本研究探討台灣企業碳排放表現對信用評等之影響，並比較須遵循溫室氣體（GHG）排放申報規定之高碳排產業與無須申報之非高碳排產業的差異。研究採用 2020 年至 2024 年 6 月之季度資料，運用隨機森林（Random Forest）機器學習方法，整合財務指標、TESG 評分與 TCRI 信用評等進行分析。
實證結果顯示，TCRI 評等等級的分布呈現高碳排企業傾向於較低的信用風險，但此結果仍待正式統計檢驗加以確認。雖然多數 ESG 變數因多重共線性被剔除，GHG 排放評分在兩組樣本中皆為重要預測因子。於高碳排產業中，較佳的 GHG 表現能提升獲得較優信用評等（如 TCRI 第4級）的機率；反之，在非高碳排產業並未觀察到穩定的碳排放–評等等級連結，顯示碳排放績效在非碳密集產業中並非決定性因素。
研究亦發現，部分高碳排企業可能被歸入非高碳排樣本，致使碳排放衡量指標之預測力被稀釋。此結果意謂現行 GHG 盤查涵蓋範圍或尚未充分反映不同產業間與碳風險相關之信用評等差異，未來監理機關或需進一步檢討相關規範。

This study examines the impact of carbon emission performance on corporate credit ratings in Taiwan, contrasting firms subject to GHG reporting mandates (high-carbon-emitting industries) with non-mandated sectors. Utilizing quarterly data from 2020 to June 2024 and a Random Forest machine learning approach, the analysis integrates financial metrics, TESG scores, and TCRI ratings.
The distribution of TCRI ratings suggests a tendency toward lower perceived credit risk among high-carbon-emitting firms; however, this observation awaits confirmation through formal statistical tests. Despite eliminating most ESG variables due to multicollinearity, GHG Emission Scores emerged as significant predictors in both groups. For high-carbon industries, stronger GHG performance increased the probability of favorable credit ratings (e.g., TCRI 4). In contrast, non-high-carbon industries exhibited no consistent emission-rating linkage, suggesting carbon performance is less decisive outside carbon-intensive sectors.
The study identifies potential misclassification of high-carbon firms within the non-high-carbon sample, which may obscure emission metrics' predictive power. The evidence implies that the present GHG-inventory scope may not yet fully capture differences in carbon-related credit risk across industrial groups, signaling a possible need for future regulatory review.

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