Leveraging Language Models to Detect Greenwashing

• URL: http://arxiv.org/abs/2311.01469v2
• Date: Sun, 24 Nov 2024 09:59:23 GMT
• Title: Leveraging Language Models to Detect Greenwashing
• Authors: Avalon Vinella, Margaret Capetz, Rebecca Pattichis, Christina Chance, Reshmi Ghosh, Kai-Wei Chang,
• Abstract summary: We introduce a novel preliminary methodology to train a language model on generated labels for greenwashing risk. Our best model achieved an average accuracy score of 86.34% and F1 score of 0.67, demonstrating that our proof-of-concept methodology shows a promising direction of exploration.
• Score: 39.58317527488534
• License:
• Abstract: 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 preliminary methodology to train a language model on generated labels for greenwashing risk. Our primary contributions encompass: developing a preliminary 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 proof-of-concept methodology shows a promising direction of exploration for this task.

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