A four-tier framework for AI inference GHG emissions in Scope 3 reporting, progressing from direct physical estimation using GPU benchmarks to EEIO spend-based methods, with a case showing low total emissions.
and Ren, Shaolei , title =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
AI data center electricity demand will reach 1% of global power use by 2030, with concentrated siting causing high power stress in specific regions like Oregon, Virginia, and Ireland.
A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.
The paper formalizes the Water and AI Feedback Loop, introduces the Water Consumption Impact index, and shows water burden from AI data centers varies from 0.2% to 134% of local capacity across ten US sites.
citing papers explorer
-
Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
A four-tier framework for AI inference GHG emissions in Scope 3 reporting, progressing from direct physical estimation using GPU benchmarks to EEIO spend-based methods, with a case showing low total emissions.
-
Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand
AI data center electricity demand will reach 1% of global power use by 2030, with concentrated siting causing high power stress in specific regions like Oregon, Virginia, and Ireland.
-
Brief Announcement: Generative Markov Model for Distributed Computing Systems
A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.
-
AI Data Centers and the Water Use Feedback Loop
The paper formalizes the Water and AI Feedback Loop, introduces the Water Consumption Impact index, and shows water burden from AI data centers varies from 0.2% to 134% of local capacity across ten US sites.