Examples
Worked examples
- Is an instance
A model card declaring '450 MWh training energy, 80 tCO2e at the training-region grid intensity'.
- Is an instance
A research paper appendix using CodeCarbon to log training-run emissions.
Counter-examples
Looks similar, but isn't
- Not an instance
A statement of GPU-hours alone.
- Not an instance
A vague 'this work used renewable energy' assertion.
Editorial commentary
Reporting follows Strubell et al. (2019) and Schwartz et al.'s 'Green AI' (2020) framings. Reproducible reporting requires disclosure of hardware (GPU/TPU type and count), training duration, PUE-adjusted energy, and grid carbon intensity at the training location. Tools include CodeCarbon, ML CO2 Impact, and provider-published carbon transparency.
References
- Strubell, Ganesh, McCallum, 'Energy and Policy Considerations for Deep Learning in NLP' (ACL 2019); Schwartz et al., 'Green AI' (Communications of the ACM, 2020).
Also known as
model training CO2 · training emissions
Machine-readable encodings
Use in your systems
xml
<role vocab="credit"
vocab-identifier="https://casrai.org/dictionary/"
vocab-term="Training carbon footprint"
vocab-term-identifier="https://casrai.org/dictionary/term/training-carbon-footprint" />json
{
"@context": "https://schema.org",
"@type": "DefinedTerm",
"name": "Training carbon footprint",
"identifier": "https://casrai.org/dictionary/term/training-carbon-footprint",
"description": "The total greenhouse-gas emissions, expressed in kilograms or tonnes of CO2-equivalent, attributable to training a machine-learning model, estimated from energy consumption and the carbon intensity of the electricity supply.",
"inDefinedTermSet": "https://casrai.org/dictionary/domain/ai-and-ml-research-outputs/",
"url": "https://casrai.org/dictionary/term/training-carbon-footprint",
"sameAs": [
"model training CO2",
"training emissions"
],
"license": "https://creativecommons.org/licenses/by/4.0/"
}
