Pith Number

pith:L3PNEVFR

pith:2019:L3PNEVFRT2OW2JUOGWMCFSY2LA

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Quantifying the Carbon Emissions of Machine Learning

Alexandra Luccioni, Alexandre Lacoste, Thomas Dandres, Victor Schmidt

A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware.

arxiv:1910.09700 v2 · 2019-10-21 · cs.CY · cs.LG

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Claims

C1strongest claim

we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models

C2weakest assumption

The listed factors (server location, energy grid, training length, hardware) are sufficient to accurately approximate emissions and that the tool's estimates will be reliable enough to guide mitigation decisions.

C3one line summary

Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.

References

25 extracted · 25 resolved · 8 Pith anchors

[1] Energy and policy considerations for deep learning in nlp 1906 · arXiv:1906.02243

[2] Emma Strubell, Ananya Ganesh, and Andrew McCallum 1907

[3] Institute for Global Environmental Strategies Hayama, Japan, 2006 2006

[4] Ghg emissions from electricity consumption: A case study of hong kong from 2002 to 2015 and trends to 2030 2002

[5] Electricity- speciﬁc emission factors for grid electricity.Ecometrica, Emissionfactors 2011

Formal links

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Cited by

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Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters

Receipt and verification

First computed	2026-05-17T23:38:53.338922Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b

Aliases

arxiv: 1910.09700 · arxiv_version: 1910.09700v2 · doi: 10.48550/arxiv.1910.09700 · pith_short_12: L3PNEVFRT2OW · pith_short_16: L3PNEVFRT2OW2JUO · pith_short_8: L3PNEVFR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b

Canonical record JSON

{
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    "abstract_canon_sha256": "d4ce882e1d4e646bf26fc5ac7996efe579ab08f03782965320b110c07728eadd",
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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2019-10-21T23:57:32Z",
    "title_canon_sha256": "53c718faef53841f47f977dc8641503cb9d4fc1a9e2602604343414e85ae24bc"
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  "source": {
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}