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24 

 

 

 

Article 

CO2 emission from the electricity sector in Iran; 

calculation, prediction and reduction policies 
Hossein Yousefi, Shiva Ansaripour, Aminabbas Golshanfard, Mohammad Hasan Ghodusinejad* 

Energy Modelling and Sustainable Energy System (METSAP) Research Lab., Faculty of New Sciences and Technologies, 
University of Tehran, Tehran, Iran 

A R T I C L E   I N F O 
 

Article history: 
Received 01 July 2023  
Received in revised form 
02 August 2023 
Accepted 08 August 2023 
 
Keywords: 
CO2 emission, Renewable Energy, GPR,  
Fuel Consumption Forecast, Reformation Strategy         
 
*Corresponding author 
Email address:  
mh.ghodusi@ut.ac.ir 

 
DOI: 10.55670/fpll.fuen.3.2.3 

A B S T R A C T 
 

Fossil fuel power plants produce a significant amount of CO2 emissions, and this 
pollutant causes global warming, respiratory and heart diseases, and other 
significant issues. Electricity interprets as a primary and rising demand in each 
energy system; thus, in this paper, carbon dioxide (CO2) emission reduction was 
selected as the objective value for 2025. Power plant fuel consumption was 
surveyed to calculate the CO2 emission caused by each fuel. Also, Esfahan 
province (an industrial province in Iran) was investigated as the study case. 
Forecasting the fuel consumption for 2025 was run by two parameters: 
population and Gross Domestic Production (GDP), which were forecasted by 
the report of the Iran Statics Center and the Gaussian Process Regression (GPR) 
method, respectively. The CO2 emission of power plants was obtained using the  

coefficients of each fuel. Based on Iran's commitment to the Paris Agreement, a 
4% reduction of CO2 emissions is the main objective. Thus, this study aims to 
reach this goal by implementing four scenarios: a) adding renewable energies, 
b) adding renewable energies and improving the generation efficiency, c) 
adding renewable energies and decreasing the grid losses, and d) combining the 
three scenarios mentioned above. According to these scenarios, reformation 
strategies compensated 10.5% of the required power, which was satisfied by 
renewable energies, and finally, this province can gradually satisfy a 4% 
reduction until 2025. 

 
1. Introduction  

The Intergovernmental Panel on Climate Change (IPCC) 
report, which is related to an increase of 1.5 ˚C the earth's 
temperature after the industrial revolution, expresses the 
combustion impacts of fossil fuels in greenhouse gases 
emission [1, 2]. Today, climate-changing problems, global 
warming, rising numbers of respiratory and heart diseases, 
etc., have raised concerns in the countries. The Paris 
Agreement was signed in 2015 to balance the number of 
pollutants from human activities and ensure sustainable 
development in the second half of this century and limit the 
temperature increase up to 2˚C [3, 4]. Due to the increase in 
population, the limitations of energy sources, and the 
environmental effects of fossil fuels, societies are moving 
towards alternative energies [5]. The pollutants emission 
generally is derived from five sectors: transportation, power 
plant, industrial, commercial-residential, and agricultural. As 
shown in Figure 1, the power plant sector produces a 
significant amount of emissions. This sector contains three 
parts: generation, transmission, and distribution, which 
major contribution to pollution emissions such as CO2, 
Methane (CH4), Nitrogen Oxide (NOx), Sulfur Oxide (SOx), etc. 
are related to generation [6]. Several theories mentioned a 
relationship between environmental pollution and economic 

