Home / Current Issue / Paper 1717851
Green Artificial Intelligence: Energy-Efficient and Sustainable Approaches for Modern AI Systems
Subject area: Science,Engineering and Technology · Area of research: Sustainable & Energy-Efficient AI Systems
DOI: https://doi.org/10.64388/IREV9I11-1717851
Abstract
The rapid advancement of Artificial Intelligence (AI) has significantly increased the computational power and energy required to train and deploy large-scale machine learning models. This growing demand contributes to higher electricity consumption and carbon emissions, raising concerns about the environmental sustainability of AI technologies. Green AI has emerged as a research paradigm that focuses on developing energy-efficient AI models, algorithms, and computing infrastructures while maintaining high performance and accuracy. This research project examines the principles of Green AI and explores techniques for reducing the environmental impact of AI systems, including model optimization, efficient hardware utilization, and sustainable data center practices. The study also evaluates the trade-off between model performance, computational cost, and energy consumption. By promoting energy-aware AI development, Green AI aims to create environmentally sustainable and cost-effective intelligent systems for future technological applications.
References
[1] Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
[2] Patterson, D., et al. (2021). Carbon and the broad challenge of sustainable AI. arXiv preprint arXiv:2104.10350.
[3] Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02629.
[4] Dhar, P. (2020). The carbon impact of artificial intelligence. Nature Machine Intelligence, 2(8), 423–425.
[5] Rolnick, D., et al. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1–96. 6. World Economic Forum. (2023). AI for the Earth: Using AI to tackle environmental challenges. WEF Report.
[6] Verdecchia, R., et al. (2023). A systematic review of Green AI. WIREs Data Mining and Knowledge Discovery, 13(4)
How to cite this paper
@article{1717851,
author = {Isha Jagdale, Samreen Shaikh, Snehal Tajanpure, Abrashmeena Saikh},
title = {Green Artificial Intelligence: Energy-Efficient and Sustainable Approaches for Modern AI Systems},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2172-2174},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717851.pdf},
abstract = {The rapid advancement of Artificial Intelligence (AI) has significantly increased the computational power and energy required to train and deploy large-scale machine learning models. This growing demand contributes to higher electricity consumption and carbon emissions, raising concerns about the environmental sustainability of AI technologies. Green AI has emerged as a research paradigm that focuses on developing energy-efficient AI models, algorithms, and computing infrastructures while maintaining high performance and accuracy. This research project examines the principles of Green AI and explores techniques for reducing the environmental impact of AI systems, including model optimization, efficient hardware utilization, and sustainable data center practices. The study also evaluates the trade-off between model performance, computational cost, and energy consumption. By promoting energy-aware AI development, Green AI aims to create environmentally sustainable and cost-effective intelligent systems for future technological applications.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717851}
}