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題名 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
作者 李香儀
Lee, Hsiang-Yi
貢獻者 江彌修
Chiang, Mi-Hsiu
李香儀
Lee, Hsiang-Yi
關鍵詞 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
日期 2025
上傳時間 1-Jul-2025 15:18:12 (UTC+8)
摘要 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.
參考文獻 Akinjole, A., Shobayo, O., Popoola, J., Okoyeigbo, O., & Ogunleye, B. (2024). Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction. Mathematics, 12(21), 3423. Álvarez-García, O., & Sureda-Negre, J. (2023). Greenwashing and education: An evidence-based approach. The Journal of Environmental Education, 54, 265–277. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173. Berg, F., Kölbel, J., & Rigobon, R. (2022a). Aggregate Confusion: The Divergence of ESG Ratings. Review of Finance, 26(6), 1315–1344. Brandon, G., Krueger, P., & Schmidt, P. (2019). ESG Rating Disagreement and Stock Returns. Financial Analysts Journal, 77, 104–127. De Freitas Netto, S., Sobral, M., Ribeiro, A., & Da Luz Soares, G. (2020). Concepts and forms of greenwashing: A systematic review. Environmental Sciences Europe, 32, 1–12. Delmas, M., & Burbano, V. (2011a). The Drivers of Greenwashing. California Management Review, 54, 64–87. Delmas, M., & Burbano, V. (2011b). The Drivers of Greenwashing. California Management Review, 54, 64–87. Galletta, S., Mazzù, S., Naciti, V., & Paltrinieri, A. (2024). A PRISMA systematic review of greenwashing in the banking industry: A call for action. Research in International Business and Finance. Gao, B., & Balyan, V. (2022). Construction of a financial default risk prediction model based on the LightGBM algorithm. Journal of Intelligent Systems, 31, 767–779. Garrido-Merchán, E., González-Barthe, C., & Vaca, M. (2023). Fine-tuning ClimateBert Transformer with ClimaText for the Disclosure Analysis of Climate-related Financial Risks. arXiv preprint arXiv:2303.13373. Guo, K., Bian, Y., Zhang, D., & Ji, Q. (2024). ESG performance and corporate external financing in China: The role of rating disagreement. Research in International Business and Finance. Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C. (2018). Ensemble Learning or Deep Learning? Application to Default Risk Analysis. Journal of Risk and Financial Management, 11(1), 12. Hu, X. & coauthors. (2023). ESG Rating Discrepancy and Climate Greenwashing. Hu, X., Hua, R., Liu, Q., & Wang, C. (2023). The green fog: Environmental rating disagreement and corporate greenwashing. Pacific-Basin Finance Journal, 78, 101952. Kim, R., & Koo, B. (2023). The impact of ESG rating disagreement on corporate value. Journal of Derivatives and Quantitative Studies. Kölbel, J., Leippold, M., Rillaerts, J., & Wang, Q. (2020a). Ask BERT: How Regulatory Disclosure of Transition and Physical Climate Risks Affects the CDS Term Structure. Environmental Science eJournal. Kölbel, J., Leippold, M., Rillaerts, J., & Wang, Q. (2020b). Does the CDS Market Reflect Regulatory Climate Risk Disclosures? University of Zurich Working Paper. Kündig, P., & Sigrist, F. (2024). A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios. arXiv preprint arXiv:2410.02846. Lai, Y., & Chen, M. (2024). Using Natural Language Processing With Explainable AI Approach to Construct a Human-Centric Consumer Application for Financial Climate Disclosures. IEEE Transactions on Consumer Electronics, 70, 1112–1121. Li, G., & Cheng, Y. (2024). Impact of environmental, social, and governance rating disagreement on real earnings management in Chinese listed companies. Global Finance Journal. Li, W., Ding, S., Wang, H., Chen, Y., & Yang, S. (2019). Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China. World Wide Web, 23, 23–45. Liu, X., Dai, J., Dong, X., & Liu, J. (2024). ESG rating disagreement and analyst forecast quality. International Review of Financial Analysis. Liu, X., Liu, J., Liu, J., & Qiong, Z. (2024). Can investor-firm interactions mitigate ESG rating divergence? Evidence from China. International Review of Financial Analysis. Menardi, G., Tedeschi, F., & Torelli, N. (2011). On the Use of Boosting Procedures to Predict the Risk of Default. In Classification and Multivariate Analysis for Complex Data Structures (pp. 211-218). Berlin, Heidelberg: Springer Berlin Heidelberg. Nemes, N., Scanlan, S., Smith, P., Smith, T., Aronczyk, M., Hill, S., Lewis, S., Montgomery, A., Tubiello, F., & Stabinsky, D. (2022). An Integrated Framework to Assess Greenwashing. Sustainability, 14(8), 4431. Saavedra, C., Gomes, J., De Castro Gomes, E., & Kimura, H. (2024). Probability of default for lifetime credit loss for IFRS 9 using machine learning competing risks survival analysis models. Expert Systems with Applications, 249, 123607. Sautner, Z., Van Lent, L., Vilkov, G., & Zhang, R. (2023). Firm-level climate change exposure. The Journal of Finance, 78(3), 1449–1498. Sigrist, F., & Hirnschall, C. (2017). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance. Spaniol, M., Danilova-Jensen, E., Nielsen, M., Rosdahl, C., & Schmidt, C. (2024). Defining Greenwashing: A Concept Analysis. Sustainability, 16(20). Szabo, S., & Webster, J. (2020). Perceived Greenwashing: The Effects of Green Marketing on Environmental and Product Perceptions. Journal of Business Ethics, 171, 719–739. Torelli, R., Balluchi, F., & Lazzini, A. (2019). Greenwashing and Environmental Communication: Effects on Stakeholders’ Perceptions. Socially Responsible Investment eJournal. Uddin, M., Chi, G., Habib, T., & Zhou, Y. (2019). An Alternative Statistical Framework for Credit Default Prediction. Journal of Risk Model Validation. Wang, J., Wang, S., Dong, M., & Wang, H. (2023). ESG rating disagreement and stock returns: Evidence from China. International Review of Financial Analysis. Wu, M. & coauthors. (2020). Greenwashing Theoretical Model. Yu, E., Luu, B., & Chen, C. (2020). Greenwashing in environmental, social and governance disclosures. Research in International Business and Finance, 52, 101192. Zhang, X., Kong, L., & Hu, X. (2024). Shades of Green: The Impact of Greenwashing on Stock Price Crash Risk. Finance Research Letters. Zhang, X., Shan, Y., Zhang, Y., & Xing, C. (2025). Does ESG rating divergence affect the cost of corporate debt? Accounting & Finance. Zhou, J., Li, W., Wang, J., Ding, S., & Xia, C. (2019). Default prediction in P2P lending from high-dimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications. Zhu, J., Xiong, Z., Lu, X., & Yao, Z. (2024). Does ESG rating disagreement impede corporate green innovation? Global Finance Journal.
