本研究聚焦於高碳排放產業，探討企業永續資訊揭露對負債資金成本的影響，並進一步檢驗銀行在進行貸款定價時，是否會實際閱讀並根據永續報告書內容進行風險評估與授信條件調整。樣本涵蓋美國能源、金屬與礦業、化工及電子與半導體產業之上市公司，期間為 2017 至 2023 年。
為強化對「揭露內容本身」的量化分析，本文應用自然語言處理技術，透過大型語言模型 ClimateBERT，自企業永續報告書中萃取與 TCFD 四大揭露面向（治理、策略、風險管理、指標與目標）相關資訊，建構文本型衡量指標 TCFD_Score，並與 Refinitiv Asset4 提供之 ESG_Score 與 E_Score 搭配比較。貸款利差變數則採雙軌設計，主模型使用「利息費用除以非流動負債」所估得之 LS 代理變數，另輔以 Dealscan 實際契約利差 LS_real 作為對照指標，以驗證模型穩健性。
實證結果顯示，ESG_Score 與 E_Score 均與貸款利差呈顯著負向關聯，顯示第三方評級已被銀行納入風險定價流程；而 TCFD_Score 雖多數模型中未達顯著，但其方向穩定，顯示企業揭露內容具潛在解釋力。進一步交互作用分析顯示，當企業具備較高 ESG 或 E 評級時，TCFD 揭露更能有效傳遞正向永續信號，有助於降低貸款利差。此外針對揭露與評級不一致的「漂綠」檢驗結果顯示，銀行對永續報告書與實質績效間的落差具初步辨識能力，惟尚未反映於統計顯著之貸款定價差異。
整體而言，本研究透過自然語言處理技術建構文本型衡量指標，提供揭露品質定量化的實證方法，補足現有文獻僅聚焦於結構化 ESG 評級的限制。並進一步回應永續金融實務中之核心疑問：銀行是否會閱讀並解讀企業永續報告書作為授信依據。結果顯示，企業永續資訊能夠透過評級與揭露內容共同影響資金成本，亦提供銀行、企業與監理機關在揭露機制設計與永續風險評估上的政策參考。

This study investigates whether commercial banks incorporate both structured ESG ratings and unstructured sustainability disclosures into loan pricing decisions for firms operating in high-carbon industries. Utilizing advanced natural language processing techniques based on the ClimateBERT model, the analysis constructs a novel TCFD_Score to capture the extent of firms' climate-related textual disclosures, specifically focusing on the Strategy and Metrics & Targets dimensions of the TCFD framework. By integrating textual disclosure measures with third-party ESG_Score and E_Score ratings, this research examines the complementary role of structured ratings and unstructured disclosures in determining firms' cost of debt capital. The empirical results demonstrate that while third-party ESG ratings are consistently priced by lenders, the additional value of textual climate disclosures emerges when supported by strong environmental performance, highlighting the importance of both disclosure credibility and substantive sustainability actions in credit risk assessment.

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