Article Modeling Long-Term Electricity Generation Planning to Reduce Modeling Long-Term Electricity Generation Planning to Reduce Carbon Dioxide Emissions in Nigeria Juyoul Kim \*, Ahmed Abdel-Hameed, Soja Reuben Joseph, Hilali Hussein Ramadhan, Mercy Nandutu and Joung-Hyuk Hyun Department of NPP Engineering, KEPCO International Nuclear Graduate School, 658-91 Haemaji-ro, Seosaeng-myeon, Ulju-gun, Ulsan 45014, Korea; [eng.aelhameed@kings.ac.kr](mailto:eng.aelhameed@kings.ac.kr) (A.A.-H.); [soja.reuben@kings.ac.kr](mailto:soja.reuben@kings.ac.kr) (S.R.J.); [hilali.ramadhan@kings.ac.kr](mailto:hilali.ramadhan@kings.ac.kr) (H.H.R.); [nmercy@email.kings.ac.kr](mailto:nmercy@email.kings.ac.kr) (M.N.); [cooper2710@kings.ac.kr](mailto:cooper2710@kings.ac.kr) (J.-H.H.) - Correspondence: [jykim@kings.ac.kr](mailto:jykim@kings.ac.kr); Tel.: +82-52-712-7306 Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). - Correspondence: [jykim@kings.ac.kr](mailto:jykim@kings.ac.kr); Tel.: +82-52-712-7306 Citation: Kim, J.; Abdel-Hameed, A.; Joseph, S.R.; Ramadhan, H.H.; Nandutu, M.; Hyun, J.-H. Modeling Long-Term Electricity Generation Planning to Reduce Carbon Dioxide Emissions in Nigeria. Energies 2021, 14, 6258. [https://doi.org/10.3390/](https://doi.org/10.3390/) en14196258 Received: 22 August 2021 Accepted: 28 September 2021 Published: 1 October 2021 Abstract: The most recent assessments conducted by the International Energy Agency indicate that natural gas accounts for the majority of Nigeria’s fossil fuel-derived electricity generation, with crude oil serving mostly as a backup source. Fossil fuel-generated electricity represents 80% of the country’s total. In addition, carbon dioxide (CO2) emissions in Nigeria in 2018 (101.3014 Mtons) demonstrated a 3.83% increase from 2017. The purpose of this study is to suggest an alternate energy supply mix to meet future electrical demand and reduce CO2emissions in Nigeria. The Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) was used in this study to model two case situations of the energy supply systems in Nigeria to determine the best energy supply technology to meet future demand. The Simplified Approach to Estimating Electricity Generation’s External Costs and Impacts (SIMPACTS) code is also used to estimate the environmental impacts and resulting damage costs during normal operation of various electricity generation technologies. Results of the first scenario show that gas and oil power plants are the optimal choice for Nigeria to meet future energy needs with no bound on CO2emission. If Nigeria adopts CO2emission restrictions to comply with the Paris Agreement’s target of decreasing worldwide mean temperature rise to 1.5 C, the best option is nuclear power plants (NPPs). The MESSAGE results demonstrate that both fossil fuels and NPPs are the optimal electricity-generating technologies to meet Nigeria’s future energy demand. The SIMPACTS code results demonstrate that NPPs have the lowest damage costs because of their low environmental impact during normal operation. Therefore, NPP technology is the most environmentally friendly technology and the best choice for the optimization of future electrical technology to meet the demand. The result from this study will serve as a reference source in modeling long-term energy mix therefore reducing CO2 emission in Nigeria. Received: 22 August 2021 \ \\mathrm{(C O\_{2})} \\mathrm{C O}\_{2} 1.5,^{\\circ}\\mathbb{C}, \\mathrm{C O\_{2}} Known as the “Giant of Africa”, Nigeria is located on Africa’s west coast, with total 2 2 2 size of 923,766 km comprising of 910,768 km land mass and 13,000 km water bodies \[1\]. As of 2021, the current population is 211,400,708 (a 2.55% increase from 2020). The total GDP of Nigeria in 2019 was $448.12 billion, demonstrating a 12.8% rise from 2018 \[2\]. In 2019, the gross domestic product (GDP) growth rate was 2.21%, a 2.09% increase from 2018\. In 2019, the GDP per capita was $2230, a 9.97% increase from 2018 \[3\]. The Statistical Review of World Energy 2019 by British Petroleum (BP) shows that Nigeria is Africa’s top oil producer \[4\]. It boasts Africa’s greatest natural gas reserves. It was rated as fifth-largest liquefied natural gas exporter in the world in 2018. The country’s economy majorly relies on crude oil and natural gas reserves \[5\]. However, despite being Africa’s largest crude 1. Introduction 923{},{666};\\mathrm{k m}{}^{2} * * * _Energies 2021, 14, 6258_ 2 of 17 oil producer, Nigeria’s output is hampered by intermittent supply disruptions. Nigeria’s economy is primarily reliant on oil revenue, therefore variations in crude oil prices have a significant impact. The country’s crude oil and natural gas exports are expected to generate $55 billion in 2018, according to the International Monetary Fund ($23 billion higher than that in 2016) \[5\]. In 2017, Nigeria’s electrical generation capability was 12,664 MW, with fossil fuels accounting for 10,522 MW (83 percent), hydroelectricity 2110 MW (16 percent), and solar, wind, biomass, and waste sources accounting for 32 MW (1 percent). Natural gas provides the majority of Nigeria’s fossil fuel-based energy generation. The most recent emission projections, based on the revised business-as-usual (BAU) scenario and low carbon scenario presented in Nigeria’s Third National Communication, show that they will continue to increase until 2030 and that reductions will not be enough to meet the upper range of the national mitigation targets. Nigeria needs to increase the scale of its climate action to align with the Paris Agreement temperature target of regulating the worldwide mean temperature rise of 1.5 C \[6\]. To cope with economic development and to reduce GHG emissions, the need for renewable and nuclear energy in Nigeria’s energy mix is clear. Therefore, between 2005 and 2028, the Nigerian government intends to generate 4800 MW from nuclear power. To realize its set goal, the government engaged a contract with the Government of Russian State Atomic Energy Corporation (ROSATOM) to build and operate twin nuclear power plants (NPPs) and a flexible research reactor compound for its nuclear research reactor. Four power reactor units will be constructed by ROSATOM with a total capability of 4800 MW. The VVER-1200 reactor is to be supplied by Atomstroyexport. Two candidate sites have been selected for the planned NPPs namely the Geregu site in Kogi State and the Itu site in Akwa Ibom State. Each facility will accommodate nuclear reactors with a combined capacity of 2400 megawatts (MW). \[7\]. In Nigeria, government support and investor interest in solar power projects has grown in recent years as a method to alleviate natural gas supply constraints and improve access to electricity in remote and rural communities. A $75 million award is being jointly sponsored by the Nigerian government, the Rural Electrification Agency, and the Nigeria Electrification Project (supported by the World Bank) to stimulate off-grid solar projects therefore minimizing the dependence on kerosene and diesel consumption for lighting and backup power \[8\]. A study was conducted to match electricity supply with electrical demand in Nige- ria \[5\]. MESSAGE code was employed to simulate Nigeria’s long-term energy plan and the viability of its nuclear power resources. The results demonstrated that the ratio of hydroelectricity to the total generated capability decreased from 31.30% to 11% in 2005 and 2030. In addition, the ratio of fossil fuel electricity capability increased from 68.30% to 82.15% in 2005 