# Carbon accounting in the Cloud: a methodology for allocating emissions across data center users

arXiv: https://arxiv.org/abs/2406.09645 · HTML: https://mlsanity.com/paper/2406.09645/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Measuring the environmental impact of delivering AI at Google Scale](https://mlsanity.com/paper/2508.15734/) (2025)
- [Performance Prediction for Large Systems via Text-to-Text Regression](https://mlsanity.com/paper/2506.21718/) (2025)
- [Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends](https://mlsanity.com/paper/2502.01671/) (2025)
- [Who Pays for the KV Cache? Attributing Shared AI Inference Spend Across Kubernetes and LLM Provider Bills](https://mlsanity.com/paper/2609.24991/) (2026)

## Abstract (arXiv)

This paper presents a methodology for allocating energy consumption to multiple users of shared data center machines, infrastructure, and software. Google uses this methodology to provide carbon reporting data for enterprise customers of multiple Google products, including Google Cloud and Workspace. The approach documented here advances the state-of-the-art of large scale Cloud carbon reporting systems. It uses detailed, granular measurement data on machine energy consumption. In addition, it uses physical factors for allocating energy consumption and carbon emissions--preferred by the Greenhouse Gas Protocol's Scope 3 Reporting Standard. Specifically, the approach described here allocates machine energy consumption based on a combination of data center resource reservations and hourly measured resource usage. It also accounts for Google's own internal use of shared software services, reallocating energy use to the users of those shared services. Finally, it uses hourly, location-specific estimates of carbon intensity to precisely measure carbon emissions of users in a global fleet of data centers.