用大模型分析企业气候披露，发现承诺与行动脱节、模仿现象普遍。
Discourse vs emissions: Analysis of corporate narratives, symbolic practices, and mimicry through LLMs
- 用微调的LLM提取气候报告中的情感、承诺等四类叙事指标。
- 828家美国上市公司中，高排放企业更频繁披露承诺但目标不明确。
- 报告风格高度相似，反映模仿行为，削弱信息披露价值。
气候变化加剧了对企业气候披露透明度和可比性的需求，但模仿和象征性报告常削弱其价值。本文针对828家美国上市企业，开发多维度评估框架，利用微调的大语言模型（LLMs）分析可持续发展报告与年报中的气候沟通内容。四个分类器——情感、承诺、具体性、目标雄心——提取叙事指标，并与企业碳排放、市值、行业等属性关联。分析揭示三点：(1) 风险导向叙事常与明确承诺一致，但量化目标（如净零承诺）与语气仍脱节；(2) 更大、高排放企业披露更多承诺与行动，却与量化目标不一致；(3) 披露风格广泛趋同，显示模仿行为，降低信息差异化与决策有用性。结果表明，LLM在ESG叙事分析中具有价值，亟需更强监管以连接承诺与可验证转型路径。
原文摘要 · Abstract (English)
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.
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