Integrated Assessment Modelling of Energy Transition Pathways for the Nigerian Economy


Authors : John, M.P U.; Nwaozuzu, C.; Nteegah, A.

Volume/Issue : Volume 10 - 2025, Issue 2 - February


Google Scholar : https://tinyurl.com/2wwmd6cs

Scribd : https://tinyurl.com/3s8rv2wr

DOI : https://doi.org/10.38124/ijisrt/25feb1375

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The worldwide push for shifting from fossil fuels to renewable energy sources has gained momentum due to concerns about greenhouse gas emissions and their detrimental effects on the environment. Nevertheless, since their discovery in 1958, fossil fuels have played a crucial role in Nigeria's economy, generating substantial revenue and foreign exchange. Any efforts to transition should be grounded in policy frameworks that take into account Nigeria's strengths, weaknesses, opportunities, and potential challenges. Numerous attempts have been made to create decarbonisation models for Nigeria, each varying in sector grouping, system components, modelling approaches, and pathways. The future evolution of the energy system is challenging to forecast due to multiple variables, including technological advancements, policy changes, socioeconomic factors, financial considerations, and geopolitical influences. A comprehensive assessment model was developed using the pymedeas modelling framework, incorporating Nigerian socioeconomics, energy, climate, land use, water resources, minerals, and transportation systems. The economic model was built using Nigeria Input-Output Tables (IOT) and its Leontief Matrix covering 1995 – 2014. Simulated GDP was calibrated by historical GDP performance before using the model for prediction. The model was used to assess the impact of renewable Net-zero (NZP), non-renewable (Business-As-Usual (BAU)) pathways, and gas as a transition fuel on Nigeria's socioeconomic growth using Root Mean Square Deviation (RMSD). GDP growth for NZP was observed to be slow at -3% in the early years compared to an increase of about 2% in the BAU. It peaks up and outpaces BAU from 2038 onward. Nigeria should pursue a policy that allows for aggressive development of its gas resources as a transition fossil fuel, balanced by early and structured investment in centralised renewable energy infrastructures. Work provides complimentary approach to existing body of literature on Shared Socioeconomic Pathways (SSP). Pymedeas_ng can be used further to explore alternative pathways for decarbonisation of the Nigerian economy. Model modularity in terms of structure and functions means detailed investigation could be done by the user on a range of energy transition subjects.

References :

