透過 Sautner et al. (2023)由法說會之自願性氣候變遷資訊輔以機器學習方法萃取之氣候資訊揭露程度，我們發現公司氣候變遷的資訊揭露與其信用風險呈現顯著負向關係，且隨著期限結構增加，此關係愈強烈，我們推測氣候資訊的揭露導致公司資本成本下降，進而使公司信用風險下降。從短期角度來看，公司所在國家對其短期信用風險有所影響;而在長期，公司本身的特性則成為影響長期信 用風險的關鍵因素。我們更進一步發現，相較於獲利波動較小的公司，獲利波動較大的公司在揭露氣候資訊後，其短期信用風險更為顯著地下降，這顯示了獲利不穩定的公司透過揭露氣候資訊，能夠快速減少資訊不對稱性，降低短期信用風險。最後，氣候情緒指標反映了投資者對公司氣候資訊的樂觀程度，其與各天期的違約機率呈現負向關聯，顯示出投資人的樂觀情緒有助於降低公司的信用風險。

Using machine learning methods to extract voluntary climate change information disclosure from earnings calls conducted by Sautner et al. (2023), we find a significant negative relationship between firms' disclosure of climate change information and their credit risk. Moreover, as the term structure increases, this relationship becomes stronger. We speculate that climate change information disclosure leads to a decrease in firms' cost of capital, thereby reducing their credit risk. From a short-term perspective, the country in which the firm operates influences its short-term credit risk. Whereas in the long term, the firm's characteristics become key factors affecting long-term credit risk. Furthermore, we find that compared to firms with smaller performance volatility, firms with larger performance volatility experience a more significant reduction in short-term credit risk after disclosing climate information. This demonstrates that firms with unstable profits can quickly reduce information asymmetry and lower short-term credit risk through climate information disclosure. Finally, the climate sentiment index reflects investors' optimism about firms' climate information, showing a negative correlation with default probabilities across different maturity periods, indicating that investor optimism helps reduce firms' credit risk.

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