and 2010, then decreased in 2030 to 62.95%. According to the study, coal and nuclear power (neither of which are currently used for power generation) are expected to contribute 15.6% and 6.7% of electricity production by 2030. Solar energy and wind power are also expected to contribute 8.3% and 1.8% by 2030 \[9\]. Another study by Baraka et al. \[10\] used the MESSAGE code to model energy supply alternatives for electric- ity production in Tanzania from 2010 to 2040 \[10\]. Three economic scenarios developed for modeling purposes: Business as usual (BAU), low economic consumption (LEC), and high economic growth (HEC). Dry weather scenarios were also established to explore how the national electrical system operates under dry weather situations. The outcomes of the model suggested that as the expected final power demand for the three scenarios increases, its overall installed capability also increases from the base 804.2 MW by 9.05%, 8.46%, and 9.8%, respectively. Therefore, the model results show the dominance of hydroelectricity, coal, fossil, and geo-thermal energy has the minimum energy supply alternatives in terms of cost of electricity production in all situations \[10\]. A study of Mongolia’s long-term energy plan with NPPs using the MESSAGE code aimed to create the first model of Mon- golia’s energy modeling to predict the feasibility of introducing nuclear power in its energy circle and to fulfill total electricity demand by 2040. The study also aimed to predict what emerging technologies could meet future electricity demand. After weighing the costs and CO emissions of various generation technologies, nuclear power technology was selected * * * _Energies 2021, 14, 6258_ 3 of 17 as the best option to meet the electricity shortage. Nuclear power had a cost advantage in terms of the effect of fuel prices on power generation costs. However, coal continues to be a favored resource over NPPs because of its low price and abundant deposits in Mongolia \[11\]. Liun et al. \[12\], used the SIMPACTS code in his study to evaluate the environmental effects of major electricity generation technologies in Java, Indonesia \[13\]. That study’s main purpose was to explore the future optimal energy mix in Indonesia using an approach that combined both economic and environmental aspects in long-term energy system analyses. The study concluded that the Muria NPP had the lowest cost of damage among all electricity generation alternatives investigated. Another study used the SIMPACTS software to estimate the effects in terms of costs from various electricity genera- tion technologies \[14\]. That research assessed the externalities from several power plants in metropolitan area of Mexico City by developing a unique system to apply the impact pathway analysis (IPA) of SIMPACTS to evaluate the resulting cost resulting in damage from various plants. The estimate showed that the annual total cost was $71 million. A similar study was conducted (using IPA) to determine the damage costs of Syrian elec- tricity generation \[15\]. The results demonstrated that the environmental impacts could add considerable external costs to the typical generation cost, of which externalities vary between US 2.5 and 0.07 cents/kWh. There is an argument around the use of nuclear power plants in the energy mix. After Chernobyl accident in 1986, some countries were turned off their nuclear reactors; the number of reactor connected to grid was sharply decreased \[16\]. On the other hand, some countries see nuclear energy as a worthy alternative to fulfill their sustainability and reliability of energy, as well as reducing air pollutants, greenhouse gases \[17\]. A Recent study on public perception to nuclear power plants in Nigeria shows 44% of the respondents did not support the country’s decision to introduce nuclear power as part of its electricity generation mix \[18\]. This study’s objective is to use the MESSAGE software to assess the BAU technology used to generate electricity in Nigeria without any constraints on the CO2 emissions. In addition, the external cost of environmental impacts will be calculated using the SIMPACTS software. Constraints on CO2emissions for oil and gas power plants will be applied and the best choice of technology that can generate electricity with minimal external cost and less environmental impacts will be determined. According to the Nigeria climate transparency report of 2020, worldwide CO2emissions must be reduce by 45% less than the 2010 ranks by 2030 to achieve the Paris agreement on net-zero by 2050 \[6\]. Thus, predicting Nigeria’s long- term energy needs and estimating its associated environmental impact therefore reducing carbon emission is vital. Various research has been conducted using different energy modeling tools with the aim of modeling long-term electricity generation technologies to meet the future electricity demand of Nigeria; however, no studies conducted in assessing the environmental impacts and damage cost associated with such technologies therefore reducing carbon dioxide emission emanating from existing technology. In this study, we evaluate the best choice of technology that can be used to generate electricity based on two criteria, cost and environmental impact. Findings from this study will serve as a reference point for modeling long term energy mix and reduce CO2emission. This study consists of four sections. The Section1describes the energy situation in Nigeria and a literature review of our work. The Section2presents the methods and input data for both programs. The Section3discusses the results obtained from the two software. The Section4summarizes the conclusion of this study. ## 2\. Materials and Methods The methods employed in this paper were divided into two scenarios. The first scenario was the current technologies used in Nigeria to generate electricity. The second scenario involved applying CO2 emission limits to the oil and gas power plants. In each scenario, two software techniques were used. The MESSAGE code was used to find the optimal energy mix of technologies to meet the future demand for electricity. The * * * SIMPACTS code was used to estimate the environmental impact and damage costs of these technologies. 2.1. First Scenario (No Carbon Dioxide Emission Constraints) 2.1.1. MESSAGE Input Data 2.1.1. MESSAGE Input Data The MESSAGE software is used to create optimization models for energy supply systems. These models can be developed at state, sub regional, and regional levels to evaluate various energy system development strategies in the short or long term while considering their overall environmental impact. MESSAGE is a system engineering optimization model that optimizes a linear objective function under certain constraints or linear equalities and inequalities based on decision variables. The objective function may be to minimize costs or maximize profits, and several optimization parameters are applied where the optimal solution would be sought \[19\]. The objective function is obtained from the system cost. For each period (denoted as “t”), the following information is recorded: Fixed and variable operation and maintenance costs Fixed and variable operation and maintenance costs Investment and penalty costs, and taxes induced by regulation Investment and penalty costs, and taxes induced by regulation \\begin Tarray}{r}{T omathit a l a\ \\mathit y{s t s t m}\ c o o t t=\\sum\_{t=1}^{T}\\beta\\times\\sum\_{i=1}^{n}C\_{i t}\\times X\_{i t}}\\end{array} (1) 1 r where CitXitis the sum of costs incurred