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AI-driven productivity gains enable more CO₂ emissions than they avoid in a global energy–economy model
- Truvace record
- TRV-2026-2593raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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- e7d92e9459b0…check it
Abstract The net climate impacts of artificial intelligence (AI) depend largely on how its applications propagate through competing energy pathways. Predominant analyses examine the relationship between datacenter energy demand, renewables optimization, and demand-side efficiencies, but insufficiently address how AI also reshapes fossil fuel supply economics. We instead model AI as a bidirectional productivity amplifier in a global computable general equilibrium model, quantifying both enabled emissions from fos…
The problem
A global economic model suggests that AI productivity gains applied equally to fossil fuel and renewable energy would raise net annual CO₂ emissions by 0.5–1.8 gigatonnes, 1.2–4.8% of 2024 energy-related emissions.
What this doesn’t fix
The model is static and calibrated to a 2017 base year, so results show the direction and approximate scale of economic incentives rather than time-resolved forecasts, and they exclude datacenter emissions, non-CO₂ gases, infrastructure lock-in and political dynamics.
Sources
1 on file- npj Climate Actiondoi:10.1038/s44168-026-00411-0
Cite this record
v1Truvace Impact Record TRV-2026-2593, v1: “AI-driven productivity gains enable more CO₂ emissions than they avoid in a global energy–economy model.” Truvace, 2026-11-12. /record/TRV-2026-2593 (accessed on the day you copy it). sha256 e7d92e9459b02945…
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