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Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning

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arxiv 2302.08476 v1 pith:K3RUR3MO submitted 2023-02-16 cs.LG cs.CY

Alexandra Sasha Luccioni , Alex Hernandez-Garcia This is my paper
classification cs.LGcs.CY
keywords emissionsenergycarbonmodelsacrossenvironmentallearningmachine
verification ladder T0 review T1 audit T2 compute T3 formal
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Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. Existing research on the environmental impacts of ML has been limited to analyses covering a small number of models and does not adequately represent the diversity of ML models and tasks. In the current study, we present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision. We analyze them in terms of the energy sources used, the amount of CO2 emissions produced, how these emissions evolve across time and how they relate to model performance. We conclude with a discussion regarding the carbon footprint of our field and propose the creation of a centralized repository for reporting and tracking these emissions.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 26 citations worldwide. Full citation record

  1. Misinformation by Omission: The Need for More Environmental Transparency in AI

    cs.CY 2025-06 conditional novelty 6.0 of 10

    Environmental disclosure for notable AI models peaked in 2022 and then declined, and out-of-context energy and emissions estimates now dominate media coverage.

  2. Climate Implications of Diffusion-based Generative Visual AI Systems and their Mass Adoption

    cs.CY 2025-05 conditional novelty 6.0 of 10

    Mass adoption of text-to-image diffusion models could consume roughly 2 to 9 TWh per year, based on very rough assumptions about user behavior and hardware.

  3. Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead

    cs.SE 2025-06 conditional novelty 5.0 of 10

    A structured survey of 21 software energy and carbon calculation tools, organized as Monitoring, Estimation, or Black-Box approaches, with a component-wise comparison and a list of open challenges.

  4. EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge-Cloud Environments

    cs.DC 2026-07 conditional novelty 4.5 of 10

    EcoKube provides a configurable event-driven simulator and reference policy that cuts estimated emissions ~45% versus default Kubernetes under synthetic hybrid edge-cloud scenarios, with modest makespan cost.

  5. Irresponsible AI: big tech's influence on AI research and associated impacts

    cs.CY 2025-11 conditional novelty 2.0 of 10

    A review/position paper arguing that big tech's outsized influence on AI research channels the field toward scaling and general-purpose systems, producing environmental and social harms that technical fixes alone cann...

  6. Digital Overconsumption and Waste: A Closer Look at the Impacts of Generative AI

    cs.AI 2025-05 conditional novelty 2.0 of 10

    Generative AI is framed as a driver of digital overconsumption and waste, with user surveys cited to show low awareness of energy use.

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