in period (t) and b =. In Equation (1) \[20–22\], in=1 1+ (r) is the discount rate. The performance of a linear programming solution may be an infeasible solution \\textstyle\\sum\_{i=1}^{n}C\_{i t}\\times X\_{i t} \\begin{array}{r}{\\beta=\\frac{1}{1+r}}\\end{array} The performance of a linear programming solution may be an infeasible solution because of restrictions that prevent the program from finding the optimal solution. These restrictions are an unbounded solution (where the objective function takes arbitrary values) or an optimal solution (where the objective function value is optimized). The decision variables, on the other hand, cannot constantly vary and must take only discrete values. MESSAGE allows the use of mixed-integer programming (MIP) when certain variables in an optimization problem are integers. Unlike in linear programming, where large-scale problems are solved regularly, MIP is a methodology where finding the best solution is difficult. Large-scale MIPs can take a long time to solve an optimization problem, or it may never be solved. Figure1illustrates the different steps involved in converting various energy resources to different energy forms using several technologies to produce the required electricity in Nigeria using the available resources. The energy chain consists of various levels of resources: primary, secondary, and final energy forms. All resources are useful for industrial and commercial purposes. Various resources are transformed from one energy level to another using different technologies from production, import, and extraction at the resources level, following processing and supply at the primary level. Power production technologies are initiated from primary to secondary energy levels to produce electricity, gas, and oil. Transmission and distribution occurs at the final energy level, with the useful demand of electricity and various resources used for commercial and industrial purposes. Energy technology is a process of transforming one energy form into another energy form. Several skills are employed in producing and transforming various energy forms from the initial resources level to the final energy demand level. The various technologies employed in this study are represented in Figure1. The input data were divided into general input data and technology input data for both scenarios. The general input data comprised the base year, the modeling period, the discount rate, the resources available, and the energy forms at various levels (primary, secondary, and final) as well as the final demand. The technology input data consisted of the various technologies used in converting resources to final electricity and demand. Table1summarizes the basic input data used in MESSAGE to predict future energy needs. In this study, the available resources for energy production in Nigeria were first modeled for electricity generation from oil and gas resources over various energy forms. The final demand for all the available resources used for energy and industrial consumption was predicted using the MESSAGE code. * * * , x FOR PEER REVIEW Future energy demand is the most important input parameter in the optimization process for the future energy mix. Table 1. General input data used in MESSAGE code for both scenarios using a base year of 2015 and modeling period of 45 years. Parameter Input | Parameter | | --- | | Base Year | | Modeling Period | | Discount Rate | | Available Resources Used | | Energy Forms | | Demand | | Input | | --- | | 2015 | | 2015 to 2060 | | 15% \[23\] | | Oil and Gas | | Resources, Primary, Secondary, and Final | | Final Demand for Electricity | The first model year is referred to as the base year, and the modeling period represents The first model year is referred to as the base year, and the modeling period the period of prediction commencing from the base year up to the last model year. In represents the period of prediction commencing this study, the base year is 2015, the last model year is 2060, and the time step is 1 year. The discount rate represents the rate at which the value of the plant decreases over time due to depreciation in the value of the plant over time. As a result, discount rates are employed to assign a significance to impending cash flows. The lesser the value attached to future savings in today’s judgments, the greater the discount rate. As a result, great discount rates create energy effectiveness initiatives and regulations appear less appealing. MESSAGE allows four types of data sets to give values to any data entity. These data types are; constant (c) which is a fixed number used during the modeling period in the case study; Time series (ts) which is used to enter the required values for each year in the case study; Constant growth (cg) which is the average annual growth rate, which indicates the increase in the value of demand over the modeling period; and periodic growth (pg) which defines a set of average annual growth rates for the specific number of periods in the study \[24\]. This study adopted the constant growth method to enter values for all future electricity demands which is in line with the population growth of Nigeria. Average annual growth rate (AAGR) of population in Nigeria is 2.6% which was calculated using the population data obtained from Energy Information Administration (EIA) from 1980 to 2015 while the Average constant growth rate in electricity demand is 5.5% which was also calculated in the same manner. The electricity demand data from 1980 until 2015 (base year) were collected and plotted in Figure2and used to calculate the average annual growth rate which is consistent with the population growth rate adopted for this study. The initial value of future electricity demand in 2016 was 25,941.970 GWh \[24\], with a constant mean yearly increase of 5.5% throughout the modeling age. The technology input data are divided into modeling age. The technology input data are divided into two tabs. The first is the activity two tabs. The first is the activity tab describing the input and output of the technology, tab describing the input and output of the tec the loss during the transformation process, the variable operations, the maintenance costs, and the output level in the base year. Table2describes the activity tab input parameters for every type of technology evaluated in this study. The second is the capacity tab that describes the first and last years in which the technology can be built, the plant capacity factor, the plant lifetime, the investment costs and fixed costs, the historical capacity, and the minimum power. Table2describes the capacity tab input parameters for each type of technology evaluated in this study. Figure 2. Prediction of future electricity demand for Nigeria from 2015 to 2060 using av Figure 2. Prediction of future electricity demand for Nigeria from 2015 to 2060 using average annual growth rate calculated from historical data obtained from Energy Information Administration from 1980 to 2015 \[24\]. | Technology Name | Plant Capacity Factor | Plant Life(Years) | | --- | --- | --- | | Electricity distribution(Elect-TD) | N/A | 60 | | Oil power plant(Oil-PP) | 0.3 | 35 | | Gas power plant(Gas-PP) | 0.3 | 40 | | Solar power plant(Solar-PP) | 0.2 | 25 | | Hydropower plant(Hydro) | 0.3 | 70 | | Wind power plant | 0.15 | 20 | | Nuclear power plant(Nuc-PP) | 0.9 | 60 | | Investment Cost (USD/kW)\[25\] | Variable Cost(USD/MWh)\[25\] | Loss | Output Level in the Base Year(GWh) | | --- | --- | --- | --- | | 1000 | 10 | 20% | 