Sustainable Energy and Artificial Intelligence

Futures of AI in Climate-Responsive Energy Systems: A Scenario-Based Approach

Document Type : Original Article

Authors

Mohammad Reza Fathi 1
Seyyed Reza Mirsaeid Ghazi 2

1 Associate Professor, College of Farabi, University of Tehran, Iran

2 M.Sc. Student in Information Technology Engineering, Noor Tuba Electronic Institute of Higher Education, Tehran, Iran

https://doi.org/10.66224/seai.2601-1039
Abstract
Climate change has significantly reshaped energy supply and demand by increasing the frequency and severity of extreme events, thereby intensifying challenges related to the sustainability and security of energy systems. In response, artificial intelligence has emerged as a critical enabling technology with the potential to improve forecasting accuracy, operational flexibility, and system resilience under non-stationary climatic conditions. Nevertheless, the future integration of AI into energy systems is subject to profound uncertainties arising from climatic dynamics, governance structures, and policy environments. This study investigates plausible futures of AI-enabled climate-responsive energy systems in Iran through a scenario-based approach. The research adopts an applied mixed-methods design. Initially, key drivers influencing the future development of climate-responsive energy systems were identified through a systematic literature review and expert interviews. Subsequently, Cross-Impact Analysis using the MICMAC method was employed to examine the influence–dependence relationships among these drivers. The analysis identified climate and energy security pressures and environmental and market-based policy stringency as the dominant exogenous drivers shaping system evolution. Based on the alternative states of these two critical uncertainties, four plausible future scenarios were constructed. The results indicate that under high climate pressure, strong and coherent governance combined with market-oriented environmental policies can convert stress into a driver of structural transformation and systematic AI deployment. Conversely, weak policy frameworks even when climate pressures are moderate lead to missed opportunities and the accumulation of long-term vulnerabilities. Overall, the study highlights that managing uncertainty through effective governance and strategic AI adoption is central to achieving resilient energy futures.

