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. 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
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: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:2606. 11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community.
By Sabrina C. Eimler, Lukas Erle, Daniel Flood, Aditi Haiman, Luca H\"ackert, Andr\'e Helgert, Lachlan McGinness, B\"usra Yapici
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
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)
Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses regulatory text into a sentiment score, and repor...
The paper proposes EPA-CarbonNet, a six‑layer model that fuses carbon market price series with policy text via cross‑attention and calibrated intervals, aiming to provide explainable, policy‑aware predictions for carbon credit prices. It evaluates the approach on eleven years of daily S&P carbon index data, finding that a simple random walk outperforms the model on five‑day RMSE, while the model achieves the best directional accuracy at 58.6%. The study identifies ten recurring gaps in current research and releases all code, data, and results publicly.
By Summaiya Unnisa Begum, Mohammed Nadeem Ullah, Mohammed Abdul Ghani Khan
The paper argues that large language models (LLMs) have a growing carbon footprint and that current efficiency gains are offset by rebound effects such as Jevons Paradox. It proposes applying the EU waste hierarchy—prevention, reuse, recycling, recovery, and disposal—to LLMs, suggesting that preventing waste and unnecessary use can significantly reduce environmental impact. The authors emphasize that treating LLMs as products that can become waste offers new ways to motivate more sustainable practices.
By Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard
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