25941.97 | | 1563 | 4.7 | 50% | 3764.277 | | 1175 | 6.2 | 50% | 21330.903 | | 1313 | 0 | 0 | 24 | | 5316 | 0 | 0 | 3 | | 1265 | 10 | 0 | 5663 | | 4800 | 2.37 | 10% | N/A | * * * _Energies 2021, 14, 6258_ 7 of 17 2.1.2. SIMPACTS Input Data SIMPACTS is a code developed by the IAEA to estimate and quantify the health and environmental implications of various energy generation methods, as well as the associated damage costs. This code is especially suitable for comparing fossil, nuclear, and hydroelectric power plants. It consists of different modules to estimate the effects of routine pollutants from energy facilities on human health, agricultural crops, and buildings. It can be used to compare the cost-effectiveness of fossil, nuclear, and renewable energy production, as well as the establishment of new power plants and environmental mitigation policies. It calculates both internal and external costs. The code requires a small amount of input data, simplifying its use and applications. The model uses the impact pathway analysis (IPA) method for airborne emissions from fossil or nuclear plants, in which the emission source is characterized, and a catalog of flying discharges is prepared. The variations in ambient absorptions of different contaminants are estimated using atmospheric dispersion and deposition models. As a result, exposure–response functions (ERFs) are used to link a shift in contaminant attentiveness to a physical effect on the receptors in question. SIMPACTS calculates the physical effects and associated cost resulting from damage to human health, agricultural crops, and receptors associated with routine air emissions (e.g., particulates, SO2, NOx, CO2, etc.) from fossil fuels, typical operational releases of radioactivity to air or surface waters from NPPs, and external costs of hydropower projects in terms of impact from hypothetical dam failure accidents. The methodology is termed IPA, where physical impacts (health consequences) and external costs (damages) are calculated by tracing the fate of a pollutant from the point of emission to its environmental interaction, dispersion and chemical transformation, and receptor uptake in terms of exposure as well as estimating the resulting effects and resulting damage costs related with such emissions \[26\]. The damage cost is calculated by the product of total number of cases or impacts with the associated unit costs. Equation (2) represents the effect on human health while Equation (3) represents the impact on receptor r across the entire domain. 2 _n n_ _I_ _ik_ = G _x=1 y=1rxyer fik Cixy_(2) where G is the grid size for each exposure area A _xy_ within the impact region (km). The population density is r _xy_ within the exposure area (person/km). The unit health impact is _erfik_ for type k and classes i (cases/year per person/ g/m3). The surge in ground-level ambient air concentration is C _ixy_ within the exposure area ( g/m 3 ) \[27\]. _n n_ _I_ _r_ = _x=1 y=1Rrxyer frxy Cxy_(3) where R _rxy_ is the total annual crop production (tons per year) of receptor r within the exposure area A _xy_, erf _rxy_ is the erf of receptor type r (% yield change per g/m 3 ), and C _xy_ is the incremental concentration of the pollutants SO2within the exposure area ( g/m 3 ). Equation (4) represents the external cost (ECY) owing to the health impact and Equation (5) represents the external cost owing to the agricultural impact. ## ECYik= IikUk(4) where I _ik_ is the health effect for type k and classes i (cases/year), U _k_ is the unit cost for health effect k (dollar/case) \[27\]. _n n_ _ECYr_ = _x=1 y=1_ _I_ _rxyUr_(5) where I _rxy_ is the annual crop yield reduction (tons per year) of receptor type r within the exposure area A _xy_, and U _r_ is unit cost of receptor type r (dollar per ton). In this scenario, two types of power plants were simulated and the environmental impact of each was measured. The first power plant was the Egbin fossil power plant in * * * Lagos State; It is Nigeria’s and the West African sub-region’s largest electricity-generating station which supplies over 30 percent of the overall existing electricity generation to the country’s national grid and comprises six units, each producing 220 MW with the overall country’s national grid and comprises six units, each producing 220 MW with the overall designed capability of 1320 MW \[28\]. Table3shows the emission and dispersion input data required to simulate the fossil power plant in SIMPACTS in addition to the health and country’s national grid and comprises six units, each producing 220 MW with the overall designed capability of 1320 MW \[28\]. Table3shows the emission and dispersion input data required to simulate the fossil power plant in SIMPACTS in addition to the health and data required to simulate the fossil power plant in SIMPACTS in addition to the health and agricultural impacts. The second power plant is the Jebba hydroelectric power plant. It is agricultural impacts. The second power plant is the Jebba hydroelectric power plant. It is located 100 km downstream of the Kainji Dam in Niger State. The plant design capacity is 578.4 MWe from six turbines (each turbine generates 96.4 MW). The power of this plant is enough to power 364,000 homes. This plant is one of Nigeria’s most cost-effective electricity sources. It was commissioned in 1985 \[29\]. Table4is a summary of the input information agricultural impacts. The second power plant is the Jebba hydroelectric power plant. It is located 100 km downstream of the Kainji Dam in Niger State. The plant design capacity is 578.4 MWe from six turbines (each turbine generates 96.4 MW). The power of this plant is enough to power 364,000 homes. This plant is one of Nigeria’s most cost-effective electricity sources. It was commissioned in 1985 \[29\]. Table4is a summary of the input information sources. It was commissioned in 1985 \[29\]. Table4is a summary of the input information used to simulate the Jebba hydropower plant. Table 3. Input data of Egbin fossil power plant simulated in SIMPACT code. | Domain Data | | | | --- | --- | --- | | Domain Name | Egbin Fossil Power Station | | | Time Frame | Full year | | | Cell Size | 50×50km | | | Latitude | 6.5635° | | | Longitude | -3.6151° | | | Emission and Dispersion | | | | Base elevation | 10m | | | Stack Height\[30\] | 40m | | | Stack Diameter | 8.3m | | | Exit Temperature\[31\] | 541.15℃ | | | Exit Velocity\[31\] | 40.96m/s | | | SO2 emissions\[32\] | 33.588kg/h | | | NOx emissions\[32\] | 2928.6kg/h | | | PM10 emissions\[32\] | 65.772kg/h | | | Month | Ozone(O3)Concentration | Ammonia(NH3)Concentration | | All year | 80ppb(default) | 10ppb(default) | | Health Impacts | | | | Lagos | | | | Population Density=3345/km2\[33\] | Ogun | | | Population Density=311.3/km2\[33\] | | | 50\\times50,\\mathrm{k m} 6.5635^{\\circ} -3.6151^{\\circ} \\mathrm{S O\_{2}} 541.15,^{\\circ}\\mathrm{C} \\left(\\mathrm{O}\_{3}\\right) 65.772,\\mathrm{k g}/\\mathrm{h} Table 4. Input data for Jebba hydropower plant simulated in SIMPACT code. | Site Location and Cost Data | | | | --- | --- | --- | | Economic defaults fromGDP per Capita\[34\] | | NIGERIA$2097 per capita | | Hydro Power Plant Data | | | | Plant Capacity\[35\] | | 578.4 MW | | Capacity Factor\[36\] | | 83% | | Lifetime | | 50 years | | Dam Data | | | | Reservoir inundated area\[37\] | | 350km2 | | Average Dam failure rate(default) | | 0.0001 fraction | | Average accident warning time(default) | | 1.5h | | Population Data | | | | Population displacedShare of population