Abstract
Artificial intelligence is advancing rapidly, but its environmental cost is often underestimated. Training a single large model can consume as much electricity as several households use in a year. While “Green AI” has become a popular term, most sustainability assessments remain limited to FLOPs per Watt or carbon emissions during training. This narrow focus ignores critical lifecycle impacts such as the water required to cool data centers and the rare earth minerals embedded in GPUs, which contribute to e waste and resource depletion. To address this gap, we propose the Environmental Impact Factor (EIF), a unified framework that integrates energy use, carbon emissions, cooling water footprint, hardware degradation, and e waste into a single sustainability score. EIF provides a more transparent and accountable measure of AI’s true environmental burden.
Keywords
Green AI, Environmental Impact Factor (EIF), lifecycle assessment (LCA), Sustainability Aware Hyperparameter Optimization (SA HPO).
Citations
IRE Journals:
Hardika Raut, Dr. Mrs. Pratibha Adkar "Environmental Impact Factor (EIF): A Lifecycle Framework for Sustainable AI" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2036-2040 https://doi.org/10.64388/IREV9I10-1716550
IEEE:
Hardika Raut, Dr. Mrs. Pratibha Adkar
"Environmental Impact Factor (EIF): A Lifecycle Framework for Sustainable AI" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716550
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