12. Related Documents

Greenpixie Methodologies

• Greenpixie, Methodology for Cloud Emission Measurement.

Standards and Protocols

• World Resources Institute, GHG Protocol Scope 2 Guidance: An Amendment to the GHG Protocol Corporate Standard, 2015.
• International Organization for Standardization, ISO 14064-1:2018.
• International Organization for Standardization, ISO 14064-3:2019.

AI Inference Energy Measurement

• Jegham, N. et al., How Hungry is AI? Benchmarking Energy, Water and Carbon Footprint of LLM Inference, arXiv:2505.09598, 2025.
• Luccioni, A. S., Jernite, Y. and Strubell, E., Power Hungry Processing: Watts Driving the Cost of AI Deployment?, arXiv:2311.16863, 2024.
• Fernandez, J. et al., Energy Considerations of Large Language Model Inference and Efficiency Optimizations, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025.
• Samsi, S. et al., From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference, arXiv:2310.03003, 2023.
• Stojkovic, J. et al., Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference, arXiv:2403.20306, 2024.
• EcoLogits, LLM Inference Methodology, accessed August 2026.

Model and Provider Disclosures

• Google, Measuring the Environmental Impact of Delivering AI at Google Scale, 2025.
• Mistral AI, Our Contribution to a Global Environmental Standard for AI, 2025.
• Patterson, D. et al., The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink, arXiv:2204.05149, 2022.

Model Scale and Architecture

• Epoch AI, Notable AI Models, accessed August 2026.
• Hoffmann, J. et al., Training Compute-Optimal Large Language Models, arXiv:2203.15556, 2022.
• Artificial Analysis, Independent Analysis of AI Models and Providers, accessed August 2026.

Lifecycle Assessment and Embodied Emissions

• Boavizta, Methodologies for Digital Environmental Impact Assessment, accessed August 2026.
• Boavizta, Boavizta API: Embedded Impacts Methodology, accessed August 2026.

Water

• Li, P. et al., Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models, ACM, 2025.
• World Resources Institute, Guidance for Calculating Water Use Embedded in Purchased Electricity, 2020.

Sector Context

• International Energy Agency, Energy and AI, 2025.
• MIT Technology Review, We Did the Math on AI's Energy Footprint. Here's the Story You Haven't Heard, 2025.

Instrumentation and Serving

• NVIDIA, NVIDIA Management Library (NVML) Reference, accessed August 2026.