臺灣博碩士論文加值系統

隨著氣候變遷問題日益嚴重，碳市場作為減少溫室氣體排放的重要工具，受到越來越多的關注。然而，碳市場的波動性和不透明性為投資者帶來了挑戰。本研究的動機在於開發一個可靠的碳市場情緒指標（CMSI），以幫助投資者更好地理解和預測市場動態。
研究方法主要包括四個步驟：首先，收集了從2006年到2023年的81,045條碳市場相關新聞文章，以及歐盟、紐西蘭、加州、韓國、英國和中國六個主要碳市場的價格數據。其次，運用大型語言模型對新聞文本進行情緒分析，並進行加權，生成新聞情緒指數（NSI）。第三，根據各碳市場的運營時間、覆蓋溫室氣體排放比例和碳價收入對六個碳市場進行加權，構建碳市場指數（CMI）。最後，將NSI和CMI進行加權組合，通過時間序列交叉驗證確定最佳權重，最終構建CMSI。
研究結果顯示，CMSI能夠有效捕捉市場情緒變化及其對碳價格的影響。該指標在重大事件（如2008年全球金融危機、2015年巴黎協定簽署等）期間表現出顯著波動，反映了其對市場動態的敏感性。此外，研究還分析了CMSI與全球能源價格指數的關係，發現兩者在某些時期存在同步或滯後相關性。CMSI的滯後效應分析表明，該指標具有一定的預測能力，可為投資者提供早期風險警示。
CMSI的應用將對碳市場管理者、政策制定者和投資者產生重要影響。它可以幫助投資者制定更好的投資策略，降低風險；協助管理者識別潛在的市場不穩定因素，提高市場穩定性；為政策制定者提供即時的市場反饋，制定更有效的政策。

As climate change issues become increasingly severe, carbon markets, as an important tool for reducing greenhouse gas emissions, are receiving more attention. However, the volatility and opacity of carbon markets pose challenges for investors. The motivation for this study is to develop a reliable Carbon Market Sentiment Index (CMSI) to help investors better understand and predict market dynamics. The research methodology mainly includes four steps: First, 81,045 carbon market-related news articles from 2006 to 2023 were collected, along with price data from six major carbon markets: EU, New Zealand, California, South Korea, UK, and China. Second, large language models were used to conduct sentiment analysis on the news texts, which were then weighted to generate a News Sentiment Index (NSI). Third, a Carbon Market Index (CMI) was constructed by weighting the six carbon markets based on their operational time, covered greenhouse gas emission ratio, and carbon price revenue. Finally, the NSI and CMI were weighted and combined, with the optimal weights determined through time series cross-validation, to construct the CMSI. The research results show that the CMSI can effectively capture changes in market sentiment and their impact on carbon prices. The index showed significant fluctuations during major events (such as the 2008 global financial crisis and the signing of the Paris Agreement in 2015), reflecting its sensitivity to market dynamics. Additionally, the study analyzed the relationship between CMSI and the global energy price index, finding synchronous or lagged correlations between the two in certain periods. The lag effect analysis of CMSI indicates that the index has some predictive ability and can provide early risk warnings for investors. The application of CMSI will have important implications for carbon market managers, policymakers, and investors. It can help investors develop better investment strategies and reduce risks; assist managers in identifying potential market instability factors and improve market stability; and provide real-time market feedback for policymakers to formulate more effective policies.

誌謝 I
國立臺北大學112年度第2學期碩士學位論文摘要 II
ABSTRACT III
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1: INTRODUCTION 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Purpose 3
CHAPTER 2: LITERATURE REVIEW 4
2.1 Sustainable Finance 4
2.1.1 Carbon Market 5
2.1.2 Net-Zero Emissions 7
2.2 Sentiment Analysis 8
2.2.1 Levels in Sentiment Analysis 8
2.2.2 Tasks in Sentiment Analysis 9
2.2.3 Approaches in Sentiment Analysis 11
2.2.4 Application in Sentiment Analysis 14
2.3 Pre-trained Language Models in Sentiment Analysis 17
2.4 Sentiment Index 23
2.5 Summary 26
CHAPTER 3: RESEARCH METHODOLOGY 27
3.1 Research Architecture 27
3.2 Research Design 28
3.3 Data Collection 30
3.3.1 News Articles 30
3.3.2 Carbon Price 31
3.4 Data Preprocessing 32
3.4.1 Embedding-Based Similarity Calculation 32
3.4.2 Data Cleaning 33
3.4.3 Price Imputation 33
3.5 Sentiment Analysis Models 34
3.5.1 News Article Sampling and Annotation 34
3.5.2 Deriving Sentiment Scores 34
3.5.3 Weighting Mechanisms for Sentiment Scores 35
3.6 Construction of News Sentiment Index (NSI) 36
3.7 Construction of Carbon Market Index (CMI) 38
3.8 Construction of Carbon Market Sentiment Index (CMSI) 40
CHAPTER 4: EXPERIMENTAL RESULTS AND DISCUSSION 41
4.1 Definition of Key Events 41
4.2 Analysis of News Text and Sentiment Index 42
4.2.1 Number of News Articles and Trends 43
4.2.2 Analysis of News Platforms 44
4.2.3 Comparative Sentiment Annotation Performance 47
4.2.4 Sentiment Scores Trends 50
4.2.5 Results of News Sentiment Index (NSI) 51
4.3 Analysis of Carbon Price and Market Index 52
4.3.1 Descriptive Statistics of Carbon Price 53
4.3.2 Carbon Price Trends 54
4.3.3 Results of Carbon Market Index (CMI) 59
4.4 Analysis of Carbon Market Sentiment Index (CMSI) 61
4.4.1 Results of CMSI 61
4.4.2 Comparison with Energy Market Index 66
4.4.3 Lag Effects and Correlation of CMSI 67
CHAPTER 5: CONCLUSIONS 71
5.1 Conclusion 71
5.2 Research Contribution 71
5.3 Managerial Implication 72
5.4 Research Limitation and Future Work 73
著作權聲明 82

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