 12:30:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5849821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5849821","identity":"rs-5849821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}