growth [5-7]. On the other hand, the quality of the 
environmental parameter is affected by renewable energy 
development. Thus, more than ever, societies are moving to 
use renewable energy resources to satisfy ecological 
indicators. Finally, a U-shaped relationship between 
renewable energy resources and economic growth, or in 
other words, GDP per capita, will be derived [8, 9]. As a result 
of the new technologies development and increasing the 
number of consumers, electricity demand has been rising. 
Lack of fossil fuel sources, fuel price, and combustion's 
harmful environmental effects are potential challenges 
during power generation. A solution to this problem is 
Distributed Energy Planning (DEP) [10]. According to the IEA 
report, five factors can affect DEPs, which are: Distributed 
Generation (DG) Technology, limitations on new 
transmission lines, increase in the electricity consumers with 
high reliability, privatization, competition in the electricity 
market, and climate change concerns [11]. IEA forecasted a 
reduction of 1.4 to 13 GT in the CO2 emission caused by power 
generation, which means about 90%, until the year 2050 [12]. 
Iran is one of the most important oil and natural gas exporting 
countries. Statistics show that more than 98% of Iran's energy 
consumption is provided through these sources, which has 
led to severe problems such as air pollution, and the effects of 

 

 

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H. Yousefi et al. /Future Energy                                                                                                         May 2024| Volume 03 | Issue 02| Pages 24-30 

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this pollution are more visible in metropolitan areas [13-14]. 
Along with this, statics showed a share of 1.97% in global CO2 
emission in the year 2018, which made this developing 
country the seventh CO2 producer country around the world 
[15]. Furthermore, Iran's Energy balance sheet (2016) 
reports that annual CO2 emissions released in the electricity 
sector are higher than any other pollutant [16]. 

 
Figure 1. The contribution of each energy sector in the 
pollutant emissions 

 
Most of the CO2 emissions in 2016 for Iran are related to 

natural gas, diesel, gasoline, Mazut, and coal, respectively . 
Figure 2 illustrates the importance of CO2 and the share of 
other pollutants, as well. As can be seen, following CO2, NOx 
has a larger share in air pollutants [17]. In order to achieve 
sustainable development goals and control pollutants, 
consumption management and utilization of renewable 
energy sources are required. The high potential of renewable 
sources, like solar and wind, can help apply sustainable 
programs and strategies [14]. Investment in renewable 
energy sources has been conducted so that solar power 
plants' installation had a growth rate equal to 50 between 
2007 and 2017 [12]. About 195 countries signed the Paris 
Agreement, including Iran, and each country made its 
commitments. Based on the Bill expressed in the Iranian 
parliament, this country would decrease at least 4 percent of 
its emission by 2030 [18]. Hence, this paper is focused on the 
4 percent reduction of CO2 emission. 

Figure 2. The contribution of each pollutant 
 

2. Literature review 

The amount of fuel consumption due to the fuel type is 
determined by achieving the number of pollutants caused by 
the power plants. According to references [19] and [20], the 
most influencing parameters on fuel consumption in power 
plants are GDP and population. Two main study areas focused 
on this research are demand forecasting and CO2 emission 

reduction strategies. A comprehensive study of 50 countries 
worldwide, including Iran, has investigated the impact of GDP, 
population growth, and renewables installation on air 
pollution [21]. It has concluded that the two first criteria have 
a positive effect on CO2 emission. According to [22], a novel 
self-adapting intelligent Grey model is a better approach than 
competing natural gas forecasting methods. The logistic 
model has been applied in [23] for long-term natural gas 
consumption forecasting in China. For getting the parameters 
of the logistic model Levenberg-Marquardt algorithm is 
adopted. In [24], the Autoregressive–Moving-Average model 
with exogenous inputs (ARMAX) model has been developed 
for residential and commercial energy demand forecasting in 
Iran. Alcaraz and Villalvazo [25] presented the natural gas 
estimation shortage with econometric analysis based on 
panel data and analyzed the natural gas interconnection 
shortage and GDP. In 2017, Scarpa and Bianco [26] 
investigated long-term natural gas consumption, considering 
heating degree days, natural gas prices, and GDP per capita. 
To obtain this goal, they used the regression algorithm and 
the Kalman filter method. The relationship between price and 
income with natural gas consumption has been shown by Liu 
et al. [27]. The generalized least square method was used in 
this study. Wang et al. [28] studied the natural gas 
consumption model with high accuracy by a MAPE value of 
2.32% with a hybrid model based on the Particle Swarm 
Optimization-Wavelet Neural Network (PSO-WNN). In [29], 
Artificial Neural Networks (ANN), Multiple Linear Regression 
(MLR), and Support Vector Regression (SVR) was presented 
for forecasting natural gas consumption in Istanbul with 
these criteria: seasonal index, temperature, price of natural 
gas, population and 12 years history of natural gas 
consumption. In [30,31], electricity consumption forecasting 
in a building is done by using the GPR method.  Sharifzadeh et 
al. [32] did a comparative study of ANN, SVR, and GPR for 
forecasting residential electricity demand. Further, the 
Gaussian process quantile regression is used in [33] to predict 
Power load probability density. In some cases, the GPR 
method is preferred in forecasting wind forecasting because 
it is flexible to provide uncertainty representations [34, 35].  

