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Scientific Paper · source-verified
A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge
Li et al., 2025 · 2025-08-27
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%.
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Li et al., 2025. 2025-08-27. A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge. https://dl.acm.org/doi/10.1145/3764944.3764950 (AI & Environment Resource Hub; record paper-436; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
orientation
Available source metadata
- Author or creator
- Li et al., 2025
- Publisher
- ACM Digital Library
- Publication date
- 2025-08-27
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T17:00:20.706Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Sustainable Computing & AI Footprint
- Legacy subdomain
- AI Inference and Environmental Impact of AI Models
- journal
- ACM Digital Library
Provenance and review
Combined source rows: Scientific Paper 440; Scientific Paper 442.
Source sheet: Scientific Paper · Row 440 · Original ID: paper-436.
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
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water
Multimedia · 2026-02-25
AI for Energy, Water and Waste Management Conference
Conference held on the 19th of February 2026, organised by the Veolia Institute, hosted at the Collège des Bernardins, on the role of AI for the main environmental challenges. This event followed the joint publication of our last report, coproduced with Microsoft, called "AI for Energy, Water and Waste Management". Our experts explored the potential benefits of AI for the ecological transition in relation to concerns raised by its carbon footprint and growing energy, water, and precious metal requirements.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water
Scientific Paper · 2026-02-05
AI, Climate Change and Justice: Elements for a Normative Framework Centring the Global South
This paper argues that AI's environmental impacts from energy consumption and water use to mineral extraction constitute a global climate justice concern that demands moving beyond efficiency metrics to center the unequal distribution of costs and benefits, particularly in the Global South.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water
Multimedia · 2025-04-11
AI’s Hidden Climate Cost - How Much is Too Much?
How much water, energy, and carbon emissions does it take to train and run AI models like ChatGPT? And why aren’t the biggest AI companies making these numbers public? From the carbon emissions equivalent to round-trip flights to the lack of transparency from major AI companies, Dr. Sasha Luccioni and Brenda Wilkerson unpack the environmental consequences of AI and how it's much bigger than most realize.