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.
Cite
@article{arxiv.2502.15094,
title = {Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models},
author = {Marianne Chuang and Gabriel Chuang and Cheryl Chuang and John Chuang},
journal= {arXiv preprint arXiv:2502.15094},
year = {2025}
}