resettled/compensatedPopulation at risk in the event of accidentValue of statistical life | | 28,875 persons60%47,200 persons$128,839.11 | | Land Use Data(type) | | | | Region typeType of Terrain | | TropicalFlatlands | | Agricultural Products\[38\] | | | | Maize Corn | 2800 tons/year | 8$/ton | | Beans | 4200 tons/year | 12$/ton | | Onions | 3500 tons/year | 10$/ton | | Rice | 5600 tons/year | 16$/ton | * * * _Energies_ , 14, x FOR PEER REVIEW 10 of 18 kWh) Type of Terrain Flatlands **Agricultural Products \[38\]** Maize Corn 2800 tons/year 8 $/ton _Energies 2021, 14, 6258_ Beans 4200 tons/year 12 $/ton 9 of 17 Onions 3500 tons/year 10 $/ton Rice 5600 tons/year 16 $/ton _2.2. Second Scenario (with Carbon Dioxide Emission Constraints)_ _2.2. Second Scenario (With Carbon Dioxide Emission Constraints)_ In this scenario, the CO22 emissions from oil and gas power plants were restricted with emissions from oil and gas power plants were restricted with In this scenario, the CO a certain value. Figure3shows the relationship between CO2 emissions and the electricity 2emissions and the electricity a certain value. Figure 3 shows the relationship between CO generated by fossil power plants in Nigeria from 1980 to 2018. generated by fossil power plants in Nigeria from 1980 to 2018. Electricity generated by Fossil fuels (billion 100120 80 emissions (MMtonnes) 2 2530 204060CO 20 0 1015 Years Electricity generated by Fossil fuels (billion kWh) 5 CO2 emissions (MMtonnes) Upper limit of Co2 Emission 0 **Figure 3. Figure 3.** Showing the trend of increasing CO showing the trend of increasing CO22 emissions concurrent with the increasing electricity generated by fossil fuels emissions concurrent with the increasing electricity generated by fossil fuels in Nigeria from 1980 to 2018 \[39\]. in Nigeria from 1980 to 2018 \[39\]. 2.2.1. MESSAGE Input Data 2.2.1. MESSAGE Input Data Constraints are limits or restrictions imposed on the decision variables. The Constraints are limits or restrictions imposed on the decision variables. The restric-1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 restrictions in MESSAGE represent various limitations on technology (including emission tions in MESSAGE represent various limitations on technology (including emission limits limits imposed by environmental regulations). An important constraint in a model of an imposed by environmental regulations). An important constraint in a model of an energy energy supply system is the satisfaction of demand. Depending on the model, the energy supply system is the satisfaction of demand. Depending on the model, the energy demand demand can be expressed at the level of fina l energy forms (demand for gas, demand for can be expressed at the level of final energy forms (demand for gas, demand for electricity, electricity, etc.) or at the useful energy le vel (demand for transportation by cars, demand etc.) or at the useful energy level (demand for transportation by cars, demand for residen- for residential heat, demand for industrial heat, etc.). The constraint is that supply must tial heat, demand for industrial heat, etc.). The constraint is that supply must be at least be at least equal to demand at each period “t.” equal to demand at each period “t”. ∑ 𝑆 × 𝑋 ≥ 𝐷 (6) _i=n_ _i=1_ _SijXitDjt_(6) where _jt_ is the demand for energy form ( _D it_ is the activity of technology _j ) in period ( t ), X_ where D _jt_ is the demand for energy form ( _ij_ is the rate at which technology ( ( i ) in period ( _j) in period (t),_ _t ), and_ _Xit_ is the activity of technology ( _S i ) produces energy form (_ _i)_ _j ) \[20\]. In_ in period (t), and S _ij_ is the rate at which technology ( 2 emissions was determined from information in the _i) produces energy form (_ this study, the upper limit of CO _j) \[20\]. In this_ study, the upper limit of CO2emissions was determined from information in the Nigerian Nigerian climate transparency report 2020 and the Paris Agreement \[6\]. To keep its climate transparency report 2020 and the Paris Agreement \[6\]. To keep its determinations 2 determinations to restrict world average temperature rise to 1.5 °C on track, global CO to restrict world average temperature rise to 1.5 C on track, global CO2emissions must be 45 percent lower than 2010 levels by 2030 and net-zero by 2050. Therefore, it was assumed that the upper limit of CO2emissions shall not exceed 21,983.55 kton \[6,40\]. In all cases, the input data used in MESSAGE code includes the historical data of electricity production for various electricity generation sources from 1980 to 2015 obtained from energy information administration (EIA) alongside other defaults values and plant specific data calculated by the code. The historical data obtained were used for prediction of future electricity generated by each technology for both scenarios throughout the modeling period. As shown in Figure4, the gray line represents the upper limit assumed in the MESSAGE code 2046 for a fossil power plant. CO2emission intensities were below 400 g/kWh in the majority 2044 , x FOR PEER REVIEW of natural gas combined cycle power plants \[41\], whereas oil power plants had emission intensities below 644 g/kWh \[ 2042 42\]. \\mathrm{C O\_{2}} Figure 4. Increase in electricity production and demand from all power plants without a limit on CO Increase in electricity production and demand from all power plants without a limit on CO 2 emission using Figure 4.2emission using MESSAGE code for the modeli ng period under study. MESSAGE code for the modeling period under study. \\mathrm{C O\_{2}} 2.2.2. SIMPACT Input Data 4.883 6224.68 4.883 In this scenario, an NPP was introduced as an alternative clean energy for oil and gas power plants. The external cost for environmental impacts was calculated by SIMPACTS. 6.0 The SIMPACTS software can estimate the environmental impact of radioactive releases from NPPs deposited on the ground and on vegetation during routine operation. These 4.05.0 radioactive discharges have an external and internal impact on human health, as radionu- 3.0 clides in the air and food are inhaled and eaten, respectively. Nigeria has plans to construct 0.860 1.02.0 an NPP at the Geregu site. According to the agreement between Nigeria and ROSATOM, 701.26 1,0002,0003,0004,0005,0006,0007,000 351.76 two units of VVER-1200 reactors will be constructed. Damage costs \[US$ / year\]0In this study, a similar unit was Health impact \[cases / year\]simulated using SIMPACTS software. In all cases, the input data used in SIMPACTS code to estimate the environmental impact and damage cost associated with various electricity Pollutant Pollutant generation technologies from fossil, hydro and nuclear power plants includes plant specific data for particulate emission and dispersion from Egbin fossil power plant obtained PM10 Sulfates Nitrates ( a from previous literature, population density around the cell size of the plant, the area of) ( b) Environmental impacts and damage costs of th coverage estimated using google earth as well as other SIMPACTS default values which e Egbin fossil power plant (FPP) in Nigeria. ( provides the resulting damage cost; for Jebba hydro power plant includes plant capacity showing the number of cases per year affected by pollution from Egbin FPP. ( factor, reservoir inundated area and some agricultural production obtained from previous research alongside other defaults data provided in the SIMPACTS code; for the proposed Geregu Nuclear power plant includes plants stack height, exit velocity emission rate of The