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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/157839
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157839


    Title: 檢驗美國上市企業之漂綠行為、ESG 評級分歧與信用風險傳遞:多階段中介因果識別分析與機器學習違約預警構建
    Examining Corporate Greenwashing, ESG Rating Divergence, and Credit Risk Transmission in U.S. Listed Firms: Causal Identification via Multi-Stage Mediation Analysis and Default-Risk Early Warning by Machine-Learning
    Authors: 李香儀
    Lee, Hsiang-Yi
    Contributors: 江彌修
    Chiang, Mi-Hsiu
    李香儀
    Lee, Hsiang-Yi
    Keywords: 漂綠
    ESG 評級分歧
    信用違約風險
    EPA 違規
    中介效果
    機器學習
    Greenwashing
    ESG rating discrepancy
    Default risk
    EPA violations
    Mediation analysis
    Machine learning
    Date: 2025
    Issue Date: 2025-07-01 15:18:12 (UTC+8)
    Abstract: 在氣候風險揭露與永續金融逐漸制度化的背景下,企業 ESG 評級的不一致性與其形象管理行為所隱含的信用風險受到高度關注。本文以美國上市公司為對象,建構一套結合 ESG 評級、氣候相關新聞與法說會文本情緒,以及 EPA 環境違規資訊的漂綠指標(Greenwashing Index, GWI),以量化企業在永續聲明與實際行為間的落差。實證採用多階段分析架構,首先驗證 ESG 評級分歧是否驅動企業進行漂綠行為,其次檢驗漂綠是否顯著提升企業的違約風險,並進一步透過中介模型確認漂綠在 ESG 評級分歧與違約風險之間的傳導角色。最終,本文將 GWI 納入 XGBoost 機器學習架構中,建構信用違約預警模型,發現該指標具備顯著預測力。整體結果顯示:ESG 評級分歧會透過漂綠行為間接提高企業違約機率,尤其在存在 EPA 違規紀錄時更為明顯。此發現揭示資訊一致性與揭露誠信在信用風險評價中不可忽視,亦對永續金融監理政策提供具體啟示。
    In light of increasing regulatory attention to climate disclosure and sustainable finance integrity, this study investigates how ESG rating divergence influences corporate greenwashing behavior and transmits to credit default risk. Using a panel of U.S. listed firms, we construct a novel Greenwashing Index (GWI) that integrates ESG ratings, sentiment derived from climate-related news and earnings call transcripts, and violations reported by the U.S. Environmental Protection Agency (EPA). Empirical results based on panel regressions and cross-sectional estimations reveal that firms facing greater ESG rating divergence are more likely to engage in greenwashing. This, in turn, significantly increases their probability of default (PD), particularly in the presence of regulatory violations. Mediation tests further confirm that greenwashing serves as a transmission mechanism between ESG rating discrepancy and credit risk. Finally, incorporating GWI into an XGBoost-based default prediction model improves classification performance and interpretability, as evidenced by SHAP analysis. The findings underscore the role of reputational credibility and information consistency in credit risk assessment and offer practical insights for ESG disclosure policies and sustainable financial regulation.
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    Description: 碩士
    國立政治大學
    金融學系
    112352028
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112352028
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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