Policy Document · 2026-05-01
24/7 Carbon-Free Energy for All: Towards a Resilient Energy System
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Course · source-verified
Carbon Aware Computing for GenAI Developers
Deep Learning · Publication date unknown
Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud!
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Deep Learning. Date unknown. Carbon Aware Computing for GenAI Developers. https://www.deeplearning.ai/short-courses/carbon-aware-computing-for-genai-developers/ (AI & Environment Resource Hub; record atlas-5a579fdff4d2; collection snapshot 2026-09-15).
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- Deep Learning
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- 2026-09-15T16:45:29.296Z
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- 2026-09-06
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- Energy
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Provenance and review
Source sheet: Course · Row 6 · Original ID: Not supplied.
Availability, fees, and enrollment dates may have changed since collection. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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Shared topics: Energy and electricity; Climate and greenhouse gases
Shared topics: Energy and electricity; Climate and greenhouse gases
Scientific Paper · 2024-12-11
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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: 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%.