Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint

arXiv:2608.09998 · cs.AI, cs.CY, cs.LG, cs.SE · Submitted 2026-08-07 · Read on arXiv

Samar Garrab, Sarra Boughriou, Manel BenSassi

cs.AI, cs.CY, cs.LG, cs.SE

Submitted: 2026-08-07

Updated: 2026-08-12

Comments: Published in Applied Intelligence. DOI: https://doi.org/10.1007/s10489-026-07208-y

Journal ref: Applied Intelligence, vol. 56, Article 173, 2026

DOI: 10.1007/s10489-026-07208-y

Code: https://github.com/mlco2/codecarbon

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 54× in this paper · explained in 1 episode
• Deep Learning — 27× in this paper
• Machine Learning — 16× in this paper
• Artificial Intelligence — 13× in this paper
• Convolutional Neural Network — 5× in this paper
• Energy Efficiency — 5× in this paper · explained in 4 episodes
• Pruning — 5× in this paper · explained in 4 episodes
• Systematic Literature Review — 5× in this paper
• Augmentation — 4× in this paper · explained in 2 episodes
• Data Augmentation — 4× in this paper · explained in 10 episodes
• Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
• Machine Learning (ML) — 3× in this paper · explained in 2 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Quantization — 3× in this paper · explained in 15 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Network (CNN) — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 3 episodes
• Curriculum Learning — 1× in this paper · explained in 3 episodes
• Decoder — 1× in this paper · explained in 2 episodes
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Harness — 1× in this paper · explained in 2 episodes
• International Conference on Big Data Analysis — 1× in this paper
• Localization — 1× in this paper · explained in 2 episodes
• Model Compression — 1× in this paper · explained in 3 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Residual Connections — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes

Sources

• Tackling Climate Change with Machine Learning
• Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues
• A Survey on Green Deep Learning
• Sustainable AI: Environmental Implications, Challenges and Opportunities
• Quantifying the Carbon Emissions of Machine Learning
• On the Opportunities of Green Computing: A Survey
• Green Algorithms: Quantifying the carbon footprint of computation
• Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
• Further advantages of data augmentation on convolutional neural networks
• Fixup Initialization: Residual Learning Without Normalization
• All you need is a good init
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Layer Normalization
• Group Normalization
• Greedy Layerwise Learning Can Scale to ImageNet
• Language model compression with weighted low-rank factorization
• Distilling the Knowledge in a Neural Network
• Energy Usage Reports: Environmental awareness as part of algorithmic accountability
• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
• Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification

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