Discourse vs emissions: Analysis of corporate narratives, symbolic practices, and mimicry through LLMs
This addresses the issue of symbolic reporting in corporate climate disclosures for regulators and investors, though it is incremental in applying LLMs to ESG analysis.
The paper tackled the problem of assessing corporate climate disclosure maturity by analyzing narratives from 828 U.S. firms using fine-tuned LLMs, revealing that quantitative targets often decouple from tone and widespread mimicry reduces decision usefulness.
Climate change has increased demands for transparent and comparable corporate climate disclosures, yet imitation and symbolic reporting often undermine their value. This paper develops a multidimensional framework to assess disclosure maturity among 828 U.S.listed firms using large language models (LLMs) fine-tuned for climate communication. Four classifiers-sentiment, commitment, specificity, and target ambition-extract narrative indicators from sustainability and annual reports, which are linked to firm attributes such as emissions, market capitalization, and sector. Analyses reveal three insights: (1) risk-focused narratives often align with explicit commitments, but quantitative targets (e.g., net-zero pledges) remain decoupled from tone; (2) larger and higher-emitting firms disclose more commitments and actions than peers, though inconsistently with quantitative targets; and (3) widespread similarity in disclosure styles suggests mimetic behavior, reducing differentiation and decision usefulness. These results highlight the value of LLMs for ESG narrative analysis and the need for stronger regulation to connect commitments with verifiable transition strategies.