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The paper presents an analysis of the environmental break-even point for ML-based data compression, estimating carbon-equivalent costs for training and inference against savings from reduced disk storage.
A systematic review and empirical study comparing carbon footprints of deep learning models, examining Green AI techniques and measurement tools, with findings that training dominates emissions and that larger architectures do not always yield proportionate accuracy gains.
Starlight's guide to building eco-friendly documentation sites, covering techniques to reduce page weight, caching, and power consumption.
Microsoft's greenhouse gas emissions rose 25% last year, driven by the expansion of data center infrastructure for AI, as reported in its new sustainability report.
Google's AI expansion caused a 37% increase in electricity use in 2025, raising its carbon footprint to 14.5 million metric tons. The company continues to invest in renewable energy and clean tech, but faces scrutiny over reliance on natural gas.
This paper investigates the lack of standardized reporting on computational and environmental costs of LLMs in AIED research, reviewing 396 AIED 2025 papers and proposing an open-source method to measure and report these impacts.
This paper proposes a carbon-aware re-ranking strategy for e-commerce recommendations, using a retrieval-augmented pipeline to estimate product carbon footprints and trading off predicted engagement against sustainability. Evaluated on Amazon Reviews data, substantial carbon reductions are achievable with minimal engagement loss.