# The Carbon Cost of Conversation, Sustainability in the Age of Language Models

arXiv: https://arxiv.org/abs/2507.20018 · HTML: https://mlsanity.com/paper/2507.20018/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 62 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://mlsanity.com/paper/2005.07683/) (2020)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)

## Built on by (most cited first, 2 of 2)

- [Spiking Neural Networks: The Future of Brain-Inspired Computing](https://mlsanity.com/paper/2510.27379/) (2025)
- [Emissions and Performance Trade-off Between Small and Large Language Models](https://mlsanity.com/paper/2601.08844/) (2025)

## Abstract (arXiv)

Large language models (LLMs) like GPT-3 and BERT have revolutionized natural language processing (NLP), yet their environmental costs remain dangerously overlooked. This article critiques the sustainability of LLMs, quantifying their carbon footprint, water usage, and contribution to e-waste through case studies of models such as GPT-4 and energy-efficient alternatives like Mistral 7B. Training a single LLM can emit carbon dioxide equivalent to hundreds of cars driven annually, while data centre cooling exacerbates water scarcity in vulnerable regions. Systemic challenges corporate greenwashing, redundant model development, and regulatory voids perpetuate harm, disproportionately burdening marginalized communities in the Global South. However, pathways exist for sustainable NLP: technical innovations (e.g., model pruning, quantum computing), policy reforms (carbon taxes, mandatory emissions reporting), and cultural shifts prioritizing necessity over novelty. By analysing industry leaders (Google, Microsoft) and laggards (Amazon), this work underscores the urgency of ethical accountability and global cooperation. Without immediate action, AIs ecological toll risks outpacing its societal benefits. The article concludes with a call to align technological progress with planetary boundaries, advocating for equitable, transparent, and regenerative AI systems that prioritize both human and environmental well-being.