Sustainability & AI
Green AI
Research and practice aimed at reducing the environmental cost of AI itself, as distinct from using AI for environmental ends.
Definition
AI research and practice that treats computational efficiency as a primary objective, evaluating and reporting the environmental cost of models alongside their accuracy.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
The term was framed by Schwartz et al. in a 2020 Communications of the ACM paper contrasting 'Green AI' with 'Red AI' — the pursuit of accuracy through escalating computation. It advocates reporting training cost, using efficiency metrics, and treating efficiency as a scientific contribution in itself.
How it is used
Practices include efficiency-aware model design, distillation and quantisation, reporting of training energy in papers, carbon-aware scheduling of runs, and choosing smaller models when they suffice. Conferences and funding programmes increasingly request energy disclosures.
Why it matters
Green AI reframes efficiency from an afterthought to a criterion of good research. For sustainability teams procuring or building AI, it provides both the vocabulary and the metrics to ask what a model costs to run, not just what it achieves.
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