Abstract
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, with applications in healthcare, finance, education, transportation, agriculture, energy and environmental management. However, the rapid development of increasingly large and computationally intensive AI models has created significant environmental concerns, including high electricity consumption, carbon emissions, water consumption, electronic waste and demand for energy-intensive computing infrastructure. Green Artificial Intelligence (Green AI) has therefore emerged as an important approach towards reducing the environmental footprint of AI while maintaining useful levels of performance. Green AI focuses on developing energy-efficient algorithms, optimising models and hardware, reducing computational requirements, using renewable energy and improving the sustainability of data centres. At the same time, AI can contribute to environmental sustainability by supporting renewable energy forecasting, climate modelling, biodiversity monitoring, smart agriculture and efficient resource management. This paper presents a conceptual study of Green AI and examines its relationship with environmental sustainability through a review of existing literature. The study adopts a descriptive and conceptual research methodology based on secondary sources, including academic journal articles, research papers and reports from international organisations. The findings indicate that Green AI requires consideration of the entire AI lifecycle, from data collection and model training to deployment, infrastructure and disposal of hardware. The study recommends greater energy-efficiency measurement, transparent environmental reporting, renewable-energy integration, efficient model design and stronger collaboration between technology companies, researchers and policymakers.
environmental
References
1. Chowdhury, I. K., et al. (2024). Towards sustainable AI: A comprehensive framework for Green AI. Discover Sustainability. https://doi.org/10.1007/s43621-024-00641-4 2. International Energy Agency. (2025). Energy and AI. Paris: IEA. 3. International Energy Agency. (2026). Key Questions on Energy and AI. Paris: IEA. 4. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. 5. Verdecchia, R., Sallou, J., & Cruz, L. (2023). A systematic review of Green AI. WIREs Data Mining and Knowledge Discovery, 13(4). https://doi.org/10.1002/widm.1507 6. United Nations Environment Programme. (2024). Artificial Intelligence (AI) end-to-end: The environmental impact of the full AI lifecycle needs to be comprehensively assessed. UNEP. 7. Green AI techniques for reducing energy consumption in AI systems. (2025). Array. 8. The green paradox: The climate, environmental, and sustainability implications of artificial intelligence. (2025). Global Environmental Change Advances. 9. Advances and challenges in energy and climate alignment of AI infrastructure expansion. (2025). Advances in Applied Energy.
Author information
V.M.Sajitha
Assistant Professor, Valliammal College for Women, E-9 Anna nagar East, Chennai.
S.Geetha Priya
III B.Com, Valliammal College for Women, E-9 Anna nagar East, Chennai.
Suree Pogu Joyce Mary
III B.Com, Valliammal College for Women, E-9 Anna nagar East, Chennai.