arXiv AI
A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling
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arXiv AI
Aug 12
Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
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.
arXiv AI
Oct 2
Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research
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.
Hugging Face Trending Papers
Oct 1
Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research
As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practi...
arXiv Machine Learning
Jun 2
Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
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.
arXiv Machine Learning
Jun 2
Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.
arXiv Machine Learning
Sep 18
The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound
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.