Environmental Impact Factor (EIF): A Lifecycle Framework for Sustainable AI

References

[1] K. Strubell, A. Ganesh, and A. McCallum, “Energy and Policy Considerations for Deep Learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

[2] R. Schwartz, J. Dodge, N. Smith, and O. Etzioni, “Green AI,” Communications of the ACM, vol. 63, no. 12, pp. 54–63, Dec. 2020.

[3] L. Zhang, T. Li, and H. Wang, “Carbon Accounting in Deep Learning: A Lifecycle Framework,” in IEEE International Symposium on Sustainable Systems and Technology (IS3), 2022.

[4] Y. Chen and F. Xu, “Benchmarking AI Sustainability: Metrics for Energy, Carbon, and Water,” Journal of Sustainable Computing: Informatics and Systems, vol. 35, pp. 100–112, 2022.

[5] S. Kaur and D. Mehta, “Transparent Reporting Standards for AI Energy Use,” IEEE Access, vol. 10, pp. 11234–11245, 2022.

[6] A. Gupta, S. Singh, and R. Kumar, “Towards Green AI: Current Status and Future Research,” in IEEE Xplore Digital Library, 2023.

[7] J. Lee and K. Park, “Sustainable AI: Emerging Trends, Impacts, and Future Challenges,” IEEE Transactions on Sustainable Computing, vol. 8, no. 3, pp. 450–462, 2023.

[8] P. Patel and M. Chen, “Artificial Intelligence for Sustainability: A Systematic Review and Research Agenda,” Journal of Cleaner Production, vol. 389, pp. 136–148, 2023.

[9] H. Wu and J. Zhao, “Lifecycle Assessment of AI Models: Towards Standardized Sustainability Metrics,” AI & Society, Springer, vol. 38, no. 4, pp. 1123–1135, 2023.

[10] M. Rojahna and M. Gruma, “Green AI: A Systematic Review and MetaAnalysis of Definitions, Lifecycle Models, Hardware and Measurement Attempts,” University of Potsdam Technical Report, 2024

@article{1716550,
      author = {Hardika Raut, Dr. Mrs. Pratibha Adkar},
      title = {Environmental Impact Factor (EIF): A Lifecycle Framework for Sustainable AI},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {2036-2040},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716550.pdf},
      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).},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716550}
  }