Leveraging Language Models to Detect Greenwashing
Avalon Vinella Margaret Capetz Rebecca Pattichis Christina Chance Reshmi Ghosh
In recent years, climate change repercussions have increasingly captured
public interest. Consequently, corporations are emphasizing their environmental
efforts in sustainability reports to bolster their public image. Yet, the
absence of stringent regulations in review of such reports allows potential
greenwashing. In this study, we introduce a novel methodology to train a
language model on generated labels for greenwashing risk. Our primary
contributions encompass: developing a mathematical formulation to quantify
greenwashing risk, a fine-tuned ClimateBERT model for this problem, and a
comparative analysis of results. On a test set comprising of sustainability
reports, our best model achieved an average accuracy score of 86.34% and F1
score of 0.67, demonstrating that our methods show a promising direction of
exploration for this task.