The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound

arXiv:2510.09022 · cs.LG · Submitted 2025-10-10 · Read on arXiv

cs.LG

Submitted: 2025-10-10

Updated: 2026-09-17

Journal ref: 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), Oct 2026, Budapest, Hungary

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Life Cycle Assessment — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Accountability — 3× in this paper · explained in 2 episodes
• Energy Efficiency — 3× in this paper · explained in 3 episodes
• Natural Language Processing — 3× in this paper
• Artificial Intelligence (AI) — 2× in this paper · explained in 2 episodes
• International Energy Agency — 2× in this paper
• Recent Machine Learning — 2× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Global Warming Potential — 1× in this paper
• Information and Communication Technology — 1× in this paper
• Integrated Circuits — 1× in this paper
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Major Artificial Intelligence — 1× in this paper
• Power Usage Efficiency — 1× in this paper
• Termal Design Power — 1× in this paper
• Water Usage Efficiency — 1× in this paper

Sources

• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
• Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
• Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends

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