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:2609.33965v2 Announce Type: replace-cross
Abstract: We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between i...
By Joshua Horswill, Ross Hunter, Matt Clifford, James Hall
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
By Yuxin Chen (University of Helsinki, Finland), Hao Gao (Independent Researcher), Chujie Zou (University of Helsinki, Finland)
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:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
By Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
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