# A Systematic Review of Green AI

arXiv: https://arxiv.org/abs/2301.11047 · HTML: https://mlsanity.com/paper/2301.11047/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 133 references that are in the map.
- First posted on arXiv 26 Jan 2023.

## Builds on (most cited first, 30 of 40)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [Sponge Examples: Energy-Latency Attacks on Neural Networks](https://mlsanity.com/paper/2006.03463/) (2020)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network](https://mlsanity.com/paper/1811.11431/) (2018)
- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [Optimizing the energy consumption of spiking neural networks for neuromorphic applications](https://mlsanity.com/paper/1912.01268/) (2019)
- [E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings](https://mlsanity.com/paper/1910.13349/) (2019)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Unraveling the Hidden Environmental Impacts of AI Solutions for Environment](https://mlsanity.com/paper/2110.11822/) (2021)
- [NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1710.05420/) (2017)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Energy-Aware Analog Aggregation for Federated Learning with Redundant Data](https://mlsanity.com/paper/1911.00188/) (2019)
- [A survey on Variational Autoencoders from a GreenAI perspective](https://mlsanity.com/paper/2103.01071/) (2021)
- [Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks](https://mlsanity.com/paper/1612.03940/) (2016)
- [Designing Adaptive Neural Networks for Energy-Constrained Image Classification](https://mlsanity.com/paper/1808.01550/) (2018)
- [Towards Accurate and Reliable Energy Measurement of NLP Models](https://mlsanity.com/paper/2010.05248/) (2020)
- [pCAMP: Performance Comparison of Machine Learning Packages on the Edges](https://mlsanity.com/paper/1906.01878/) (2019)
- [HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks](https://mlsanity.com/paper/1712.02446/) (2017)
- [Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking](https://mlsanity.com/paper/1806.04321/) (2018)
- [IrEne: Interpretable Energy Prediction for Transformers](https://mlsanity.com/paper/2106.01199/) (2021)
- [On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks](https://mlsanity.com/paper/2111.07911/) (2021)
- [ALERT: Accurate Learning for Energy and Timeliness](https://mlsanity.com/paper/1911.00119/) (2019)
- [Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs](https://mlsanity.com/paper/1808.00286/) (2018)

## Built on by (most cited first, 30 of 77)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI](https://mlsanity.com/paper/2303.13972/) (2023)
- [Near to Mid-term Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2404.17047/) (2024)
- [Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2405.08597/) (2024)
- [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://mlsanity.com/paper/2309.02065/) (2023)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Generating Energy-Efficient Code via Large-Language Models -- Where are we now?](https://mlsanity.com/paper/2509.10099/) (2025)
- [Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement](https://mlsanity.com/paper/2308.12264/) (2023)
- [How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models](https://mlsanity.com/paper/2409.18664/) (2024)
- [Estimating Deep Learning energy consumption based on model architecture and training environment](https://mlsanity.com/paper/2307.05520/) (2023)
- [Batching for Green AI -- An Exploratory Study on Inference](https://mlsanity.com/paper/2307.11434/) (2023)
- [Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder](https://mlsanity.com/paper/2401.03575/) (2024)
- [Training Green AI Models Using Elite Samples](https://mlsanity.com/paper/2402.12010/) (2024)
- [Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference](https://mlsanity.com/paper/2406.14328/) (2024)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2](https://mlsanity.com/paper/2411.12758/) (2024)
- [Mini-Giants: "Small" Language Models and Open Source Win-Win](https://mlsanity.com/paper/2307.08189/) (2023)
- [How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection](https://mlsanity.com/paper/2404.19452/) (2024)
- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights](https://mlsanity.com/paper/2504.06307/) (2025)
- [Sustainability via LLM Right-sizing](https://mlsanity.com/paper/2504.13217/) (2025)
- [Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysis](https://mlsanity.com/paper/2511.04481/) (2025)
- [FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation](https://mlsanity.com/paper/2310.11131/) (2023)
- [Identifying architectural design decisions for achieving green ML serving](https://mlsanity.com/paper/2402.07585/) (2024)
- [Green AI in Action: Strategic Model Selection for Ensembles in Production](https://mlsanity.com/paper/2405.17451/) (2024)
- [Towards Green AI: Current status and future research](https://mlsanity.com/paper/2407.10237/) (2024)
- [Impact of ML Optimization Tactics on Greener Pre-Trained ML Models](https://mlsanity.com/paper/2409.12878/) (2024)
- [How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts](https://mlsanity.com/paper/2412.17376/) (2024)

## Abstract (arXiv)

With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this paper, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm-agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.