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: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:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.
By Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang
arXiv:2608. 14359v1 Announce Type: new Abstract: The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments.
By Keya Patel, Sajib Mistry, Sheik Fattah, Deepak Kanneganti, Aneesh Krishna, Mufti Mahmud, Monowar Bhuyan
arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.
By Pedram Bakhtiarifard, P{\i}nar T\"oz\"un, Christian Igel, Raghavendra Selvan
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
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:2507. 17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale.
By Zhihan Zhang, Alexander Metzger, Yuxuan Mei, Felix H\"ahnlein, Zachary Englhardt, Tingyu Cheng, Gregory D. Abowd, Shwetak Patel, Adriana Schulz, Vikram Iyer
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)
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
SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.
By Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad
arXiv:2607. 01258v1 Announce Type: cross Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software.
By Cl\'ement Morand, Aur\'elie N\'ev\'eol, Anne-Laure Ligozat