environmental impacts of the Jebba hydr various radionuclides obtained from previous studies of the same proposed reactor type, SIMPACTS code. Although the Jebba hydrop population density of the area as well as export share of some agricultural production obtained from previous research alongside other defaults data with respect to health risk and 2, 35 km2, and 3.5 km2 of forest, farmland, and other types of land during damage cost estimated by the SIMPACTS code. Table5summarizes the input data using SIMPACTS to simulate the proposed Geregu NPP to assess the environmental impacts. the installation and operation of the Je 2 is a highly emitted pollutant, followed by GHG (in carbon equivalent) and CH 4 during the normal * * * Table 5. Input data required to assess environmental impacts and associated health and damage costs of radionuclide emissions from a nuclear power plant using SIMPACT code. | Domain Data | | | | --- | --- | --- | | Domain Name | Nigeria Nuclear Modeling | | | Time Frame | Full year | | | Cell Size | 50×50km | | | Latitude | 49.3° | | | Longitude | -1.9° | | | Emission and Dispersion | | | | Base elevation | 20m | | | Stack Height\[43\] | 100m | | | Stack Diameter | 3m | | | Exit Temperature | 177℃ | | | Exit Velocity\[44\] | 15m/s | | | Emission cycle | Constant | | | Emission rate unit\[45\] | GBq/year | | | $^{3}$H emissions | 3080 GBq/year | | | $^{14}$C emissions | 1050 GBq/year | | | $^{131}$I emissions | 0.342 GBq/year | | | $^{133}$I emissions | 0.640 GBq/year | | | $^{60}$Co emissions | 0.102 GBq/year | | | $^{85}$Kr emissions | 6720 GBq/year | | | $^{134}$Cs emissions | 0.080 GBq/year | | | $^{137}$Cs emissions | 0.071 GBq/year | | | $^{133}$Xe emissions | 18,666.7 GBq/year | | | | Population Density\[33\] | | | | Population Density=1129/km^{2}$ | | | Impact | Specific risk factors \[cases per man Sv\] | Specific economic values\[$\] | | Fatal cancer | 0.05 | 218.26 | | Nonfatal cancer | 0.12 | 237.6 | | Specific hereditary effect | 0.01 | 7143.91 | | | Food production\[38\] | | | Food | Export share\[%\] | Production\[tons/year\] | | Beef | 0 | 1454.1 | | Chicken | 0 | 256.2 | -1.9^{\\circ} 49.3^{\\circ} 50\\times\\dot{5}0;\\mathrm{k m} 177,^{\\circ}\\mathrm{C} ^3\\mathrm{H} ^{14}\\mathbb{C} ^{131}\\mathrm{I} {{}^{60}}\\mathrm{C o} ^33\_\_{\\mathrm{I}} ^{85}\\mathrm{K r} ^{134}\\mathrm{C s} ^{137}\\mathrm{C s} ^{133}\\mathrm{X e} =\\dot{1}29/\\mathrm{k m}^{2} In this scenario, gas and oil power plants are the predominant sources of electricity that meet the future electricity demand. Hydropower, solar, and wind power plants have a low contribution to the energy mix. There is no contribution from NPPs. The energy supply system reflects the current existing energy policy for the country where oil and gas are the country’s major energy resources. Figure4demonstrates the optimal electricity generation from different technologies concurrent with future electricity demand. The results show that oil is the optimal choice of electricity generation from 2016 to 2045 and gas is the optimal choice of electricity from 2046 to 2060. When the Egbin fossil power plant is in normal operation, the numbers of cases of health impacts for individuals affected annually as a result of emissions of PM10, SO2, and NO2 are 0.387, 0.860, and 4.883, respectively 3.1. First Scenario (No Carbon Dioxide Emission Constraints) \\mathrm{C O\_{2}} \\mathrm{P M} _{10},,\\mathrm{S O}_{2} \\mathrm{{N O}\_{2}} * * * _Energies_ **2021, 14, x FOR PEER REVIEW** 13 of 18 200,000250,000 300,000350,000 150,000 100,000 50,000 _Energies 2021, 14, 6258_ 12 of 17 Electricity generated by each power plant (GWh) (Figure5a). This indicates that more individuals are exposed to nitrate pollutants than years to particulate matter (PM) and oxides of sulfur (sulfate). The damage costs owing to the Gas-PP/Electricity health effects experienced by people as a result of exposure to pollutants and PM, sulfur Oil-PP/Electricity Nuc-PP/Electricity Solar-PP/Electricity Wind-PP/Electricity oxides, and nitrogen oxides are $701.26, $6224.68, and $351.76, respectively during the Hyd1/Electricity Electricity demand operation of the Egbin fossil power plant (Figure5b). These damage costs are equivalent **Figure 4.** Increase in electricity production and demand from all power plants without a limit on CO to the costs required for treatment of health effects resulting from exposure to emitted 2 emission using MESSAGE code for the modeli gaseous pollutants. ng period under study. 7000 6224.68 4.883 6000 5000 4000 3000 2.03.04.05.06.0 2000 0.860 0.387 1.0 1000 701.26 351.76 0.0 Damage costs \[US$ / year\]0 Health impact \[cases / year\] PM10 Sulfates Nitrates Pollutant Pollutant ## ( a) (b) **Figure 5. Figure 5.** Environmental impacts and damage costs of the Egbin fossil power plant (FPP) in Nigeria. ( Environmental impacts and damage costs of the Egbin fossil power plant (FPP) in Nigeria. ( **a) SIMPACT results a ) SIMPACT results** showing the number of cases per year affected by pollution from Egbin FPP. ( PM10 Sulfates Nitrates **b) SIMPACT results showing damage costshowing the number of cases per year affected by pollution from Egbin FPP. (** **b**) SIMPACT results showing damage cost resulting from pollution generated by Egbin FPP. resulting from pollution generated by Egbin FPP. The environmental impacts of the Jebba hydropower plant were calculated using the The environmental impacts of the Jebba hydr opower plant were calculated using the SIMPACTS code. Although the Jebba hydropower plant is an example of renewable energy, SIMPACTS code. Although the Jebba hydrop ower plant is an example of renewable it also has an impact on the environment. Table6summarizes the physical impacts that energy, it also has an impact on the enviro nment. Table 6 summarize s the physical impacts result from the normal operation of the Jebba hydropower plant. Table6presents a loss that result from the normal operation of th e Jebba hydropower plant. Table 6 presents a of 311.5 km 2 , 35 km , 35 km 2 , and 3.5 km , and 3.5 km 2 of forest, farmland, and other types of land during the of forest, farmland, and other types of land during loss of 311.5 km 2 2 2 installation and operation of the Jebba hydropower plant, respectively. the installation and operation of the Je CO2 2 is a highly is a highly bba hydropower plan t, respectively. CO emitted pollutant, followed by GHG (in carbon equivalent) and CH ted pollutant, followed by GHG (in carbon equivalent) and CH emit 4 4 during the normal during the normal operation of the Jebba hydropower plant. These findings suggest that the Jebba hydropower operation of the Jebba hydropower plant. These findings suggest that the Jebba plant’s operation may be contributing to global warming. The external damage costs hydropower plant’s operation may be contri buting to global warming. The external damage costs of the Jebba hydropower plant are 846.6 $per MWh for land loss 782.8 $per of the Jebba hydropower plant are 846.6 $per MWh for land loss 782.8 $per MWh for displacement of people, and 1587 $per MWh for GHG emissions. These costs are the funds the operator must pay for compensation of damages owing to the deployment and operation of the Jebba hydropower plant. The emission of gaseous pollutants during plant operation presents the highest damage cost. **Table 6. SIMPACTS results showing the physical impacts and damage cost from Jebba hydropower plants in Nigeria.** **Parameter Item Values Damage Cost ($/MWh)** Forest 311.5 2 Farmland 35 Loss of Land (km) Other land 3.5 846.6 Total 350 Displacement and Displaced persons 28,875 Resettlement (persons) Resettled persons 17,325 782.8 CH4(Mean) 6300 Emissions (tons/year) CO (Mean) 629,300 GHG (in carbon equivalent) 207,709 * * * 3.2. Second Scenario (With Carbon 3.2. Second Scenario (with Carbon Dioxide Emission Constraints) 2044 2044 In this scenario, it was assumed that NPP technology was an alternative solution for In this scenario, it was assumed that NPP 20422 emissions. Nigeria to meet its future electricity demand and was a solution to reduce CO2emissions. Figure6shows the energy mix required from all power plants to meet the future demand. 2040Figure 6 shows the energy mix required from al plant (GWh) It clearly demonstrates that oil and gas contributions to electricity production were limited, 2038 and nuclear energy contributed the highest percentage to electricity generation. This is attributed to the efforts made to reduce CO 2036 2emissions, as assumed in this scenario. 2 emissions, as assumed in this scenario. \\mathrm{C O\_{2}} \\mathrm{C O\_{2}} Figure 6. Figure 6. Increase in electricity production and demand from all power plants with limit on CO Increase in electricity production and dema 2 emission using MESSAGE emission using MESSAGE nd from all power plants with limit on CO 2 code for the modeling period under study. code for the modeling period under study. Table 7 comparison between the contributions Table7comparison between the contributions of each power plant in both scenarios. In the first scenario, the contribution of oil power plant is 81% in 2016 and increased until In the first scenario, the contribution of oil power plant is 81% in 2016 and increased until 100%, whereas in the second scenario, the contribution of oil power plant is 81% and 100%, whereas in the second scenario, the contribution of oil power plant is 81% and decreased to 32%. The contribution of gas power plant in the first scenario is 0.0% in 2016 and increased to 97% to replace the contribution of oil, whereas in the second scenario, the gas power plant contributes 57% in 2025 and decreased to 17% in 2059. The Nuclear power plant has not contribution in the first scenario, whereas in the second scenario, the nuclear power plant substitutes the oil and gas production. The contribution of nuclear power plant started in 2030 with 11% and increased to 81.5% in 2059. | | Without CO2 Constraint(Scenario 1) | | | | | | --- | --- | --- | --- | --- | --- | | Year | Gas-PP | Oil-PP | Nuc-PP | Solar-PP | Wind-PP | | 2016 | 0.00 | 81.11 | 0.00 | 0.34 | 1.09 | | 2020 | 0.00 | 84.77 | 0.00 | 0.27 | 0.88 | | 2025 | 0.00 | 88.51 | 0.00 | 0.21 | 0.52 | | 2030 | 0.00 | 91.40 | 0.00 | 0.16 | 0.21 | | 2035 | 0.00 | 93.59 | 0.00 | 0.12 | 0.00 | | 2040 | 0.00 | 95.20 | 0.00 | 0.00 | 0.00 | | 2045 | 0.00 | 100.0 | 0.00 | 0.00 | 0.00 | | 2050 | 97.19 | 0.00 | 0.00 | 0.00 | 0.00 | | 2055 | 97.86 | 0.00 | 0.00 | 0.00 | 0.00 | | 2059 | 98.27 | 0.00 | 0.00 | 0.00 | 0.00 | | With CO2 Constraint(Scenario 2) | | | | | | | | --- | --- | --- | --- | --- | --- | --- | | Hyd-pp | Gas-PP | Oil-PP | Nuc-PP | Solar-PP | Wind-PP | Hyd-pp | | 17.46 | 0.00 | 81.11 | 0.00 | 0.34 | 1.09 | 17.46 | | 14.08 | 0.00 | 91.12 | 0.00 | 0.27 | 0.88 | 7.73 | | 10.76 | 56.63 | 31.89 | 0.00 | 0.21 | 0.52 | 10.76 | | 8.23 | 79.83 | 0.00 | 10.96 | 0.16 | 0.21 | 8.84 | | 6.29 | 61.01 | 0.00 | 32.59 | 0.12 | 0.00 | 6.29 | | 4.80 | 46.62 | 0.00 | 48.57 | 0.00 | 0.00 | 4.80 | | 0.00 | 35.63 | 0.00 | 60.70 | 0.00 | 0.00 | 3.67 | | 2.81 | 27.23 | 0.00 | 69.96 | 0.00 | 0.00 | 2.81 | | 2.14 | 20.81 | 0.00 | 77.05 | 0.00 | 0.00 | 2.14 | | 1.73 | 16.78 | 0.00 | 81.49 | 0.00 | 0.00 | 1.73 | \ \\mathbf{c o\_{2}} The environmental impacts of the proposed Geregu NPP in Nigeria during normal operation were estimated using SIMPACTS software. Figure7a shows 0.014, 0.033, and 0.003 cases of fatal cancer, nonfatal cancer, and specific hereditary effects, respectively, \\mathrm{C O\_{2}} \\mathbf{c O\_{2}} * * * _Energies_ , 14, x FOR PEER REVIEW 15 of 18 power plant has not contribution in the first scenario, whereas in the second scenario, the nuclear power plant substitute s the oil and gas production. The contribution of nuclear power plant started in 2030 with 11% and increased to 81.5% in 2059. The environmental impacts of the proposed Geregu NPP in Nigeria during normal operation were estimated using SIMPACTS software. Figure 7a shows 0.014, 0.033, and 0.003 cases of fatal cancer, nonfatal cancer, and specific hereditary effects, respectively, because of the normal operation of the proposed Geregu NPP. This implies that the number of health impact cases from the NPP is lower than that from fossil fuel plants. Figure 7b shows the annual damage costs of $4.731, $12.360, and $30.968 for fatal cancer, nonfatal cancer, and specific hereditary effect s, respectively. The annual damage cost is the cost caused by health impacts resulting from radionuclide emissions per year during the normal operation of the proposed Geregu NPP. This implies that the damage cost of fossil power plants is higher than that of NPPs. **Table 7.** Percentage (%) contribution of electricity constraint. from each power plant with and without CO ## Without CO Constraint (Scenario 1) With CO2 Constraint (Scenario 2) **Year** **Gas-PP Oil-PP Nuc-PP So lar-PP Wind-PP Hyd-pp Gas-PP Oil-PP Nuc-PP Solar-PP Wind-PP Hyd-pp** _Energies_ 2016 0.00 81.11 0.00 **2021, 14, 6258** 0.34 1.09 17.46 0.00 81.11 0.00 0.34 1.09 17.4614 of 17 2020 0.00 84.77 0.00 0.27 0.88 14.08 0.00 91.12 0.00 0.27 0.88 7.73 2025 0.00 88.51 0.00 0.21 0.52 10.76 56.63 31.89 0.00 0.21 0.52 10.76 2030 0.00 91.40 0.00 0.16 0.21 8.23 79.83 0.00 10.96 0.16 0.21 8.84 2035 0.00 93.59 0.00 0.12 0.00 6.29 61.01 0.00 32.59 0.12 0.00 6.29 2040 0.00 95.20 0.00 0.00 0.00 4.80 46.62 0.00 48.57 0.00 0.00 4.80 2045 0.00 100.0 0.00 0.00 0.00 0.00 35.63 0.00 60.70 0.00 0.00 3.67 because of the normal operation of the proposed Geregu NPP. This implies that the number 2050 97.19 0.00 0.00 0.00 0.00 2.81 27.23 0.00 69.96 0.00 0.00 2.81 2055 97.86 0.00 0.00 0.00 0.00 2.14 20.81 0.00 77.05 0.00 0.00 2.14 2059 98.27 0.00 0.00 0.00 0.00 1.73 16.78 0.00 81.49 0.00 0.00 1.73 of health impact cases from the NPP is lower than that from fossil fuel plants. Figure7b shows the annual damage costs of $4.731, $12.360, and $30.968 for fatal cancer, nonfatal cancer, and specific hereditary effects, respectively. The annual damage cost is the cost caused by health impacts resulting from radionuclide emissions per year during the normal operation of the proposed Geregu NPP. This implies that the damage cost of fossil power plants is higher than that of NPPs. 0.052 30.968 (Cases/year) 25.030.035.0 0.0400.0500.060 12.360 20.0 0.022 0.030 10.015.0 Health Effects 4.731 0.0100.020 0.004 5.0 0.0 0.000 Damage Costs (US$/year) ( a ) ( **b**) Fatal cancer Non-fatal hereditary **Figure 7.