mlsanityData to 7 Oct 2026

A Systematic Review of Green AI

arXiv 2301.11047 ↗First posted 26 Jan 2023cs.AIcited by 77

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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.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). Builds on 40 of its 133 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

40 of 133 references.

References · 40

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  2. 2019Green AIcited by 620
  3. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  4. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  5. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
  6. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  7. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
  8. 2016Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruningcited by 133
  9. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 110
  10. 2020Sponge Examples: Energy-Latency Attacks on Neural Networkscited by 108
  11. 2018ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Networkcited by 90
  12. 2021A Survey on Green Deep Learningcited by 52
28 more
  1. 2019E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savingscited by 36
  2. 2019Optimizing the energy consumption of spiking neural networks for neuromorphic applicationscited by 34
  3. 2020AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintechcited by 33
  4. 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
  5. 2017NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networkscited by 30
  6. 2022Data-Centric Green AI: An Exploratory Empirical Studycited by 24
  7. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  8. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  9. 2018Designing Adaptive Neural Networks for Energy-Constrained Image Classificationcited by 18
  10. 2016Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networkscited by 17
  11. 2020Towards Accurate and Reliable Energy Measurement of NLP Modelscited by 16
  12. 2019pCAMP: Performance Comparison of Machine Learning Packages on the Edgescited by 12
  13. 2017HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networkscited by 11
  14. 2018Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Maskingcited by 11
  15. 2021IrEne: Interpretable Energy Prediction for Transformerscited by 11
  16. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  17. 2019ALERT: Accurate Learning for Energy and Timelinesscited by 9
  18. 2018Energy-based Tuning of Convolutional Neural Networks on Multi-GPUscited by 7
  19. 2020SECure: A Social and Environmental Certificate for AI Systemscited by 7
  20. 2020Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimizationcited by 6
  21. 2021Energy-Harvesting Distributed Machine Learningcited by 6
  22. 2022Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Modelscited by 6
  23. 2018Author-Based Analysis of Conference versus Journal Publication in Computer Sciencecited by 3
  24. 2019NeuPart: Using Analytical Models to Drive Energy-Efficient Partitioning of CNN Computations on Cloud-Connected Mobile Clientscited by 3
  25. 2020Sparse Optimization for Green Edge AI Inferencecited by 3
  26. 2021Energy-Efficient AI over a Virtualized Cloud Fog Networkcited by 3
  27. 2022Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-runningcited by 3
  28. 2020Energy-Aware DNN Graph Optimizationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 16; 2024: 25; 2025: 20; 2026: 16.

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  1. 2023Green Edge AI: A Contemporary Surveycited by 24
  2. 2023Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Studycited by 23
  3. 2023A Synthesis of Green Architectural Tactics for ML-Enabled Systemscited by 17
  4. 2023Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AIcited by 16
  5. 2024Near to Mid-term Risks and Opportunities of Open-Source Generative AIcited by 13
  6. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  7. 2024Risks and Opportunities of Open-Source Generative AIcited by 11
  8. 2024Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspectivecited by 9
  9. 2023Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurementcited by 5
  10. 2024How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation modelscited by 5
  11. 2025Generating Energy-Efficient Code via Large-Language Models -- Where are we now?cited by 5
  12. 2023Estimating Deep Learning energy consumption based on model architecture and training environmentcited by 4
38 more
  1. 2023Batching for Green AI -- An Exploratory Study on Inferencecited by 4
  2. 2024Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disordercited by 4
  3. 2024Training Green AI Models Using Elite Samplescited by 4
  4. 2024Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inferencecited by 4
  5. 2024On Tables with Numbers, with Numberscited by 4
  6. 2024An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2cited by 4
  7. 2023Mini-Giants: "Small" Language Models and Open Source Win-Wincited by 3
  8. 2024How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detectioncited by 3
  9. 2024Innovating for Tomorrow: The Convergence of SE and Green AIcited by 3
  10. 2025Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insightscited by 3
  11. 2025Sustainability via LLM Right-sizingcited by 3
  12. 2025Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysiscited by 3
  13. 2023FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluationcited by 2
  14. 2024Identifying architectural design decisions for achieving green ML servingcited by 2
  15. 2024Green AI in Action: Strategic Model Selection for Ensembles in Productioncited by 2
  16. 2024Towards Green AI: Current status and future researchcited by 2
  17. 2024Impact of ML Optimization Tactics on Greener Pre-Trained ML Modelscited by 2
  18. 2024How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impactscited by 2
  19. 2024Green AI: Which Programming Language Consumes the Most?cited by 2
  20. 2025How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuningcited by 2
  21. 2025Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operationscited by 2
  22. 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regimecited by 2
  23. 2025The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generationcited by 2
  24. 2025Choosing to Be Green: Advancing Green AI via Dynamic Model Selectioncited by 2
  25. 2025The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Reboundcited by 2
  26. 2026On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experimentcited by 2
  27. 2026Towards Green AI: Decoding the Energy of LLM Inference in Software Developmentcited by 2
  28. 2026Compression Method Matters: Benchmark-Dependent Output Dynamics in LLM Prompt Compressioncited by 2
  29. 2023An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Codecited by 1
  30. 2023General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governancecited by 1
  31. 2023Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquakecited by 1
  32. 2024Biospheric AIcited by 1
  33. 2024Exploring Green AI for Audio Deepfake Detectioncited by 1
  34. 2024Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learningcited by 1
  35. 2024The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systemscited by 1
  36. 2025Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumptioncited by 1
  37. 2025How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulationscited by 1
  38. 2025More-than-Human Storytelling: Designing Longitudinal Narrative Engagements with Generative AIcited by 1

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