The paper analyzes the environmental footprint of machine learning model training, focusing on large language models and their hardware. It finds that energy use and environmental impacts have risen exponentially over the past decade, even when employing carbon‑efficient electricity and more efficient hardware. The study argues that optimization strategies alone cannot curb these impacts due to a rebound effect, and stresses the need to evaluate hardware life‑cycle impacts and integrate environmental metrics into NLP research practices.
By Cl\'ement Morand (STL), Anne-Laure Ligozat (ENSIIE, LISN, STL), Aur\'elie N\'ev\'eol (STL, LISN)
arXiv:2602. 19789v2 Announce Type: replace Abstract: This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development.
By Sophia N. Wilson, Andrew Millard, Gu{\dh}r\'un Fj\'ola Gu{\dh}mundsd\'ottir, Raghavendra Selvan, Sebastian Mair
arXiv:2609.00847v1 Announce Type: cross
Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...
By Filippo Dainelli, Amirpasha Mozaffari, Marina Casta\~no, Aina Gaya i \`Avila, Llu\'is Palma Garcia, Alessio Melli, Oscar Dimdore Miles, Amanda Duarte
arXiv:2608. 14614v1 Announce Type: cross Abstract: As AI datacenters retire functional GPUs, vast quantities of still capable accelerators enter secondary markets.
By Zeyu Cao, Xuan Guo, Cheng Zhang, Cheuk Hang Lau, Ilia Shumailov, Yiren Zhao
arXiv:2608. 09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.
By Samar Garrab, Sarra Boughriou, Manel BenSassi
arXiv:2606. 07632v1 Announce Type: new Abstract: Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to assess the barriers to building systems at scale.
By Jared Fernandez, Clara Na, Yonatan Bisk, Constantine Samaras, Emma Strubell
arXiv:2608. 08761v1 Announce Type: cross Abstract: In an era defined by escalating climate change and the pervasive deployment of edge intelligence, the environmental cost of semiconductor manufacturing and operation has reached a critical threshold.
By Jatin Chopra
The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.
By Lachlan McGinness, Dan Pagendam, Robert Offner
arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.
By Guillermo Llopis (SOMA AI, Barcelona)
arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.
By Emanuele Del Sozzo, Martin Fleming, Kenneth Flamm, Neil Thompson
arXiv:2608. 14557v1 Announce Type: cross Abstract: Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems.
By Nisha Sarwar, Lei Jiang, Fan Chen
arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.
By Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e