Pathways for Renewable Energy and Clean Technology Transitions under Climate Constraints

Rose Uju Obianyo¹, Charles Chikwendu Okpala², Chukwudi Emeka Udu³

¹ Lecturer, Department of Software Engineering, Shanahan University, Onitsha, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

³ Researcher, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.   

Abstract

The ability to attain rapid and sustained reductions in greenhouse gas emissions that are consistent with global climate targets requires fundamental transformations of energy systems that are supported by robust, evidence-based decision-making. This study develops and applies a data-driven, multidisciplinary framework to identify renewable energy and clean technology transition pathways under explicit climate constraints. The framework integrates high-resolution empirical datasets, machine learning-based forecasting, climate-aligned energy system optimization, and multidimensional sustainability assessment to bridge the gap between long-term climate goals and near to medium-term operational planning. Multiple scenarios which were evaluated include business-as-usual, moderate transition, and accelerated net-zero pathways that are consistent with 2°C and 1.5°C carbon budgets. The obtained results indicated that coordinated deployment of renewable generation, energy storage, electrification, and system flexibility can reduce energy-related emissions by approximately 55–75% by 2040 relative to baseline trajectories, while lowering average system costs by 10–20% and improving reliability indicators such as loss-of-load probability. Beyond emissions mitigation, accelerated transition pathways generate substantial sustainability co-benefits, including reduced fossil fuel dependence, enhanced energy security, improved air quality, and net employment gains. Through explicit enforcement of cumulative carbon budgets and embedding data-driven analytics within system-level planning, the study demonstrates that ambitious climate-aligned energy transitions are not only feasible, but also economically and operationally advantageous. The proposed framework is scalable and replicable across national and regional contexts, as it offers a robust decision-support tool for policymakers, system planners, and investors who aim to accelerate sustainable energy transitions under climate constraints.     

Keywords: Renewable energy transitions, Clean technologies, Data-driven energy modeling, Climate constraints, Sustainability co-benefits, Energy system optimization, Net-zero pathways

