The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Measuring the Carbon Intensity of AI in Cloud Instances
Open accessAbstract
The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, which precludes development of actionable tactics. We argue that cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions.
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“Examples include fully decentralized training [59] or redirecting training processes depending on which cloud region has the lowest carbon intensity at a given time of day [30].”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [30]
In the citing paper
“A recent study estimated that the training of BERT (a 6 billion parameter LLM) would produce between 21 to 78 metric tons of CO2 to train to completion [14].”
From ChatGPT and large language models in academia: opportunities and challenges (2023) · cites this paper as [14]
In the citing paper
“The total energy consumption for training a transformer model with 6B parameters to completion is estimated to be around 103.5 MWh [33].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [33]
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Cited by
Sorted by citations
Harnessing the power of llms in practice: a survey on chatGPT and beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3649506 · Cited by 644
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“A recent study estimated that the training of BERT (a 6 billion parameter LLM) would produce between 21 to 78 metric tons of CO 2 to train to completion [14].”
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“There have also been studies of specific model architectures, e.g. BLOOM [31] and Nour [27], which carried out in-depth analyses of the different steps in the models’ life cycle and their relative contribution towards the final quantity of carbon emissions.”
Self-citation
Metadata
- Authors
- Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- Microsoft (United States) (US)
- Microsoft Research, Canada
- Hebrew University of Jerusalem (IL)
- Carnegie Mellon University (US)
- Hugging Face (US)
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 1877-1894
- OpenAlex
- W4283157527
- Cited by
- 257
- References
- 17
- Citations by year
- 2026: 73 · 2025: 74 · 2024: 60 · 2023: 43 · 2022: 7
- FWCI
- 74.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Allen Institute for Artificial Intelligence
- Microsoft
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- carbon intensity, artificial intelligence, cloud computing, greenhouse gas emissions, energy consumption, machine learning, carbon footprint, cloud service providers, emission measurement
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