Quantifying the Environmental Footprint of Curating Datasets with LLMs

This study evaluates the environmental trade-offs of using large language models to curate cross-collection oral-history datasets in the Commoning Oral Histories of Knowledge (CORAL) project. Manual screening of 2,606 interviews was benchmarked against a workflow that tested four instruction-tuned LLMs and two prompt designs. Environmental impact was approximated using token-based inputs to EcoLogits, although implementing such assessments remains non-trivial. Ultimately, we conclude that the environmental impact of our project's use case could be considered moderate compared to common academic activities such as traveling to conferences. However, such impacts should be monitored closely, as they may vary significantly across different research setups and are likely to scale with larger datasets and broader adoption of LLMs in the field. Finally, the paper urges sufficiency-oriented practices and transparent carbon reporting in Computational Humanities research.