Researchers have proposed some methods to decrease 
CO2 emissions [36, 37]. Dominkovic et al. [38] studied 
southeast Europe to make a 100% renewable energy system 
for 2050 to achieve a zero-carbon energy community. For 
getting this object, biomass, and other renewable energies 
have been used. Also, improving energy efficiency is 
considered a reformation strategy for decreasing CO2. Daví-
Arderius et al. [39] expounded on the impact of electricity 
losses on CO2 decrement. Construction of DGs near the 
consumers and covering different generations with 
renewable energy are the two essential policies suggested in 
this research. Technical progress, energy structure, and 
economic level are the variables considered in [40] to analyze 
their impact on CO2 emission in China. Also, it is concluded 
that technological advances have about a 1% effect on China's 
CO2 emission. Also, 14 years (2000-2014) of research in China 
indicated that technological developments had a remarkable 
impact on CO2 emission [41].  The South Asian Association for 
Regional Cooperation (SAARC) countries has been weighted 
and ranked based on the CO2 emission issue by Grey 
Relational Analysis (GRA) [42]. The results have shown 
substantial pollution problems in India with the first rank; 
hence, renewable energy installation and adoption of 
ISO14001 certification were introduced to solve this problem. 
Toward reduction of carbon emission, reference [43] offered 
three assortments that contain adding a clean energy supply, 



H. Yousefi et al. /Future Energy                                                                                                         May 2024| Volume 03 | Issue 02| Pages 24-30 

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development in energy conservation, and negative emission 
strategies like using CCS technology. 

3. Methodology: GPR  

 GPR is a non-parametric probabilistic kernel method 
that can model arbitrary complex systems [32]. This method 
combines arbitrary variables with a number describing the 
joint Gaussian distribution [33]. GPR models a probability 
distribution by functions and can be parameterized with 
statistical functions like mean m(x), which is the expected 
value of f(x), and covariance к(x-x') that defines the similarity 
between data points. It can be shown as: 

𝑦 = 𝑓(𝑥) ∼ 𝐺𝑃(𝑚(𝑥), 𝜅(𝑥 − 𝑥 ′))                                                    (1) 

where x and y are the input and output in the training dataset, 
respectively, and f(x) is called the latent variable. For 
simplification, mostly m(x) is considered to be 0. 
A variety of covariance functions can be used. Some of the 
most common ones are squared exponential (SE) (Eq2), 
Matern (MA) (Eq3), and rational quadratic (RQ) (Eq4).  

