mlsanityData to 7 Oct 2026

Quantifying the Carbon Emissions of Machine Learning

arXiv 1910.09700 ↗First posted 21 Oct 2019cs.CY cs.LGcited by 516

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Abstract (arXiv)

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.

Key facts. Cited by 516 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2019.

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Citing papers per year: 2021: 53; 2022: 80; 2023: 107; 2024: 125; 2025: 82; 2026: 69.

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