Published on: April 2026
ADVANCING SUSTAINABLE COMPUTING THROUGH GREEN ARTIFICIAL INTELLIGENCE
Nidhi Dhankhar Devansh Nagpal Krrish Aggrawal Disha Grover
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Abstract
The exponential growth of the Artificial Intelligence (AI) and Machine Learning (ML) models has led to tremendous scaling in costs of computational work, energy, and carbon emissions, thus becoming a major concern in ensuing of the environmental sustainability of contemporary AI systems. The classical methods, prevalent as Red AI, considers result-oriented factors such as accuracy and scalability but fails to maintain their resource efficiency. Green AI, on the other hand, addresses this problem by designing, constructing and implementing AI systems which are environment-friendly and energy-saving.
This paper includes a systematic review of the Green AI techniques, which analyzes the methods to reduce the cost of computation without adversely affecting the performance of the model. Our attention is drawn to strategies such as model compression (pruning, quantization, knowledge distillation), energy-constrained neural architecture search (NAS), smarter training tricks, and the application of low-power hardware accelerators. We also quantify other things such as energy use, training duration, carbon footprint, as well as the normal accuracy values, challenging towards a multi-objective optimization method.
How to Cite this Paper
Dhankhar, N., Nagpal, D., Aggrawal, K. & Grover, D. (2026). Advancing Sustainable Computing Through Green Artificial Intelligence. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.634
Dhankhar, Nidhi, et al.. "Advancing Sustainable Computing Through Green Artificial Intelligence." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.634.
Dhankhar, Nidhi,Devansh Nagpal,Krrish Aggrawal, and Disha Grover. "Advancing Sustainable Computing Through Green Artificial Intelligence." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.634.
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References
- Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
- Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in natural language processing. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 3645–3650. https://doi.org/10.18653/v1/P19-1355
- Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv Preprint. https://doi.org/10.48550/arXiv.2104.10350
- Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248), 1–43.
- Grover, D. (2024). The AI assistant revolution: Microsoft Copilot and the future of programming. Educational Administration: Theory and Practice, ISSN: 2148-2403, 30(1). https://doi.org/10.53555/kuey.v30i1.5758
- Grover, D. (2025). The Art of prompt engineering: shaping AI responses for Real life applications. International Journal of Science, Mathematics and Technology Learning, SSN: 2327-7971 (Print) ISSN: 2327-915X (Online), 33(1).
- Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2019). Tackling climate change with machine learning. arXiv Preprint. https://doi.org/10.48550/arXiv.1906.05433
- Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., Rolnick, D., & Steg, L. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527. https://doi.org/10.1038/s41558-022-01377-7
- Gupta, U., Kim, Y. G., Lee, S., Tse, J., Lee, H. H. S., Wei, G. Y., Brooks, D., & Wu, C. J. (2022). Chasing carbon: The elusive environmental footprint of computing. IEEE Micro, 42(4), 37–47. https://doi.org/10.1109/MM.2022.3179962
- Wu, C. J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Lee, K., Tung, L., San Miguel, J., et al. (2022). Sustainable AI: Environmental implications, challenges, and opportunities. Nature Machine Intelligence, 4, 563–565. https://doi.org/10.1038/s42256-022-00517-1
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