Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models
This addresses the problem of assessing and detecting greenwashing in corporate climate reports for stakeholders like regulators and investors, but it is incremental as it builds on existing LLM methodologies.
The study used large language models to evaluate and manipulate corporate climate disclosures, finding that LLM-as-a-Judge scoring systems, particularly pairwise comparison, effectively distinguished high-performing companies and were robust against LLM-greenwashed responses.
We study the use of large language models (LLMs) to both evaluate and greenwash corporate climate disclosures. First, we investigate the use of the LLM-as-a-Judge (LLMJ) methodology for scoring company-submitted reports on emissions reduction targets and progress. Second, we probe the behavior of an LLM when it is prompted to greenwash a response subject to accuracy and length constraints. Finally, we test the robustness of the LLMJ methodology against responses that may be greenwashed using an LLM. We find that two LLMJ scoring systems, numerical rating and pairwise comparison, are effective in distinguishing high-performing companies from others, with the pairwise comparison system showing greater robustness against LLM-greenwashed responses.