Policy Document · 2026-05-01
24/7 Carbon-Free Energy for All: Towards a Resilient Energy System
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Scientific Paper · source-verified
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Luccioni, Alexandra Sasha; Jernite, Yacine; Strubell, Emma · 2024-10-15
Generative AI models consume significantly more energy and emit more carbon than task-specific models, raising concerns about their environmental impact.
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Luccioni, Alexandra Sasha; Jernite, Yacine; Strubell, Emma. 2024-10-15. Power Hungry Processing: Watts Driving the Cost of AI Deployment?. https://arxiv.org/abs/2311.16863 (AI & Environment Resource Hub; record paper-003; collection snapshot 2026-09-15).
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- Author or creator
- Luccioni, Alexandra Sasha; Jernite, Yacine; Strubell, Emma
- Publisher
- arXiv
- Publication date
- 2024-10-15
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- day
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- Unknown
- Language
- unknown
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- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:44:20.029Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Energy
- Legacy subdomain
- Computational Power and Energy Costs of AI
- journal
- arXiv
Provenance and review
Source sheet: Scientific Paper · Row 7 · Original ID: paper-003.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
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Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases
Scientific Paper · 2024-12-11
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Accurate energy measurement is essential for reducing ML’s carbon footprint, requiring standardized tools, best practices, and improved adoption among researchers.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases
Scientific Paper · 2025-05-01
A Behavioral Finance Framework for Balancing AI Accuracy and Operational Carbon Emissions
This study presents a behavior-driven decision framework that helps AI developers choose models balancing accuracy and environmental sustainability by quantifying energy use and emissions during fine-tuning and applying behavioral decision theories.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases
Scientific Paper · 2025-08-27
A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge
This paper examines the environmental impact of deploying generative AI models on cloud versus edge platforms, finding that edge deployment can achieve over 90% energy savings while reducing carbon emissions and water consumption by more than 80%.