References

1. Ajaefobi, J.O. and Okpala, C.C. (2026) ‘Optimization of smart manufacturing systems through renewable energy integration and sustainable material utilization’, International Journal of Engineering Inventions, 15(2), pp. 18–32. https://www.ijeijournal.com/papers/Vol15-Issue2/15021832.pdf
2. Bistline, J.E., Buddemeier, R.W., Kahn, E., et al. (2023) ‘Energy system modeling for deep decarbonization: Advances, challenges, and research needs’, Energy Policy, 172, 113311. https://doi.org/10.1016/j.enpol.2022.113311
3. Chukwunedum, O.C., Okpala, C.C. and Udu, C.E. (2026) ‘A data-driven integration of total productive maintenance and Industry 4.0 technologies: A machine learning framework for predictive OEE optimization’, International Journal of Engineering Research and Development, 22(3), pp. 85–95. https://www.ijerd.com/paper/vol22-issue3/22038595.pdf
4. Chukwumuanya, E.O., Okpala, C.C. and Udu, C.E. (2025) ‘Carbon accounting at the shop-floor: The integration of real-time energy monitoring, process modeling, and LCA for net-zero targets’, Jurnal Teknik Indonesia, 4(1), pp. 28-41. https://jurnal.seaninstitute.or.id/index.php/juti/article/view/728
5. Creutzig, F., Agoston, P., Goldschmidt, J.C., et al. (2022) ‘Demand-side solutions to climate change mitigation’, Nature Climate Change, 12(1), pp. 37–46. https://doi.org/10.1038/s41558-021-01219-y
6. Davis, S.J., Lewis, N.S., Shaner, M., et al. (2018) ‘Net-zero emissions energy systems’, Science, 360(6396), eaas9793. https://doi.org/10.1126/science.aas9793
7. Deswal, S., Pal, M., Bhardwaj, P., et al. (2026) ‘Traffic Noise Modelling using Integrated Conformal Prediction Based Uncertainty Estimation with Machine Learning Algorithms’, International Journal of Technology, Health and Sustainability, 2(2), pp. 465-485. https://ijths.com/wp-content/uploads/IJTHS-0202005.pdf
8. Deswal, S. and Deswal, P. (2025) ‘Sustainability: Greenhouse Gas Protocol and global GHG emissions’ status and trends’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), pp. 2051-2063. https://doi.org/10.54660/.IJMRGE.2025.6.1.2051-2063
9. Deswal, S. and Pal, M. (2025) ‘Uncertainty estimation in predicting oxygenation by plunging jet aerators using probabilistic machine learning and conformal prediction’, International Journal of Technology, Health and Sustainability, 1(2), pp. 83-93. https://ijths.com/wp-content/uploads/2025/12/IJTHS-010230.pdf
10. Deswal, P. (2025) ‘Article 6 of the Paris Agreement: A comprehensive review of mechanisms, progress, and persistent challenges’, International Journal of Technology, Health and Sustainability, 1(2), pp. 111-125. https://ijths.com/wp-content/uploads/IJTHS-010235.pdf
11. Edeh, M.O., Ugwuja, N.E., Okpala, C.C., et al. (2024) ‘The role of emerging technologies in academic career development’, Babcock University Journal of Education, 10(1), pp. 15-31. https://acrobat.adobe.com/id/urn:aaid:sc:EU:821b33a1-8d83-4688-a774-dfa390db1c8b
12. Farmer, J.D., Lafond, F., Klepper, S., et al. (2019) ‘Sensitivity of the world energy system to technology learning’, Joule, 3(11), pp. 1–19. https://doi.org/10.1016/j.joule.2019.09.012
13. Grubler, A., Wilson, C., Bento, N., et al. (2018) ‘A low energy demand scenario for meeting the 1.5 °C target’, Nature Energy, 3(6), pp. 515–527. https://doi.org/10.1038/s41560-018-0172-6
14. Igbokwe, N.C., Okpala, C.C. and Nwankwo, C.O. (2024) ‘Industry 4.0 implementation: A paradigm shift in manufacturing’, Journal of Inventive Engineering and Technology, 6(1), pp. 20-26. https://jiengtech.com/index.php/INDEX/article/view/113/135
15. IPCC (2023) AR6 synthesis report: Climate change 2023. Intergovernmental Panel on Climate Change.
16. IEA (2023) World energy outlook 2023. International Energy Agency.
17. Jacobsson, S. and Karltorp, K. (2013) ‘Mechanisms blocking the dynamics of the European offshore wind energy innovation system’, Energy Policy, 63, pp. 1182–1195. https://doi.org/10.1016/j.enpol.2013.08.077
18. Kriegler, E., Luderer, G., Bauer, N., et al. (2022) ‘Pathways to Paris: Long-term mitigation pathways and the role of carbon budgets’, Energy Strategy Reviews, 40, 100843. https://doi.org/10.1016/j.esr.2022.100843
19. Lazard (2024) Levelized cost of energy analysis (Version 17.0). Lazard Ltd.
20. Markandya, A., Sampedro, J., Smith, S.J., et al. (2018) ‘Health co-benefits from air pollution and mitigation costs of the Paris Agreement’, Nature Communications, 9, 4939. https://doi.org/10.1038/s41467-018-05198-5
21. Nwamekwe, C.O., Ewuzie, N.V., Igbokwe, N.C., et al. (2026) ‘Evaluating the performances of various machine learning models for accurate production forecasting in the textile industry’, International Journal of Technology, Health and Sustainability, 2(1), pp. 138–148. https://ijths.com/wp-content/uploads/IJTHS-020163.pdf
22. Okpala, C.C. (2026) ‘Machine learning–enabled design of composite materials: Scalable structure–processing–property relationships across applications’, International Journal of Technology, Health and Sustainability, 2(1), pp. 154–161. https://ijths.com/wp-content/uploads/IJTHS-020166.pdf
23. Okpala, C.C. and Onukwuli, S.K. (2026) ‘Emerging technologies for global sustainability: A multidisciplinary review of trends, impacts, and future research directions’, International Journal of Engineering Inventions, 15(2), pp. 44–53. https://www.ijeijournal.com/papers/Vol15-Issue2/15024453.pdf
24. Pfenninger, S., Hawkes, A. and Keirstead, J. (2014) ‘Energy systems modeling for twenty-first century energy challenges’, Renewable and Sustainable Energy Reviews, 33, pp. 74–86. https://doi.org/10.1016/j.rser.2014.02.003
25. Pfenninger, S. and Keirstead, J. (2015) ‘Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions, and energy security’, Applied Energy, 152, pp. 83–93. https://doi.org/10.1016/j.apenergy.2015.04.102
26. Rogelj, J., Shindell, D., Jiang, K., et al. (2018) ‘Mitigation pathways compatible with 1.5°C in the context of sustainable development’. In: Global warming of 1.5°C; Intergovernmental Panel on Climate Change.
27. Sovacool, B.K., Hook, A., Martiskainen, M., et al. (2020) ‘The whole systems energy injustice of four European low-carbon transitions’, Global Environmental Change, 58, 101958. https://doi.org/10.1016/j.gloenvcha.2019.101958
28. Sun, Y., Tian, Y. and Lee, T. (2020) ‘Data-driven methods for renewable energy forecasting: A review’, Renewable and Sustainable Energy Reviews, 125, 109797. https://doi.org/10.1016/j.rser.2020.109797
29. Udu, C.E., Ejichukwu, E.O. and Okpala, C.C. (2025) ‘The application of digital tools for supply chain optimization’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(3), pp. 308-316. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250508172828_MGE-2025-3-047.1.pdf
30. Udu, C.E. and Okpala, C.C. (2026) ‘Artificial intelligence-enabled resilient scheduling: A systematic review and research roadmap for digital twin and machine learning in disruption-aware operations’, International Journal of Technology, Health and Sustainability, 2(2), pp. 486–497. https://ijths.com/wp-content/uploads/IJTHS-0202014.pdf
31. UN (2023) Global stocktake synthesis report. UNFCCC.
32. van Sluisveld, M.A.E., Hof, A.F. and van Vuuren, D.P. (2023) ‘The role of near-term action in long-term climate mitigation pathways’, Climatic Change, 176, 23. https://doi.org/10.1007/s10584-023-03473-0
33. Voyant, C., Notton, G., Kalogirou, S., et al. (2017) ‘Machine learning methods for solar radiation forecasting: A review’, Renewable Energy, 105, pp. 569–582. https://doi.org/10.1016/j.renene.2016.12.095
34. Wilson, C., Grubler, A., Gallagher, K.S., et al. (2021) ‘Marginalization of end-use technologies in energy innovation for climate protection’, Nature Climate Change, 11(9), pp. 780–788. https://doi.org/10.1038/s41558-021-01166-8