# Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study arXiv: https://arxiv.org/abs/2305.11164 ยท HTML: https://mlsanity.com/paper/2305.11164/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 50 references that are in the map. - First posted on arXiv 18 May 2023. ## Builds on (most cited first, 15 of 15) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [Green AI](https://mlsanity.com/paper/1907.10597/) (2019) - [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019) - [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022) - [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020) - [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023) - [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021) - [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022) - [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023) - [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022) - [Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI](https://mlsanity.com/paper/2303.13972/) (2023) - [Energy Efficiency of Training Neural Network Architectures: An Empirical Study](https://mlsanity.com/paper/2302.00967/) (2023) - [HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks](https://mlsanity.com/paper/1712.02446/) (2017) - [Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?](https://mlsanity.com/paper/2109.15284/) (2021) ## Built on by (most cited first, 23 of 23) - [Analyzing the Evolution and Maintenance of ML Models on Hugging Face](https://mlsanity.com/paper/2311.13380/) (2023) - [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024) - [Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends](https://mlsanity.com/paper/2401.13177/) (2024) - [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024) - [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024) - [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024) - [Lessons Learned from Mining the Hugging Face Repository](https://mlsanity.com/paper/2402.07323/) (2024) - [An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face](https://mlsanity.com/paper/2502.04484/) (2025) - [Estimating Deep Learning energy consumption based on model architecture and training environment](https://mlsanity.com/paper/2307.05520/) (2023) - [Towards green AI-based software systems: an architecture-centric approach (GAISSA)](https://mlsanity.com/paper/2307.09964/) (2023) - [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024) - [How do Machine Learning Models Change?](https://mlsanity.com/paper/2411.09645/) (2024) - [Benchmarking Energy Efficiency of Large Language Models Using vLLM](https://mlsanity.com/paper/2509.08867/) (2025) - [The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation](https://mlsanity.com/paper/2506.17016/) (2025) - [Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Society](https://mlsanity.com/paper/2309.09450/) (2023) - [PeaTMOSS: Mining Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2310.03620/) (2023) - [L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection](https://mlsanity.com/paper/2406.16913/) (2024) - [Dissecting Transformers: A CLEAR Perspective towards Green AI](https://mlsanity.com/paper/2510.02810/) (2025) - [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026) - [Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development](https://mlsanity.com/paper/2501.11909/) (2025) - [MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models](https://mlsanity.com/paper/2512.12121/) (2025) - [Quantize with Confidence? An Empirical Study of Quantization for Code Generation](https://mlsanity.com/paper/2607.14181/) (2026) - [A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models](https://mlsanity.com/paper/2607.17242/) (2026) ## Abstract (arXiv) The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon footprint of ML models is actually measured, reported, and evaluated. In light of this, the paper aims to analyze the measurement of the carbon footprint of 1,417 ML models and associated datasets on Hugging Face, which is the most popular repository for pretrained ML models. The goal is to provide insights and recommendations on how to report and optimize the carbon efficiency of ML models. The study includes the first repository mining study on the Hugging Face Hub API on carbon emissions. This study seeks to answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models? The study yielded several key findings. These include a stalled proportion of carbon emissions-reporting models, a slight decrease in reported carbon footprint on Hugging Face over the past 2 years, and a continued dominance of NLP as the main application domain. Furthermore, the study uncovers correlations between carbon emissions and various attributes such as model size, dataset size, and ML application domains. These results highlight the need for software measurements to improve energy reporting practices and promote carbon-efficient model development within the Hugging Face community. In response to this issue, two classifications are proposed: one for categorizing models based on their carbon emission reporting practices and another for their carbon efficiency. The aim of these classification proposals is to foster transparency and sustainable model development within the ML community.