描述 碩士
國立政治大學
金融學系
112352028
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112352028
資料類型 thesis
dc.contributor.advisor 江彌修zh_TW
dc.contributor.advisor Chiang, Mi-Hsiuen_US
dc.contributor.author (Authors) 李香儀zh_TW
dc.contributor.author (Authors) Lee, Hsiang-Yien_US
dc.creator (作者) 李香儀zh_TW
dc.creator (作者) Lee, Hsiang-Yien_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Jul-2025 15:18:12 (UTC+8)-
dc.date.available 1-Jul-2025 15:18:12 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2025 15:18:12 (UTC+8)-
dc.identifier (Other Identifiers) G0112352028en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157839-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 112352028zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 研究貢獻 3 第二章 文獻探討 5 第一節 ESG 評級分歧與企業財務風險 5 第二節 漂綠行為與其金融後果 7 第三節 氣候相關文本分析 10 第四節 傳導機制 11 第五節 Boosting模型於信用違約預警之應用 12 第三章 研究方法 14 第一節 概述 14 第二節 變數定義 16 第三節 資料來源與處理 19 第四節 模型與估計方法 22 第四章 實證結果 29 第一節 ESG 評級不一致與漂綠行為之關聯 29 第二節 漂綠指標對企業違約風險的解釋力 36 第三節 傳導效果分析 52 第四節 違約預警模型 54 第五章 結論與展望 61 第一節 結論 61 第二節 未來展望 62 第六章 參考文獻 65zh_TW
dc.format.extent 2855049 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112352028en_US
dc.subject (關鍵詞) 漂綠zh_TW
dc.subject (關鍵詞) ESG 評級分歧zh_TW
dc.subject (關鍵詞) 信用違約風險zh_TW
dc.subject (關鍵詞) EPA 違規zh_TW
dc.subject (關鍵詞) 中介效果zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) Greenwashingen_US
dc.subject (關鍵詞) ESG rating discrepancyen_US
dc.subject (關鍵詞) Default risken_US
dc.subject (關鍵詞) EPA violationsen_US
dc.subject (關鍵詞) Mediation analysisen_US
dc.subject (關鍵詞) Machine learningen_US
dc.title (題名) 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zh_TW
dc.title (題名) 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-Learningen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Akinjole, A., Shobayo, O., Popoola, J., Okoyeigbo, O., & Ogunleye, B. (2024). Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction. Mathematics, 12(21), 3423. Álvarez-García, O., & Sureda-Negre, J. (2023). Greenwashing and education: An evidence-based approach. The Journal of Environmental Education, 54, 265–277. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173. Berg, F., Kölbel, J., & Rigobon, R. (2022a). Aggregate Confusion: The Divergence of ESG Ratings. Review of Finance, 26(6), 1315–1344. Brandon, G., Krueger, P., & Schmidt, P. (2019). ESG Rating Disagreement and Stock Returns. Financial Analysts Journal, 77, 104–127. De Freitas Netto, S., Sobral, M., Ribeiro, A., & Da Luz Soares, G. (2020). Concepts and forms of greenwashing: A systematic review. Environmental Sciences Europe, 32, 1–12. Delmas, M., & Burbano, V. (2011a). The Drivers of Greenwashing. California Management Review, 54, 64–87. Delmas, M., & Burbano, V. (2011b). The Drivers of Greenwashing. California Management Review, 54, 64–87. Galletta, S., Mazzù, S., Naciti, V., & Paltrinieri, A. (2024). A PRISMA systematic review of greenwashing in the banking industry: A call for action. Research in International Business and Finance. Gao, B., & Balyan, V. (2022). Construction of a financial default risk prediction model based on the LightGBM algorithm. Journal of Intelligent Systems, 31, 767–779. Garrido-Merchán, E., González-Barthe, C., & Vaca, M. (2023). Fine-tuning ClimateBert Transformer with ClimaText for the Disclosure Analysis of Climate-related Financial Risks. arXiv preprint arXiv:2303.13373. Guo, K., Bian, Y., Zhang, D., & Ji, Q. (2024). ESG performance and corporate external financing in China: The role of rating disagreement. Research in International Business and Finance. Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C. (2018). Ensemble Learning or Deep Learning? Application to Default Risk Analysis. Journal of Risk and Financial Management, 11(1), 12. Hu, X. & coauthors. (2023). ESG Rating Discrepancy and Climate Greenwashing. Hu, X., Hua, R., Liu, Q., & Wang, C. (2023). The green fog: Environmental rating disagreement and corporate greenwashing. Pacific-Basin Finance Journal, 78, 101952. Kim, R., & Koo, B. (2023). The impact of ESG rating disagreement on corporate value. Journal of Derivatives and Quantitative Studies. Kölbel, J., Leippold, M., Rillaerts, J., & Wang, Q. (2020a). Ask BERT: How Regulatory Disclosure of Transition and Physical Climate Risks Affects the CDS Term Structure. Environmental Science eJournal. Kölbel, J., Leippold, M., Rillaerts, J., & Wang, Q. (2020b). Does the CDS Market Reflect Regulatory Climate Risk Disclosures? University of Zurich Working Paper. Kündig, P., & Sigrist, F. (2024). A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios. arXiv preprint arXiv:2410.02846. Lai, Y., & Chen, M. (2024). Using Natural Language Processing With Explainable AI Approach to Construct a Human-Centric Consumer Application for Financial Climate Disclosures. IEEE Transactions on Consumer Electronics, 70, 1112–1121. Li, G., & Cheng, Y. (2024). Impact of environmental, social, and governance rating disagreement on real earnings management in Chinese listed companies. Global Finance Journal. Li, W., Ding, S., Wang, H., Chen, Y., & Yang, S. (2019). Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China. World Wide Web, 23, 23–45. Liu, X., Dai, J., Dong, X., & Liu, J. (2024). ESG rating disagreement and analyst forecast quality. International Review of Financial Analysis. Liu, X., Liu, J., Liu, J., & Qiong, Z. (2024). Can investor-firm interactions mitigate ESG rating divergence? Evidence from China. International Review of Financial Analysis. Menardi, G., Tedeschi, F., & Torelli, N. (2011). On the Use of Boosting Procedures to Predict the Risk of Default. In Classification and Multivariate Analysis for Complex Data Structures (pp. 211-218). Berlin, Heidelberg: Springer Berlin Heidelberg. Nemes, N., Scanlan, S., Smith, P., Smith, T., Aronczyk, M., Hill, S., Lewis, S., Montgomery, A., Tubiello, F., & Stabinsky, D. (2022). An Integrated Framework to Assess Greenwashing. Sustainability, 14(8), 4431. Saavedra, C., Gomes, J., De Castro Gomes, E., & Kimura, H. (2024). Probability of default for lifetime credit loss for IFRS 9 using machine learning competing risks survival analysis models. Expert Systems with Applications, 249, 123607. Sautner, Z., Van Lent, L., Vilkov, G., & Zhang, R. (2023). Firm-level climate change exposure. The Journal of Finance, 78(3), 1449–1498. Sigrist, F., & Hirnschall, C. (2017). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance. Spaniol, M., Danilova-Jensen, E., Nielsen, M., Rosdahl, C., & Schmidt, C. (2024). Defining Greenwashing: A Concept Analysis. Sustainability, 16(20). Szabo, S., & Webster, J. (2020). Perceived Greenwashing: The Effects of Green Marketing on Environmental and Product Perceptions. Journal of Business Ethics, 171, 719–739. Torelli, R., Balluchi, F., & Lazzini, A. (2019). Greenwashing and Environmental Communication: Effects on Stakeholders’ Perceptions. Socially Responsible Investment eJournal. Uddin, M., Chi, G., Habib, T., & Zhou, Y. (2019). An Alternative Statistical Framework for Credit Default Prediction. Journal of Risk Model Validation. Wang, J., Wang, S., Dong, M., & Wang, H. (2023). ESG rating disagreement and stock returns: Evidence from China. International Review of Financial Analysis. Wu, M. & coauthors. (2020). Greenwashing Theoretical Model. Yu, E., Luu, B., & Chen, C. (2020). Greenwashing in environmental, social and governance disclosures. Research in International Business and Finance, 52, 101192. Zhang, X., Kong, L., & Hu, X. (2024). Shades of Green: The Impact of Greenwashing on Stock Price Crash Risk. Finance Research Letters. Zhang, X., Shan, Y., Zhang, Y., & Xing, C. (2025). Does ESG rating divergence affect the cost of corporate debt? Accounting & Finance. Zhou, J., Li, W., Wang, J., Ding, S., & Xia, C. (2019). Default prediction in P2P lending from high-dimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications. Zhu, J., Xiong, Z., Lu, X., & Yao, Z. (2024). Does ESG rating disagreement impede corporate green innovation? Global Finance Journal.zh_TW