Green AI: A Comprehensive Survey on Sustainable and Eco-Efficient Artificial Intelligence Practices

DOI

10.2991/978-94-6463-976-6_14How to use a DOI?

Keywords

Green AI; Sustainable Computing; Eco-Efficient AI; Energy-Efficient AI; Model Compression; Knowledge Distillation; Pruning; Quantization; Edge AI; TinyML; Carbon Footprint of AI; Responsible AI; Federated Green AI; Efficient Transformers; Neuromorphic Computing

Abstract

Artificial Intelligence (AI) have become a major focus of innovation, yet with significant environmental costs. Constructing and deploying large-scale AI models requires significant and deep computational and processing power, which leads to high energy consumption and ultimately carbon emissions. Green AI is another paradigm in AI which emphasizes energy efficiency, resource efficiency and environmental responsibility concerning AI research and applications. We provide a thorough review and discussion of Green AI practices evolved from early model compression practices and solutions to the most current ways of training and deploying AI in a sustainable way. We organize and categorize eco-friendly practices in four categories, (i) algorithm efficiency by pruning, quantization, distillation, and efficient architectures (ii), efficiency in hardware with specialized accelerators, neuromorphic chips, and low-powered edge-devices, (iii) efficiency in software and frameworks using low-precision training, adaptive scheduling, and optimized resource intensive compilers, and (iv) systemic and reporting practices such as carbon footprint measurement, energy aware benchmarks, and policy. Case studies shown in NLP, computer vision and edge IoT, demonstrate that sustainable AI enables users to evaluate performance gains relative to ecological trade-offs. Finally, we discuss future directions in the area, focusing on the efficiency in diffusion models, federated sustainable learning, standardized reporting of energy consumption. The ambition of this review paper is a thorough overview of the Green AI topic to encourage future research on energy-efficient artificial intelligence.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saikiran Gogineni
AU  - Yuvaraju Chinnam
AU  - Kanaka Durga Returi
AU  - Vaka Murali Mohan
AU  - G. Suryanarayana
PY  - 2025
DA  - 2025/12/29
TI  - Green AI: A Comprehensive Survey on Sustainable and Eco-Efficient Artificial Intelligence Practices
BT  - Proceedings of the International Conference on Intelligent Information Systems Design and Indian Knowledge System Applications (ICISDIKSA 2026)
PB  - Atlantis Press
SP  - 198
EP  - 215
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-976-6_14
DO  - 10.2991/978-94-6463-976-6_14
ID  - Gogineni2025
ER  -