Corporate Climate Narratives for Transition‐Finance Governance: A Language‐Model Index of Transition Risk and Financing Conditions in China

ABSTRACT Achieving the Sustainable Development Goals (SDGs), especially SDG 13 (Climate Action), requires mobilizing transition finance, yet firm‐level climate transition risk is difficult to observe in emerging economies with incomplete emissions data and uneven disclosure. Using the Management Discussion and Analysis sections of Chinese listed firms' annual reports from 2010 to 2023, this study constructs a narrative‐based transition‐risk index (CTR) by fine‐tuning a sentence‐level transformer classifier and aggregating sentence‐level transition‐risk intensity to the firm‐year level. The language‐model approach outperforms dictionary methods on a held‐out hand‐labeled test set. In within‐firm fixed‐effects regressions, higher disclosed transition‐risk content, as captured by CTR, is associated with higher borrowing costs, indicating tighter debt‐market financing conditions; in the controlled baseline specifications, a one‐standard‐deviation increase in CTR is associated with a 0.335–0.365 percentage‐point increase in the cost of debt. A shift‐share instrument combining predetermined baseline carbon intensity with leave‐one‐industry‐out transition‐risk trends provides supportive, though not definitive, identification evidence. In joint disclosure‐based specifications, a narrative‐based physical‐risk measure extracted from the same source exhibits a different pricing pattern from transition‐risk narratives, consistent with the possibility that physical‐risk disclosure mixes exposure with adaptation‐ or resilience‐related content. Overall, the results indicate that disclosed transition‐risk content, rather than a cleanly observed structural risk measure, contains lender‐relevant information and can support transition‐finance governance in data‐constrained settings.