# Green BOA: Determining the environmental break-even point for ML-based data compression

arXiv: https://arxiv.org/abs/2608.19994 · HTML: https://mlsanity.com/paper/2608.19994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 1 of 1)

- [The Dirty Secret of SSDs: Embodied Carbon](https://mlsanity.com/paper/2207.10793/) (2022)

## Abstract (arXiv)

We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-based data compression algorithms. Using the example of a ML-based lossless compression algorithm, we compare estimates for the carbon-equivalent of the infrastructure needed for ML training and inference with the carbon-equivalent savings from reduced disk storage requirements, and discuss their break-even point.