An Application of Artificial Intelligence as a Tool for Climate Change: A Review

References

[1] ADB. 2022. 225 Million Displacements in Asia and Pacific Due to Disasters, As Impact of Climate Change Deepens, Says New ADB Report. Asian Development Bank.

[2] Abdalla AN, Nazir MS, Tao H, Cao S, Ji R, Jiang M, Yao L (2021) Integration of energy storage systems and renewable energy sources based on artificial intelligence: an overview. J Energy Stor 40:102811. https://doi.org/10.1016/j.est.2021.102811

[3] Abduljabbar R, Dia H, Liyanage S, Bagloee SA (2019) Applications of artificial intelligence in transport: an overview. Sustainability 11:189.

[4] Adikari KE, Shrestha S, Ratnayake DT, Budhathoki A, Mohanasundaram S, Dailey MN (2021) Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environ Model Softw 144:105136. https://doi.org/10.1016/j.envsoft.2021.105136

[5] Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2020) Compu tation of evapotranspiration with artificial intelligence for pre cision water resource management. Appl Sci 10:1621. https:// doi.org/10.3390/app10051621

[6] Ahmad T, Zhang D, Huang C, Zhang H, Dai N, Song Y, Chen H (2021) Artificial intelligence in sustainable energy indus try: status Quo, challenges and opportunities. J Clean Prod 289:125834.

[7] Ahmad T, Zhu H, Zhang D, Tariq R, Bassam A, Ullah F, AlGhamdi AS, Alshamrani SS (2022) Energetics systems and artificial intelligence: applications of industry 4.0. Energy Rep 8:334-361. https://doi.org/10.1016/j.egyr.2021.11.256

[8] Ahmed QW, Garg S, Rai A, Ramachandran M, Jhanjhi NZ, Masud M, Baz M (2022a) AI based resource allocation techniques in wireless sensor internet of things networks in energy efficiency with data optimization. Electronics 11:2071. https:// doi. org/ 10. 3390/electronic s11132071

[9] Ahmed S, Alshater MM, Ammari AE, Hammami H (2022b) Artifi cial intelligence and machine learning in finance: a bibliomet ric review. Res Int Bus Financ 61:101646. https:// doi. org/ 10. 1016/j.ribaf.2022.101646

[10] Alassery F, Alzahrani A, Khan AI, Irshad K, Islam S (2022) An artificial intelligence based solar radiation prophesy model for green energy utilization in energy management system. Sustain Energy Technol Assess 52:102060. https:// doi. org/ 10. 1016/j. seta.2022.102060

[11] AlDousari AE, Kafy AA, Saha M, Fattah MA, Almulhim AI, Faisal A A, Al Rakib A, Jahir DMA, Rahaman ZA, Bakshi A, Shah rier M, Rahman MM (2022) Modelling the impacts of land use/ land cover changing pattern on urban thermal characteristics in Kuwait. Sustain Cities Soc 86:104107. https:// doi. org/ 10. 1016/j.scs.2022.104107

[12] Alem A, Kumar S (2022) Transfer learning models for land cover and land use classification in remote sensing image. Appl Artif Intell 36:2014192. https:// doi. org/ 10. 1080/ 08839 514. 2021. 2014192

[13] Alexandru M, Dragoș C, Bălă Constantin Z (2022) Digital Twin for automated guided vehicles fleet management. Proc Comput Sci 199:1363–1369.

[14] Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213. https:// doi. org/ 10.1016/j.atmosenv.2018.07.058

[15] Allam Z, Dhunny ZA (2019) On big data, artificial intelligence and smart cities. Cities 89:80–91. https:// doi. org/ 10. 1016/j. cities. 2019.01.032

[16] Zhang Jian, Hu Xian and Fang Cheng (2024): AI and Climate Crises, Institute of climate change and Sustainable Development, Tsinghua University

[17] BA, Babanazarov NS, et al. 2025. Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output, E3S Web of Conferences, 623, p. 04001.

[18] Nayyef, Z.T., Abdulrahman, M.M. and Kurdi, N.Al. (2024) ‘Optimizing Energy Efficiency in Smart Grids Using Machine Learning Algorithms: A Case Study in Electrical Engineering’, SHIFRA, 2024, pp. 46–54.

[19] J., Lee, J. and Zhou, R. (2023) ‘Review of big-data and AI application in typhoon-related disaster risk early warning in Typhoon Committee region’, Tropical Cyclone Research and Review, 12(4), pp. 341–353.

[20] IEA (2025) Energy and AI – Analysis - IEA. Available at: https://www.iea.org/reports/energy-and-ai. IMF (2022) Poor and Vulnerable Countries Need Support to Adapt to Climate Change, IMF.

[21] Hasan, R. et al. (2025) ‘AI-Driven greenhouse gas monitoring: enhancing accuracy, efficiency, and real-time emissions tracking’, AIMS Environmental Science, 12(3), pp. 495–525.

[22] Baltazar, R., et al. 2024. The Role of Artificial Intelligence in Disaster Prediction, Mitigation, and Response in the Philippines: Challenges and Opportunities. International Journal of Artificial Intelligence, 11(1), 37–51.

[23] GPAI (2021) Climate Change and AI: Recommendations for Governments, Climate Change AI.

[24] Canpolat, E. and Rahim, A. 2024. AI and climate: Tackling challenges and embracing change with a people-centered approach. World Bank Blogs, 05 March.

[25] Correia, J. et al. 2025. The promise and perils of AI for the Asia Pacific’s green transition. East Asia Forum, 24 April.

[26] Fu, X. (2025) AI-empowered Digital Twin for Smart Building Management | Knowledge Transfer and Entrepreneurship Office

@article{1717140,
      author = {Lesor Ikeh},
      title = {An Application of Artificial Intelligence as a Tool for Climate Change: A Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3997-4001},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717140.pdf},
      abstract = {Climate change is a significant threat, already damaging urban and natural systems and causing global economic losses exceeding $500 billion. These problems could be partly addressed with artificial intelligence, which leverages internet resources to offer quick suggestions based on accurate climate predictions. In this study, the applications of AI in reducing the negative effects of climate change, focusing on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and building resilient cities was reviewed. It is observed that improving energy efficiency can greatly reduce climate change impacts. Smart manufacturing can cut energy use, waste, and carbon emissions by 30–50%, especially in buildings. About 80% of the global natural gas industry uses AI to improve the accuracy and reliability of weather forecasts. Combining smart grids with AI can optimize power system efficiency, leading to a 5–10% reduction in electricity bills. Intelligent transportation systems can lower CO₂ emissions by around 70%. Consequently, managing natural resources and designing resilient cities with AI can further support sustainability.},
      keywords = {Climate Change, Environment, Energy, Artificial Intelligence, Emissions, Greenhouse.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1717140}
  }