Sustainability & AI
AI carbon footprint
The greenhouse-gas emissions associated with AI systems across hardware manufacture, training and operation.
Definition
The greenhouse-gas emissions attributable to an AI system across its life cycle: hardware manufacture, model training, and ongoing operation (inference).
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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This source supports the wider sustainability significance and context described in the entry.
Overview
What it means
Accounting follows established boundaries — embodied emissions of chips and servers, electricity consumed in training and serving, weighted by grid carbon intensity. Peer-reviewed studies (Strubell 2019; Patterson 2021; Dodge 2022; Luccioni 2022) established the method and showed results vary by orders of magnitude with scale, hardware and location.
How it is used
Organisations increasingly include AI use in scope 3 and ICT inventories; researchers publish per-model footprint estimates; and the EU AI Act obliges general-purpose model providers to document energy consumption. Estimates remain sensitive to allocation choices and data gaps.
Why it matters
AI's climate ledger has two columns — emissions caused and emissions potentially avoided. Without credible footprint accounting, neither the cost side nor the benefit side of that ledger can be managed, compared or regulated.
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