Work

Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models

Year: 2025

Type: conference-paper

Abstract: 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 LLMgreenwashed responses.* Denotes co-first authorship, ordered randomly.Co-first authors will prioritize their names on their resumes/websites.

DOI: 10.18653/v1/2025.climatenlp-1.2

Authors: Marianne Chuang, Gabriel Chuang, Cheryl Chuang, John Chuang

Institutions: University of California, Santa Cruz, Columbia University, University of California, Berkeley

Language: English

Topic: Corporate Social Responsibility Reporting

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Funder: National Science Foundation

OpenAlex ID: W4412889069