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AI-Driven Carbon-Neutral Computing Sustainability: A Data-Driven Framework Integrating Machine Learning and Environmental–Economic Systems
- Truvace record
- TRV-2026-2624raw data (JSON)
- Version
- v2Revised
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- af0c3d4d07b4…check it
While artificial intelligence (AI) can improve energy efficiency in carbon neutrality applications, its high energy consumption and rebound effect weaken the actual emission reduction effect. To address the issues of high energy consumption and the rebound effect of AI weakening emission reduction, this paper proposes a green AI-driven environmental economic computing framework. First, an energy consumption perception index is introduced, and carbon emissions are monitored in real time using Carbontracker versio…
The problem
In experiments on a green AI framework, knowledge distillation had a rebound index of 0.75 and a net energy-saving rate of only 9.1%, so rebound erodes most of the expected savings.
Sources
1 on file- Sustainabilitydoi:10.3390/su18115526
Cite this record
v2Truvace Impact Record TRV-2026-2624, v2: “AI-Driven Carbon-Neutral Computing Sustainability: A Data-Driven Framework Integrating Machine Learning and Environmental–Economic Systems.” Truvace, 2026-11-12. /record/TRV-2026-2624 (accessed on the day you copy it). sha256 af0c3d4d07b48581…
Change history
2 versionsEvery change made to this record since it was first published, and why.
Reworded the problem so it stands on its own; no facts changed.
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