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Exploring the sustainable scaling of AI dilemma: a projective study of corporations’ AI environmental impacts
- Truvace record
- TRV-2026-2487raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 88da18709153…check it
The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), raises environmental concerns extending beyond greenhouse gas emissions. Limited transparency from major providers hinders companies’ ability to evaluate their AI-related environmental impacts and achieve their net-zero targets. This paper proposes a methodology to estimate the environmental impact of a company’s AI portfolio, delivering actionable insights without requiring deep AI or Life-Cycle Assessment (LCA) experti…
The problem
A modeling study projects that, in a high-adoption scenario with little efficiency progress, a typical Global 2000 company's AI electricity use would reach 24.4 times its 2024 level by 2030, with greenhouse gas emissions up 18.6-fold.
What this doesn’t fix
The 2030 figures are scenario projections that depend on assumptions about adoption, model size and electricity mix, and the model leaves out pre-training and indirect effects.
Sources
1 on file- Scientific Reportsdoi:10.1038/s41598-026-77711-2
Cite this record
v1Truvace Impact Record TRV-2026-2487, v1: “Exploring the sustainable scaling of AI dilemma: a projective study of corporations’ AI environmental impacts.” Truvace, 2026-11-12. /record/TRV-2026-2487 (accessed on the day you copy it). sha256 88da187091534138…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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