  1. Davidson, N. C. (2014). How much wetland has the world lost? Long-term and recent trends in global wetland area’; Marine and Freshwater Research, 2014, 65, 934-941.
  2. International Renewable Energy Agency (IRENA) (2024). Renewable energy statistics 2024. Accessed December 2024. Available at https://www.irena.org/Publications/2024/Jul/Renewable-energy-statistics-2024
  3. Environmental and Energy Study Institute (2020): “Fossil Fuels”, Washington, DC. www.eesi.org/
  4. Löffler, K., Burandt, T., Hainsch, K., & Oei, P. Y. (2019). Modeling the low-carbon transition of the European energy system-a quantitative assessment of the stranded assets problem. Energy Strategy Reviews26, 100422.
  5. Solé, J., Samsó, R., García-Ladona, E., Garcia-Olivares, A., Ballabrera-Poy, J., Madurell, T., ... & Theofilidi, M. (2020). Modelling the renewable transition: Scenarios and pathways for a decarbonized future using pymedeas, a new open-source energy systems model. Renewable and sustainable energy reviews132, 110105.
  6. Hafner, S., Anger-Kraavi, A., Monasterolo, I., & Jones, A. (2020). Emergence of new economics energy transition models: A review. Ecological Economics177, 106779.
  7. Capellán-Pérez et al. (2020). MEDEAS: A new modeling framework integrating global biophysical and socioeconomic constraints. Energy & environmental science13(3), 986 1017.
  8. OECD (2015): Final NAEC Synthesis. New Approaches to Economic Challenges. Available at: http://www.oecd.org/mcm/documents/Final-NAEC-Synthesis-Report-CMIN2015-2.pdf (05.04.2019).
  9. OECD (2017): New Approaches to Economic Challenges. Towards a new Narrative. Available at: http://www.oecd.org/naec/OSG%20NAEC%20Forum%20report.pdf (20.04.2019)
  10. Scrieciu, S., Rezai, A., & Mechler, R. (2013). On the economic foundations of green growth discourses: the case of climate change mitigation and macroeconomic dynamics in economic modeling. Wiley Interdisciplinary Reviews: Energy and Environment2(3), 251 268.
  11. Sterman, J., Fiddaman, T., Franck, T. R., Jones, A., McCauley, S., Rice, P., ... & Siegel, L. (2012). Climate interactive: the C-ROADS climate policy model.
  12. Meadows, D. H., Meadows, D. L., Randers, J., & Behrens III, W. W. (1972). The limits to growth club of Rome.
  13. Lavoie, J. M. (2014). Review on dry reforming of methane, a potentially more environmentally friendly approach to the increasing natural gas exploitation. Frontiers in chemistry2, 81.
  14. Taylor, J. A., Dhople, S. V., & Callaway, D. S. (2016). Power systems without fuel. Renewable and Sustainable Energy Reviews57, 1322-1336.
  15. Pollitt, H., Alexandri, E., Chewpreecha, U. and Klaassen, G. (2014) ‘Macroeconomic analysis of the employment impacts of future EU climate policies’, Climate Policy, DOI:10.1080/14693062.2014.953907
  16. Dowlatabadi, H. (1998). Sensitivity of climate change mitigation estimates to assumptions about technical change. Energy Economics20(5-6), 473-493.
  17. Kemfert, C., 2005. Induced technological change in a multi-regional, multi-sectoral, integrated assessment model (WIAGEM): Impact assessment of climate policy strategies. Ecol. Econ. 54, 293– 305. doi:10.1016/j.ecolecon.2004.12.031
  18. Kainuma, M., Matsuoka, Y., Hibino, G., Shimada, K., Ishii, H., Matsui, S., & Morita, T. (2003). Application of AIM/Enduse model to Japan. Climate Policy Assessment: Asia-Pacific Integrated Modeling, 155-176.
  19. Masui et al. (2011). An emission pathway for stabilization at 6 Wm− 2 radiative forcing. Climatic change109, 59-76.
  20. Morita, T., Jiang, K., Masui, T., Matsuoka, Y., & Rana, A. (2003). Long-term scenarios based on AIM model. Climate policy assessment: Asia-Pacific integrated modeling, 17-36.
  21. Alcamo, J., Leemans, R., & Kreileman, E. (1998). Global Change scenarios of the 21st century. Results from the IMAGE 2.1 model; 1.
  22. Bouwman, A. F., Kram, T., & Klein Goldewijk, K. (2006). Integrated modelling of global environmental change. An overview of IMAGE2(4), 225-228.
  23. Stehfest, E. et al (2014): Integrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications, Netherlands Environmental Assessment Agency (PBL), 2014.
  24. Bhattacharyya, S.C. (2011) Energy Economics: Concepts, Issues, Markets and Governance. 1st Edition, Springer, New York.
    https://doi.org/10.1007/978-0-85729-268-1
  25. Capellán Pérez, I. (2016). Development and application of environmental integrated assessment modelling towards sustainability. https://core.ac.uk/download/547385556.
  26. Christensen, P. P. (1989). Historical roots for ecological economics—biophysical versus allocative approaches. Ecological economics1(1), 17-36.
  27. Daly, H. E., & Farley, J. (2011). Ecological economics: principles and applications. Island press.
  28. Stern, D. I. (2012). Modeling international trends in energy efficiency. Energy Economics34(6), 2200-2208.
  29. De Haan, M. (2001). A structural decomposition analysis of pollution in the Netherlands. Economic Systems Research13(2), 181-196.
  30. James, D.E., Jansen, H.M.A., Opschoor, J.B., 1978. Economic Approaches to Environmental Problems. Elsevier North Holland, Amsterdam.
  31. Uehara, T. (2013). Ecological threshold and ecological economic threshold: Implications from an ecological economic model with adaptation. Ecological Economics93, 374-384.
  32. Scrieciu, S., Rezai, A., & Mechler, R. (2013). On the economic foundations of green growth discourses: the case of climate change mitigation and macroeconomic dynamics in economic modeling. Wiley Interdisciplinary Reviews: Energy and Environment2(3), 251 268.
  33. Leontief, W. (1974). Structure of the world economy: Outline of a simple input-output formulation. The American Economic Review64(6), 823-834.
  34. Miller, R. E., & Blair, P. D. (2009). Input-output analysis: foundations and extensions. Cambridge university press.
  35. Hardt, L., & O'Neill, D. W. (2017). Ecological macroeconomic models: assessing current developments. Ecological economics134, 198-211.
  36. Rezai, A., Stagl, S., 2016. Ecological macroeconomics: introduction and review. Ecol. Econ. 121, 181–185.
  37. De Castro, C., Mediavilla, M., Miguel, L. J., & Frechoso, F. (2013). Global solar electric potential: A review of their technical and sustainable limits. Renewable and Sustainable Energy Reviews28, 824-835.
  38. Diemuodeke et al. (2024). Hybrid Solar PV–Agro-Waste-Driven Combined Heat and Power Energy System as Feasible Energy Source for Schools in Sub-Saharan Africa. Biomass4(4), 1200-1218

The worldwide push for shifting from fossil fuels to renewable energy sources has gained momentum due to concerns about greenhouse gas emissions and their detrimental effects on the environment. Nevertheless, since their discovery in 1958, fossil fuels have played a crucial role in Nigeria's economy, generating substantial revenue and foreign exchange. Any efforts to transition should be grounded in policy frameworks that take into account Nigeria's strengths, weaknesses, opportunities, and potential challenges. Numerous attempts have been made to create decarbonisation models for Nigeria, each varying in sector grouping, system components, modelling approaches, and pathways. The future evolution of the energy system is challenging to forecast due to multiple variables, including technological advancements, policy changes, socioeconomic factors, financial considerations, and geopolitical influences. A comprehensive assessment model was developed using the pymedeas modelling framework, incorporating Nigerian socioeconomics, energy, climate, land use, water resources, minerals, and transportation systems. The economic model was built using Nigeria Input-Output Tables (IOT) and its Leontief Matrix covering 1995 – 2014. Simulated GDP was calibrated by historical GDP performance before using the model for prediction. The model was used to assess the impact of renewable Net-zero (NZP), non-renewable (Business-As-Usual (BAU)) pathways, and gas as a transition fuel on Nigeria's socioeconomic growth using Root Mean Square Deviation (RMSD). GDP growth for NZP was observed to be slow at -3% in the early years compared to an increase of about 2% in the BAU. It peaks up and outpaces BAU from 2038 onward. Nigeria should pursue a policy that allows for aggressive development of its gas resources as a transition fossil fuel, balanced by early and structured investment in centralised renewable energy infrastructures. Work provides complimentary approach to existing body of literature on Shared Socioeconomic Pathways (SSP). Pymedeas_ng can be used further to explore alternative pathways for decarbonisation of the Nigerian economy. Model modularity in terms of structure and functions means detailed investigation could be done by the user on a range of energy transition subjects.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe