Energies 2021, 14, 6258. https://doi.org/10.3390/en14196258 www.mdpi.com/journal/energies Article 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 (A.A.-H.); soja.reuben@kings.ac.kr (S.R.J.); hilali.ramadhan@kings.ac.kr (H.H.R.); nmercy@email.kings.ac.kr (M.N.); cooper2710@kings.ac.kr (J.-H.H.) * Correspondence: jykim@kings.ac.kr; Tel.: +82-52-712-7306 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 CO2 emissions 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 CO2 emission. If Nigeria adopts CO2 emission restrictions to comply with the Paris Agreement’s target of decreasing world- wide mean temperature rise to 1.5 °C, the best option is nuclear power plants (NPPs). The MES- SAGE 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. Keywords: energy modeling; MESSAGE; SIMPACTS; CO2 emission; Nigeria energy; environmental impact 1. Introduction Known as the “Giant of Africa”, Nigeria is located on Africa’s west coast, with total size of 923,766 km 2 comprising of 910,768 square kilometer land mass and 13,000 square kilometer water bodies [1]. As of 2021, the current population is 211,400,708 (a 2.55% in- crease 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. 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/ en14196258 Academic Editors: David Borge-Diez and Nicu Bizon Received: 22 August 2021 Accepted: 28 September 2021 Published: 1 October 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).