# Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

arXiv: https://arxiv.org/abs/2610.01116 · HTML: https://mlsanity.com/paper/2610.01116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [Position: We Need Responsible, Application-Driven (RAD) AI Research](https://mlsanity.com/paper/2505.04104/) (2025)
- [Large Language Models Imitate Logical Reasoning, but at what Cost?](https://mlsanity.com/paper/2509.12645/) (2025)
- [The Environmental Cost of LLMs in AIED: Reporting and Practices](https://mlsanity.com/paper/2606.11215/) (2026)
- [Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors](https://mlsanity.com/paper/2602.00315/) (2026)

## Abstract (arXiv)

As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practices for carbon accounting. Our automated literature review of the 5,285 papers accepted to NeurIPS 2025 reveals that reporting of environmental impact is nearly non-existent. To catalyse a shift toward sustainable AI, we define standardised sustainability metrics for evaluating model training efficiency, accompanied by simple heuristics to estimate the carbon cost of LLM inference. We implement these metrics in carbonbenchmark, a drop-in software solution for tracking and reporting emissions. Finally, to combat the pursuit of marginal accuracy gains at disproportionate environmental costs, we formalise the `Smallest Model that Achieves the Job' (SMAJ), a framework which challenges the field to prioritise computational efficiency and environmental accountability alongside traditional `State-of-the-Art' (SotA) accuracy.