** **Figure 7. Health impacts and damage costs of the proposed Geregu NPP in Nigeria. (** Health impacts and damage costs of th Fatal cancer Non-fatal **a) SIMPACT results showing various** e proposed Geregu NPP in Nigeria. ( hereditary cancer **a ) SIMPACT results showing various** health effects from the released of radioactive materials during normal operation per year. (b) SIMPACT results showing cancer health effects from the released of radioactiv e materials during normal operation per year. ( **b**) SIMPACT results showing damage cost resulting from radioactive materials released during normal operation per year. damage cost resulting from radioactive material s released during normal operation per year. ## 4\. Conclusions ## 4\. Conclusions Nuclear energy is regarded as one of the cleanest sources of energy due to its low Nuclear energy is regarded as one of the cleanest sources of energy due to its low greenhouse gas emissions, baseload power with a consistent and massive output. Unlike greenhouse gas emissions, baseload power with a consistent and massive output. Unlike other low carbon renewable energies such as solar, wind, and hydro, nuclear is regarded other low carbon renewable energies such as solar, wind, and hydro, nuclear is regarded as a reliable source of energy. However, following the Chernobyl disaster in 1986, the as a reliable source of energy. However, following the Chernobyl disaster in 1986, the radiophobia regarding the use of nuclear power plant increased and many countries radiophobia regarding the use of nuclear power plant increased and many countries implemented reactor shutdowns, while others se e nuclear energy as a valuable alternative implemented reactor shutdowns, while others see nuclear energy as a valuable alternative to meet their energy needs. The public perception is very important in acceptance of any nuclear program. To achieve this, public awareness in terms of environmental impacts and economic benefits compared to other electricity generation technologies such as fossil and hydro power plants is key. This study introduces the nuclear power plant as a best choice for Nigeria electricity generation technology not only to meet the final electricity demand in Nigeria’s future energy mix but also to protect the environment against the effect of climate change by reducing carbon emission. Previous literature discussed the nuclear power plant from the viewpoint of optimal energy mix in Nigeria to meet its future demand. In this study, we evaluated the best choice of technology that can be used to generate electricity based on two criteria, cost and environmental impact. The MESSAGE code was employed to model two case scenarios of the energy supply system in Nigeria to find appropriate alternative power technologies that can be applied to meet long time energy need therefore reducing CO2 emissions. The first scenario was modeled with no CO2emission restrictions, whereas in the second scenario, CO2emission restrictions were assumed. The SIMPACTS code was used to estimate the environmental effects and damage costs resulting from normal operation of electricity generation technolo- gies in Nigeria. The electricity generation technologies modeled in SIMPACTS included the following: the Jebba hydropower plant, the Egbin fossil fuel power plant, and the proposed Geregu NPP. The results from MESSAGE showed that oil and gas power plant technologies are the optimal sources of electricity supply with no limits on CO2 emissions. Currently, wind, solar, and hydro technologies contribute the lowest percentages to Nigeria’s energy * * * _Energies 2021, 14, 6258_ 15 of 17 mix. Gas technology is currently the most predominant source of electricity in Nigeria. With limits placed on CO2 emission in line with the Paris agreement of 2015, gas and nuclear technologies are the optimized electricity-generating technologies contributing to Nigeria’s energy mix in the future due to its growing population. Result from SIMPACTS computer code shows that NPPs have the lowest total damage costs, whereas fossil fuel power plants have the highest total damage costs. This study concluded that an NPP is the most environmentally friendly technology, given the fact that it has the lowest environmen- tal impacts during normal operation and the lowest damage costs. Conversely, fossil fuel technologies impose a high burden on the environment in terms of damage cost and health impacts. Based on these results, nuclear power is the best choice for electricity generation technology to achieve the final energy needs in Nigeria’s future energy mix. The constant growth rate used in this study was based on the rapid population growth in the country. The major drawbacks of various electricity generation technologies such as nuclear energy are mainly nuclear accidents and management of radioactive waste from the operation of nuclear power plants. The release of radioactive materials during nuclear accidents has resulted in adverse effects including death to the members of the public which increases the radiophobia of nuclear energy. Additionally, the management of radioactive waste arising from the operation of nuclear power plants constitutes long time effects on the sustainability of the environment due to limited radioactive waste management disposal facilities and its associated longer decay period. According to the US energy information 2019, fossil, hydro, wind, and solar cannot meet future electricity demand due to low generation capacity of 56.8%, 39.1%, 34.8%, and 24.5% respectively, whereas nuclear has a maximum generation capacity of 93.5% which is nearly double that of renewable sources and three times that of wind and solar. Fossil fuel is the major source of carbon emission and becomes depleted over time. A hydropower plant has a greater consequence on the environment and agricultural products due to flooding from the reservoir which results displacement of homes, loss of life, and damage to farmland and agricultural products. Wind is inconsistent and can blow at various speeds, therefore, it is hard to predict the amount of energy it can produce. Solar energy cannot produce electricity during the night and when there is no sun, power has to be stored in a form of battery to ensure consistent supply. Presently in Nigeria, fossil power plants accounts for 83% contribution of electricity sources, hydropower accounts for 16%, while wind and solar energy accounts for 1%, respectively. Conclusively, nuclear energy is recommended to be part of Nigeria’s energy mix to reduce the CO2 emissions since other low carbon emission electricity generation technologies such as solar, wind and hydro does not have the capacity to attain the future electricity needs in Nigeria due to high population growth rate. Findings from this study will serve as a reference point for modeling long-term energy mix and reducing CO2emission in Nigeria. **Author Contributions: Conceptualization, A.A.-H., S.R.J., H.H.R. and M.N.; methodology, A.A.-H.** and S.R.J.; software, J.K.; investigation, J.-H.H., M.N. and H.H.R.; resources, J.K., S.R.J. and J.-H.H.; data curation, M.N. and H.H.R.; writing—original draft preparation, A.A.-H.; writing—review and editing, J.K., A.A.-H., S.R.J., M.N. and H.H.R.; supervision, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. **Funding: This research was funded by the 2021 Research Fund of the KEPCO International Nuclear** Graduate School (KINGS), the Republic of Korea. This work was also supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2003015). **Institutional Review Board Statement: Not applicable.** **Informed Consent Statement: Not applicable.** **Data Availability Statement: The data that back up the findings of this study can be obtained from** the corresponding author upon request. * * * _Energies 2021, 14, 6258_ 16 of 17 **Conflicts of Interest: There are no conflict of interest declared by the authors.** **References** 1.Ministry of Energy. Country Report on Nigeria Energy System; Ministry of Energy: Asaba, Nigeria, 2015; pp. 1–34. 2\. Statista. Nigeria: Gross Domestic Product (GDP) in Current Prices from 1996 to 2026. 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