Keywords

Artificial intelligence
Climate change
Climate-responsive energy systems
Futures studies
Scenario analysis
Cross-Impact Analysis (MICMAC)
Energy security
Energy governance
Iran
Subjects
1. Adapa, V. R. K. (2024). AI for climate action: Leveraging artificial intelligence to address climate change challenges. International Journal for Multidisciplinary Research, 6(5), 1-8.
2. Aghlimoghadam, L. (2025). New-institutional explanations for energy transition in Iran: investigating the interplays between institutions, actors, and technology. Innovation: The European Journal of Social Science Research, 38(2), 934-960.
3. Alvarez, D. I., González-Ladrón-de-Guevara, F., Rojas Espinoza, J., Borge-Diez, D., Galindo, S. P., & Flores-Vázquez, C. (2025). The evolution of AI applications in the energy system transition: a bibliometric analysis of research development, the current state and future challenges. Energies, 18(6), 1523.
4. Apribowo, C. H. B., Hadi, S. P., & Wijaya, F. D. (2022, November). Integration of Battery Energy Storage System to Increase Flexibility and Penetration Renewable Energy in Indonesia: A Brief Review. In 2022 5th International Conference on Power Engineering and Renewable Energy (ICPERE) (Vol. 1, pp. 1-6). IEEE.
5. Arévalo‐Royo, J., Flor‐Montalvo, F. J., Latorre‐Biel, J. I., Jiménez‐Macías, E., Martínez‐Cámara, E., & Blanco‐Fernández, J. (2025). AI Guidelines for Sustainable Rural Development and Climate Resilience in Resource‐Constrained Regions. Sustainable Development, 33(6), 9385-9397.
6. Ayadi, R., Forouheshfar, Y., & Moghadas, O. (2025). Enhancing system resilience to climate change through artificial intelligence: a systematic literature review [Renforcer la résilience face au changement climatique grâce à l’intelligence artificielle: une revue systématique de la littérature]. HAL Post-Print, (hal-05268750).
7. Badekale, R. A., & Akinfaderin, A. (2025). AI-Driven Climate Policy Scenario Generation for Sub-Saharan Africa. arXiv preprint arXiv:2505.18694.
8. Banaeian, N., Zangeneh, M., & Clark, S. (2020). Trends and future directions in crop energy analyses: a focus on Iran. Sustainability, 12(23), 10002.
9. Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial intelligence alone will not democratise education: On educational inequality, techno-solutionism and inclusive tools. Sustainability, 16(2), 781.
10. Burgess, M. G., Van Boven, L., Wagner, G., Wong-Parodi, G., Baker, K., Boykoff, M., ... & Vandenbergh, M. P. (2024). Supply, demand and polarization challenges facing US climate policies. Nature Climate Change, 14(2), 134-142.
11. Chakraborty, D., Alam, A., Chaudhuri, S., Başağaoğlu, H., Sulbaran, T., & Langar, S. (2021). Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence. Applied energy, 291, 116807.
12. Chen, D., Lin, X., & Qiao, Y. (2025). Perspectives for artificial intelligence in sustainable energy systems. Energy, 318, 134711.
13. Danish, M. S. S. (2023). AI and expert insights for sustainable energy future. Energies, 16(8), 3309.
14. Danish, M. S. S., & Senjyu, T. (2023). AI-enabled energy policy for a sustainable future. Sustainability, 15(9), 7643.
15. Danish, M. S. S., & Senjyu, T. (2023). Shaping the future of sustainable energy through AI-enabled circular economy policies. Circular Economy, 2(2), 100040.
16. Dlamini, N., Senzanje, A., & Mabhaudhi, T. (2024). Modelling the water supply-demand relationship under climate change in the Buffalo River catchment, South Africa. PLOS Climate, 3(8), e0000464.
17. Fathi, M. R., Torabi, M., & Razi Moheb Saraj, S. (2026). The future of apitourism in Iran based on critical uncertainty approach and DEMATEL/COPRAS techniques. Journal of Tourism Futures, 12(1), 169-186.
18. Fathi, M. R., Maleki, M. H., Koksal, C. D., Yuzbaşıoğlu, N., & Ahmadi, V. (2019). Future study of spiritual tourism based on cross impact matrix and soft systems methodology. International journal of Tourism, Culture & Spirituality, 3(2), 19-41.
19. Heidary, B., Kiani, M. A., & Golzar, F. (2025). Toward sustainable development: energy transition scenarios for oil-dependent countries, with Iran as a case study. Energies, 18(10), 2651.
20. Jain, H., Dhupper, R., Shrivastava, A., Kumar, D., & Kumari, M. (2023). AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change. Computational Urban Science, 3(1), 25.
21. Jandaghi, G., Fathi, M. R., Maleki, M. H., Faraji, O., & Yüzbaşıoğlu, N. (2019). Identification of tourism scenarios in Turkey based on futures study approach. Almatourism-Journal of Tourism, Culture and Territorial Development, 10(20), 47-68.
22. Kaplun, V. (2023). Principles of resource-process modeling of territorial communities combined energy supply in the climate change prevention context. System Research in Energy, 4(75), 54-64.
23. Liu, L., He, G., Wu, M., Liu, G., Zhang, H., Chen, Y., ... & Li, S. (2023). Climate change impacts on planned supply–demand match in global wind and solar energy systems. Nature Energy, 8(8), 870-880.
24. Nabavi, S. A., Aslani, A., Zaidan, M. A., Zandi, M., Mohammadi, S., & Hossein Motlagh, N. (2020). Machine learning modeling for energy consumption of residential and commercial sectors. Energies, 13(19), 5171.
25. Olawade, D. B., Wada, O. Z., David-Olawade, A. C., Fapohunda, O., Ige, A. O., & Ling, J. (2024). Artificial intelligence potential for net zero sustainability: Current evidence and prospects. Next sustainability, 4, 100041.
26. Pimenow, S., Pimenowa, O., & Prus, P. (2024). Challenges of artificial intelligence development in the context of energy consumption and impact on climate change. Energies, 17(23), 5965.
27. Raymond, L., Gotham, D., McClain, W., Mukherjee, S., Nateghi, R., Preckel, P. V., ... & Wachs, E. (2020). Projected climate change impacts on indiana’s energy demand and supply. Climatic Change, 163(4), 1933-1947.
28. Razaghi, S., Ahmadvand, A. M., & Samadi-Foroushani, M. (2026). The dynamic model of Iran’s electrical energy supply system based on water-food-energy-climate change nexus. Kybernetes, 55(6), 2536-2570.
29. Razmjoo, A., Kaigutha, L. G., Rad, M. V., Marzband, M., Davarpanah, A., & Denai, M. J. R. E. (2021). A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area. Renewable energy, 164, 46-57.
30. Sitotaw Takele, G., Gebrie, G. S., & Engida, A. N. (2024). Water demand and supply under future water development and climate change scenarios in the upper Blue Nile basin. Water Supply, 24(12), 4094-4112.
31. Talha, M., Nejadhashemi, A. P., & Moller, K. (2025). Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review. Heliyon, 11(2).
32. Tasha, S. (2025). A Review of Artificial Intelligence Applications in Climate Change Mitigation. International Journal of Environment and Climate Change, 15(5), 7.
33. Torabi, M., Fathi, M. R., Raeesi Nafchi, S., & Sabalani, S. (2023). Futures Studies of Food Tourism based on Structural Analysis. International journal of Tourism, Culture & Spirituality, 6(2), 19-44.
34. Ukoba, K., Onisuru, O. R., Jen, T. C., Madyira, D. M., & Olatunji, K. O. (2025). Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome. Environmental Science and Pollution Research, 32(17), 10705-10724.
35. Wang, X. (2025). How does artificial intelligence accelerate the energy transition? Learning from empirical experience in OECD countries. Journal of Environmental Management, 391, 126397.
36. Yin, H. T., Wen, J., & Chang, C. P. (2023). Going green with artificial intelligence: The path of technological change towards the renewable energy transition. Oeconomia Copernicana, 14(4), 1059-1095.
37. Zhang, Z., Atia, A. A., Xu, P., Yetman, G., & Fthenakis, V. (2025). Water production by renewable energy powered desalination for meeting climate change induced water supply-demand deficits in the United States. Progress in Energy, 7(4), 045002.
38. Zhao, C., Dong, K., Wang, K., & Nepal, R. (2024). How does artificial intelligence promote renewable energy development? The role of climate finance. Energy Economics, 133, 107493.
Sustainable Energy and Artificial Intelligence
Volume 2, Issue 2
Spring 2026
Pages 63-80

  • Receive Date 08 January 2026
  • Revise Date 06 February 2026
  • Accept Date 09 February 2026
  • First Publish Date 15 February 2026

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