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| 研究生： |
林炫旺 Lin, Xuan-Wang |
|---|---|
| 論文名稱： |
綠色金融政策衝擊與企業債務融資成本：
防漂綠監管下之ESG精準定價與合規擠壓效應 The Impact of Green Finance Policy on Corporate Cost of Debt: ESG Precision Pricing and Compliance Crowding-Out Effect under Anti-Greenwashing Regulation |
| 指導教授： |
吳文傑
Wu, Wen-Chieh |
| 口試委員: |
毛治文
Mao, Chih-Wen 周德宇 Chou, De-yu |
| 學位類別： |
碩士
Master |
| 系所名稱： |
社會科學學院 - 應用經濟與社會發展英語碩士學位學程(IMES) International Master's Program of Applied Economics and Social Development(IMES) |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 63 |
| 中文關鍵詞： | 綠色金融行動方案 3.0 、ESG 、債務融資成本 、合規成本擠壓 、精準定價 、防範漂綠 |
| 外文關鍵詞： | Green Finance Action Plan 3.0, ESG, Cost of Debt, Compliance Crowding-out Effect, Precision Pricing, Anti-Greenwashing |
| 相關次數： | 點閱：48 下載：0 |
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隨著全球淨零排放趨勢與台灣「綠色金融行動方案 3.0」的推行，環境、社會與公司治理（ESG）已成為影響企業債務融資成本的關鍵因子。本研究旨在探討在防範漂綠（Anti-Greenwashing）監管趨勢下，ESG 表現對企業利息支出率的動態影響。本研究採用 2019 年至 2025 年台灣上市上櫃公司之面板資料（Panel Data），並透過時間固定效應模型進行實證分析。
實證結果顯示：第一，整體而言，良好的 ESG 表現能顯著降低企業融資成本，支持「風險緩釋假說」。第二，綠金 3.0 的實施引發了顯著的「合規成本擠壓效應（Compliance Crowding-out Effect）」，高昂的法規遵循與數據確信成本排擠了企業財務彈性，導致原有的 ESG 降息紅利平均被削弱約 33.7%。第三，此擠壓效應具備顯著的產業異質性，主要集中於缺乏實質氣候風險對價之「一般產業」；而「高碳排產業」則展現出較強的免疫性。第四，本研究最具突破性的發現指出，在嚴格監理下，銀行對高碳排產業已啟動「精準定價」機制：實質獎勵環境（E）構面之改善，並對過度投入社會（S）構面以掩飾減碳不力之行為祭出「防漂綠懲罰（S-Penalty）」。
本研究之結果證實了台灣授信市場已具備鑑別漂綠行為之能力。建議企業管理階層應避免將資源錯置於非核心永續項目，而主管機關應關注一體適用之法規對一般產業所造成之非預期融資懲罰。
As the global net-zero transition accelerates and Taiwan implements the "Green Finance Action Plan 3.0," Environmental, Social, and Governance (ESG) performance has emerged as a critical determinant of corporate debt financing costs. This study investigates the dynamic impact of ESG scores on firms' interest expense rates under an increasingly stringent anti greenwashing regulatory environment. Utilizing panel data of Taiwanese listed and OTC
companies from 2019 to 2025, this research employs a Time Fixed Effects model for empirical analysis. The empirical findings are as follows: First, overall superior ESG performance significantly lowers corporate financing costs, supporting the "Risk Mitigation Hypothesis." Second, the implementation of Green Finance 3.0 has triggered a significant "Compliance Crowding-out Effect," where exorbitant regulatory compliance and data assurance costs have eroded financial flexibility, offsetting approximately 33.7% of the original ESG interest rate discount (Greenium). Third, this crowding-out effect exhibits
substantial industry heterogeneity, primarily concentrated in "Normal Industries" that lack core climate-risk counterparts, whereas "High-Carbon Industries" demonstrate relative immunity. Fourth, the most breakthrough finding reveals that under strict supervision, banks have initiated a "Precision Pricing" mechanism for high-carbon industries: rewarding substantive Environmental (E) improvements with interest discounts while imposing an
"Anti-Greenwashing Penalty (S-Penalty)" on behaviors that over-invest in the Social (S) dimension to mask inadequate carbon reduction efforts. This study confirms the evolving capacity of Taiwan's credit market to identify greenwashing. It is recommended that corporate management avoid misallocating resources into non-core sustainability projects, and regulatory authorities should address the unintended financing penalties imposed on normal industries due to universal compliance requirements.