𝜅𝑆𝐸(𝑥 − 𝑥 ′) = 𝜃𝑓
2𝑒𝑥𝑝(

‖𝑥−𝑥′‖
2

𝜃𝑙
2 )                      (2) 

𝜅𝑀𝑎(𝑥 − 𝑥 ′) = 𝜎2 21−𝑣

𝛤(𝑣)
(√2𝑣

𝑥−𝑥′

𝑙
)𝑣𝜅𝑣(√2𝑣

𝑥−𝑥′

𝑙
)     ,𝑣, 𝑙 >  0  (3) 

𝜅𝑅𝑄(𝑥 − 𝑥 ′) = 𝜎2(1 +
𝑥−𝑥′

2𝛼𝑙2
)−𝛼   ,𝛼, 𝑙 > 0                       (4) 

The SE function is infinitely differentiable, so the GPR is so 
smooth using it, and it is too strict for physical action [34]. θf 
and θl are parameters that control the length scale. 
Kv  in Matern covariance was modified Bessel function. The 
function becomes simple when v is half floating-point 
v=p+1/2, where p is an integer. V is mostly considered to be 
v=5/2 and v=3/2. 
Along with this, two effective methods were proposed to 
evaluate machine learning algorithms' performance; 
Holdout-test,  which mostly applies to large datasets, and 
cross-validation (CV), which is called K-fold validation. This 
approach divides the dataset into parts and estimates each 
fold's accuracy to prevent overfitting the output. Also, by 
increasing the number of folds, more reputable outputs can 
be derived. 

 
Figure 3. The electricity system of Isfahan province 

4. Fuel consumption forecasting 

The energy system of Isfahan province for 2016 is shown 
in Figure 3, illustrating its high dependency on fossil fuels. 
Furthermore, the importance of conversion and grid losses 
are apparent in this figure. As mentioned before, GDP and 
population are the essential parameters for estimating energy 
consumption. GDP was achieved using the GPR method, and 
the population was obtained by the combined method from 
the report of Iran's Statics Center [44]. In this research, the 
estimation of GDP is based on historical data from 2006 to 
2016. As a result, future data up to 2025 was achieved by 
applying the GPR algorithm to the current data.           

4.1 Population forecast 
Population forecasting is conducted by the combined 

method, which utilizes inner population structure (i.e., 
mortality, age-sex composition, and fertility pattern) and 
outer impacting factor population structure (i.e., 
immigration) to forecast population. This method is the most 
common approach in population modeling and forecasting 
[44]. Figure 4 illustrates the population curve for 19 years. 

4.2 GDP forecast 
Real data on provincial GDP from 2006 to 2016 is derived 

from the energy balance sheet [17]. The GPR method was 
used by applying a 10-fold validation method for the years 
2016 to 2025. The results obtained from the GPR are 
presented in Figure 5. 

4.3 Natural gas and diesel consumption forecast  
Natural gas and diesel fuel consumption are shown in 

Figure 6 and Figure 7, respectively. As shown in Table 1, most 
of Isfahan's power plants are non-renewable and consume 
four fuel types, including diesel, coal, natural gas, and Mazut. 
The consumption of Mazut has gradually been discarded in 
recent years due to its high combustion pollutants. Therefore, 
in the present study, its value for the future was considered to 
be zero. Besides, coal usage for power generation is 
decreasing with a high slope and is used under particular 
conditions [45]. Therefore, the coal consumption for future 
power plants is considered to be zero too. Natural gas and 
diesel consumption are forecasted by applying the GPR 
approach with the three mentioned covariance functions and 
calculating R-squared for each one.  

 
 



H. Yousefi et al. /Future Energy                                                                                                         May 2024| Volume 03 | Issue 02| Pages 24-30 

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The best-fitted covariance function is selected by comparing 
R-squared, Matern with R-squared equal to 0.96 and 0.88. 

 
Figure 4. The population of Isfahan province (blue: historical 
data; orange: forecast data) 
 

Figure 5. GDP of Isfahan province (blue: historical data; 
orange: forecast data) 

 
Figure 6. Natural gas consumption forecast of powerplants 
(blue: historical data; orange: forecast data) 

 

 
Figure 7. Diesel consumption forecast of powerplants (blue: 
historical data; orange: forecast data) 

Table 1. List of power plants in Isfahan province 

 
 

5. CO2 emission calculation 

 The difference in the heating value of the fuels and the 
efficiency of machines' burning fuels affect each fuel's 
emission per equal amount. In order to calculate the 
coefficient of CO2 for the electricity sector caused by natural 
gas, the total volume of CO2 produced by the natural gas 
electricity sector is divided into the total consumption of 
natural gas [46]. The accrued coefficient is 0.0022 tons per 
1000 liters. Using the same approach, the coal and diesel 
emission coefficient is calculated as 0.0011 and 2.905 tons per 
1000 liters, respectively. Finally, according to the data 
presented in the energy balance sheet and estimated values, 
the CO2 emission of three more essential fuels in the 
electricity generation sector has been calculated, shown in 
Figure 8. 

