8 arXiv papers covering AI energy and water consumption, embodied carbon, the Jevons rebound, and sustainable AI practice. The OECD policy report is link-only.
All PDFs link to raw.githubusercontent.com; clicking will download the file directly. Source links go to the canonical version on arXiv, the journal, or the publisher.
3.1 · What Does AI Actually Consume?
Making AI Less ‘Thirsty’: Uncovering and Addressing the Secret Water Footprint of AI Models
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
3.2 · Infrastructure, Scale and the Rebound Problem
Chasing Carbon: The Elusive Environmental Footprint of Computing
Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
3.3 · Critical Minerals and AI
Sub-lesson uses news reporting and policy documents (CHIPS Act, EU CRMA, US Geological Survey) which aren’t redistributable PDFs.
3.4 · Sustainable AI: What Can Be Done?
Green AI
Tackling Climate Change with Machine Learning
Measuring the Carbon Intensity of AI in Cloud Instances
Linked but not redistributed
OECD (2022). Measuring the environmental impacts of artificial intelligence compute and applications. DOI:10.1787/7babf571-en 3.4
Open-access on the OECD library — included as a link rather than a downloaded copy because the OECD URL changes occasionally.