Abstract i
中文摘要 ii
Table of content iii
Table of Tables viii
Table of Figure ix
Chapter 1: Introduction 1
1.1 Research Background and Motivation 1
1.1.1 Global Net-Zero Emission Trends and the Rise of ESG Pricing 1
1.1.2 Evolution of Taiwan's Green Finance Policies and Anti-Greenwashing Regulation 1
1.1.3 Academic Pain Points: Compliance Cost Friction and Resource Misallocation 2
1.2 Research Questions 3
1.3 Research Objectives 4
1.4 Structure of the Thesis 4
Chapter 2: Literature Review 5
2.1 Evolution and Substantive Implications of Taiwan's Green Finance Policies 5
2.1.1 Green Finance Action Plan 1.0 (2017-2020): Foundation and Green Energy Policy 6
2.1.2 Green Finance Action Plan 2.0 (2020-2022): ESG Integration and the Prototype of Anti-Greenwashing 7
2.1.3 Green Finance Action Plan 3.0 (2022-Present): Net-Zero Transition, Data Assurance, and Precision Pricing 9
2.2 Review of Related Empirical Literature 11
2.2.1 Overall ESG Performance and the Cost of Debt Financing 11
2.2.2 Climate Transition Risk and the Rise of Precision Pricing 12
2.2.3 Regulatory Shocks, Compliance Costs, and Firm Size Bias 13
2.3 Theoretical Foundation 13
2.3.1 Information Asymmetry and Signaling Theory 13
2.3.2 Risk Mitigation Hypothesis 15
2.3.3 Agency Theory and Over-investment 16
2.3.4 Institutional Theory and Compliance Friction 17
2.4 Limitations of Previous Literature and Research 19
2.5 Hypotheses Development 21
Chapter 3: Research Methodology and Empirical Models 22
3.1 Data Sources and Sample Selection 22
3.2 Variable Definitions and Measurements 24
3.3 Empirical Model Specifications 26
3.3.1 Heterogeneity Analysis 27
3.3.2 Advanced Model: Joint Decomposition of ESG Dimensions and Precision Pricing Test 28
3.3.3 Robustness Check I: Independent Dimension Models to Avoid Multicollinearity 28
3.3.4 Robustness Check II: Non-linear Effect Model 29
Chapter 4: Empirical Results and Analysis 30
4.1 Descriptive Statistics and Correlation Analysis 30
4.1.1 Descriptive Statistics 30
4.1.2 Correlation Analysis 31
4.2 Full Sample Analysis: The Compliance Crowding-out Effect of Green Finance Policy 32
4.2.1 Goodness-of-Fit and Economic Implications of Control Variables 33
4.2.2 Establishment of the ESG Greenium (Main Effect Analysis) 34
4.2.3 The Shock of Green Finance 3.0: Compliance Crowding-out Effect (Interaction Term Analysis) 35
4.3 Heterogeneity Analysis: Impact Differences by Industrial Climate Risk and Firm Size 37
4.3.1 Industry Heterogeneity: High-Carbon vs. Normal Industries 38
4.3.2 Size Heterogeneity: Large Firms vs. SMEs 41
4.3.3 Comprehensive Comparison of Heterogeneity (Forest Plot Analysis) 42
4.4 Climate Transition and Anti-Greenwashing: ESG Dimension Decomposition and Precision Pricing Effect 43
4.4.1 Full Sample Joint Decomposition: Score Dilution and Multicollinearity Defense 44
4.4.2 Precision Pricing in High-Carbon Industries: Environmental (E) Discount and Social (S) Penalty 47
4.5 Robustness Checks 50
4.5.1 Non-linear Effect Analysis 50
4.5.2 Independent Dimension Models 52
4.5.3 Sensitivity Analysis: Exclusion of the Policy Transition Year (2022) 54
Chapter 5: Conclusions and Recommendations 56
5.1 Research Conclusions 57
5.2 Practical and Policy Implications 58
5.2.1 Implications for Corporate Management -Preventing Resource Misallocation 58
5.2.2 Implications for Financial Institutions - Deepening Climate Risk Pricing 59
5.2.3 Implications for Regulatory Authorities -Optimizing Green Finance Policies 59
5.3 Research Limitations and Future Directions 60
References 61
一、Chinese References 61
二、English References 61
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