This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited observability. The framework converts natural-language application descriptions into bounded environmental estimates and supports a comparative online observatory of current market models. Rather than claiming direct measurement for opaque proprietary services, it provides an auditable, source-linked proxy methodology designed to improve comparability, transparency, and reproducibility.
@article{arxiv.2604.19757,
title = {Transparent Screening for LLM Inference and Training Impacts},
author = {Arnault Pachot and Thierry Petit},
journal= {arXiv preprint arXiv:2604.19757},
year = {2026}
}