# Related Documents | AI methodology
Source: https://greenpixie.com/docs/methodology/ai/related-documents

Standards, research, and sources referenced by the methodology.

## Greenpixie Methodologies [#greenpixie-methodologies]

* Greenpixie, [*Methodology for Cloud Emission Measurement*](/methodology/cloud).

## Greenpixie Research [#greenpixie-research]

* Horswill, J., Hunter, R., Clifford, M. and Hall, J., [*Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO2-eq Impact of AI Tokens for Open and Closed Weight Models*](https://arxiv.org/pdf/2609.33965), arXiv:2609.33965, 2026.

## Standards and Protocols [#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 [#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 [#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 [#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 [#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 [#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 [#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 [#instrumentation-and-serving]

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