 
Figure 8.  CO2 emission trend 

 
6. Emission reduction scenarios 

As represented in previous sections, CO2 is becoming a 
critical problem in societies; therefore, CO2 emission was 
considered an objective function. This research aimed to 
Achieve a 4% reduction in CO2 by the implementation of four 
scenarios as follows: 
S1. Adding the photovoltaic 
S2. Adding the photovoltaic and reforming the generation 

sector 
S3. Adding the photovoltaic and reforming the grid 
S4. Adding the photovoltaic and reforming the generation 

sector and the grid 

Efficiency 
(%) 

Capacity 
(MW) 

Type Name 

26.4 249 steam Zob Ahan 

- 26 Gas Zob Ahan  

27..9 210 steam Fulad 

31 108 Gas Fulad 

38.3 1616 steam Shahid 
Montazeri 

37.1 835 steam Islam Abad 

28 87.6 Gas Hessa 

31.7 324 Gas Kashan 

32 954 Gas Chehel Sotun 

50.4 484 Combined-
cycle 

Zavareh 

39.7 126.7 Gas Distributed 
Generation 



H. Yousefi et al. /Future Energy                                                                                                         May 2024| Volume 03 | Issue 02| Pages 24-30 

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In the first step, total CO2 production caused by power plants 
until the year 2025 was calculated, then 4% of this amount 
was considered as the criteria of reduction for these 
scenarios  equals 860890 Tons. In the second step, fuel 
consumption for the power plants (natural gas & diesel) was 
calculated using the coefficients of CO2 emission. Finally, 
according to fuel consumption and total energy production, 
one coefficient is obtained using Eq5, which satisfies the 
reduction of CO2 emission. 

𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑒𝑛𝑡 =
𝑇𝐸𝑃(𝑡)

𝑃𝐸𝐶(𝑡)
                     (5) 

TEP(t) and PEC(t) represent the Total Energy Production and 
Primary Energy Consumption, respectively in tth year. For this 
amount of electrical energy, the following scenarios are 
explained: 
In scenario (1), renewable power plants would be replaced 
with fossil fuel to generate the calculated electrical power 
replacement. Based on solar radiation and ambient 
temperature, Isfahan has a high potential for PV energy 
generation [47]. According to the Iran Renewable Energy and 
Energy Efficiency Organization announcement, the estimated 
capacity of photovoltaic for Isfahan province is 3220 MW 
[48]. So, these potentials can cover the rest power with less 
CO2 emission. 
In scenario (2), an improvement in the efficiency of the fossil 
fuel plants was considered; thus, a ratio of needed power 
would be satisfied during generation. PV would be replaced 
with the rest of the required electricity. Enhancements in 
efficiency include converting the gas turbine powerplant to a 
combined cycle powerplant or improving the powerplant 
components and equipment types.  
Transmission and distribution losses play an essential role in 
grid optimization, and these can help the grid reach the 
optimum point. Therefore, in scenario (3), with activities like 
reforming the distribution grid, installation of the low-loss 
transformers, changing the defective counter, etc., the grid 
losses would reach a minimum amount, and this will cause a 
decrease in fuel consumption of powerplants or on the other 
point of view, with constant fuel consumption, the grid has 
extra power for feeding the demands. 
Eventually, in the last scenario, three scenarios were 
combined and expressed the amount of renewable power to 
improve efficiency and decrease the grid's losses. 

