This paper raises serious concerns about the environmental costs of large-scale language models (LLMs), such as GPT-3 and BERT. Using case studies of models like GPT-4 and Mistral 7B, we quantify the carbon footprint, water usage, and e-waste generated by LLMs. We demonstrate that training these models generates carbon dioxide equivalent to the annual driving of hundreds of cars and that data center cooling can exacerbate water shortages in vulnerable communities. Systemic issues such as corporate environmental degradation, redundant model development, and regulatory gaps perpetuate the damage, particularly burdening marginalized communities in the global South. However, there are options for sustainable NLP, including technological innovations like model pruning and quantum computing, policy reforms like carbon taxes and mandatory emissions reporting, and a cultural shift that prioritizes necessity. By analyzing leading companies like Google and Microsoft and less established players like Amazon, we highlight the urgency of ethical responsibility and international cooperation. We conclude by advocating for fair, transparent, and regenerative AI systems that prioritize both human and environmental well-being while aligning technological advancements within planetary boundaries.
