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
The Climate and Sustainability Implications of Generative AI
Bashir et al. · 2024-03-27
The unchecked growth of generative AI increases energy demand and environmental impact, requiring a sustainability-focused approach beyond efficiency improvements.
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Bashir et al.. 2024-03-27. The Climate and Sustainability Implications of Generative AI. https://mit-genai.pubpub.org/pub/8ulgrckc/release/2 (AI & Environment Resource Hub; record paper-004; collection snapshot 2026-09-15).
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Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
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- Author or creator
- Bashir et al.
- Publisher
- PubPub
- Publication date
- 2024-03-27
- Date precision
- day
- Geographic scope
- Unknown
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- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:44:20.068Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Cross-Cutting Sustainability
- Legacy subdomain
- Sustainability Challenges of Generative AI
- journal
- PubPub
Provenance and review
Combined source rows: Scientific Paper 8; Scientific Paper 211.
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Source sheet: Scientific Paper · Row 8 · Original ID: paper-004.
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%.