7. Results and discussion 

Referring to Figure 8, the total CO2 emission in 2025 will 
be 21,522,259 Tons for Isfahan province, and it is aimed to 
reduce 4% of CO2 emission, which means 860,890 Tons of 
reduction.  Based on Eq5, the generation coefficient in 2016 is 
2852.3 and 1.30 for natural gas and diesel, respectively.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 

This coefficient is variable for each year due to power plant 
efficiency and fuel alternation. Powerplant efficiency trend 
analysis of 2005 compared to 2016 showed 2.73% growth in 
the BAU scenario. Reducing 4% of CO2 emission by just 
decreasing fuel usage requires 1,086,303 MWh energy 
generation descending, as expressed in Eq 5. In other words, 
124.01 MW must be supplied using the appropriate sources. 
The scenarios mentioned above compensate for it by 
providing energy with different strategies.  
The capacity factor is the annual generation of a power plant 
divided by the product of the capacity and the number of 
hours over a given period (Eq6). The photovoltaic capacity 
factor is an average of 20% for Isfahan province. Therefore, 
the equated power that should be provided by renewable 
energies can be calculated using the capacity factor formula. 

𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 =
𝑃𝑟𝑒𝑎𝑙

𝑃𝑛
× 100           (6) 

where Preal and Pn are the real output power and nominal 
power, respectively.  
Table 2 shows a summary of the results of the calculations for 
different scenarios. This research aims to clarify the 
importance of power plant efficiency and grid losses in the 
contribution of CO2 emission reduction. These two factors can 
help renewable energies, as shown in Figure 9, to provide an 
environmental-friendly energy system. 

8. Conclusion 

In this paper, Isfahan province power plants' CO2 emission up 
to 2025 was estimated by considering the most affecting 
factors on consumers' electricity consumption (GDP and 
population). GP regression with a different covariance 
function was applied to the consumption trend from 2005 to 
2016. The comparison was based on the best R-squared 
validation. Finally, considering the air pollution reduction 
program, which aimed to reduce 4% of CO2 emissions, four 
scenarios were discussed. The scenarios were based on 
altering renewable energies to recent infrastructures with a 
constant rate of technological improvements and alternative 
rate that impacts the promotion of efficiency and transfer 
losses. The main conclusions that can be drawn from this 
study are as follows: 
• Matern covariance function can produce more satisfactory 

results for the fuel consumption trend. 
• Diesel consumption has decreased with a high slope in 

recent years, and it will be reduced more within the 
upcoming years. 

• The primary fuel of power plants is natural gas in Isfahan 
province. By descending other fuels like coal and diesel in 
power plants, natural gas compensates for it, and its 
consumption will be increased. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 2. The results and summary 

Grid losses (%) Efficiency  
growth (%) 

PowerL ** 
(MW) 

PowerE * 
(MW) 

PV Capacity 
(MW) 

Generation Coefficient   

Diesel Ngas 

14.5 1 0 0 620.05 1.3 2852.3 S1 

14.5 2.7 0 3.4 603.05 1.34 2931.02 S2 

7.1 1 9.2 0 574.05 1.3 2852.3 S3 

7.1 2.7 9.2 3.4 557.05 1.34 2931.02 S4 

 



H. Yousefi et al. /Future Energy                                                                                                         May 2024| Volume 03 | Issue 02| Pages 24-30 

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• By applying reformatory developments on existing 
instruments that include power plants, transport, and 
distribution networks, pollution will decrease, and the 
share of demand for renewable plant installation will be 
reduced.    

• The impact of reforming strategies is undeniable, especially 
for renewable installation issues like investment, land 
limitation, and low potential.  

Further research can mostly focus on econometric aspects of 
renewable power plant installation and technological 
development costs. 

 

Figure 9. Comparison of scenarios 
 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically concerning authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere in any language. 

Data availability statement 
Datasets analyzed during the current study are available and 

can be given following a reasonable request from the 

corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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