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研究生: 王雋杰
Wang, Chun-Chieh
論文名稱: 以區塊鏈暨智能合約技術協助台灣內部企業進行碳權交易
Assisting Domestic Enterprises in Taiwan with Carbon Credit Trading Using Blockchain and Smart Contract Technologies
指導教授: 蔡芸琤
Tsai, Yun-Cheng
口試委員: 蔡芸琤
Tsai, Yun-Cheng
陸裕豪
Lu, Yu-Hao
曾思遠
Tseng, Ssu-Yuan
詹皓詠
Chan, Hao-Yung
口試日期: 2024/07/17
學位類別: 碩士
Master
系所名稱: 科技應用與人力資源發展學系
Department of Technology Application and Human Resource Development
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 93
中文關鍵詞: 碳權區塊鏈智能合約碳排放以太坊
英文關鍵詞: Carbon Credit, Blockchain, Smart contract, Carbon emissions, Ethereum
研究方法: 實驗設計法調查研究
DOI URL: http://doi.org/10.6345/NTNU202401408
論文種類: 學術論文
相關次數: 點閱:51下載:4
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  • 19世紀工業革命的興起,人們對於化石燃料的需求大幅增長,導致溫室氣體排放量快速增長,其中二氧化碳為排放量最大之占比。近幾十年來溫室氣體排放所導致全球暖化問題日益明顯,造成生物環境之危脅及經濟層面之影響。因此於1997年12月,多達84個國家及歐盟成員國於日本京都進行《京都議定書》之簽署,並建立碳權交易等相關之機制,以促進碳權交易市場發展及助於各國或企業達成減排及永續發展之目標。因應歐盟所提出碳邊境調整機制(Carbon Border Adjustment Mechanism, CBAM)以及美國所提出的美國清潔競爭法案(Clean Competition Act, CCA)和台灣政府提出於2050達成淨零排放之政策,台灣企業勢必於政策期限內達到碳排放之標準,但在中小企業的部分可能面臨到技術門檻高、市場風險、參與門檻等困境。
    綜合上述,本研究所開發之去中心化碳權交易平台專注於碳邊境調整機制(Carbon Border Adjustment Mechanism, CBAM)和美國清潔競爭法案(Clean Competition Act, CCA)關稅制度,以協助用戶將所產生之總碳排放量,依據這兩項關稅制度進行抵免。透過結合區塊鏈技術及智能合約導入至平台內,其旨為協助台灣中小企業以成本及時間最小化狀態下於碳權市場進行買賣,以解決其於短時間內無法更換綠能設備、碳排放超額之困境,同時也滿足其碳排放目標。

    The rise of the 19th-century Industrial Revolution led to a significant increase in demand for fossil fuels, resulting in a rapid growth of greenhouse gas emissions, with carbon dioxide being the largest contributor. In recent decades, the problem of global warming caused by greenhouse gas emissions has become increasingly evident, resulting in threats to the biosphere and impacts on the economy. Therefore, in December 1997, as many as 84 coun-tries and EU member states signed the Kyoto Protocol in Japan, and estab-lished mechanisms such as carbon trading to promote the development of the carbon trading market and to help countries or enterprises achieve the goal of emission reduction and sustainable development. In response to the Carbon Border Adjustment Mechanism (CBAM) proposed by the European Union, the Clean Competition Act (CCA) by the United States, and the Taiwanese government's policy of achieving net-zero emissions by 2050, Taiwanese companies must meet carbon emissions standards within the policy deadline. However, small and medium-sized enterprises (SMEs) may face challenges such as high technical thresholds, market risks, and participation barriers.
    Based on the above, this research develops a decentralized carbon trading platform focusing on the Carbon Border Adjustment Mechanism (CBAM) and the Clean Competition Act (CCA) tariff systems. It assists users in offsetting their total carbon emissions according to these two tariff systems. Integrating blockchain technology and smart contracts into the platform aims to help Taiwanese SMEs minimize costs and time when trading in the carbon market, addressing the dilemma of not being able to replace green energy equipment in a short time and exceeding carbon emissions while meeting their carbon re-duction targets.

    Acknowledgments i Chinese Abstract ii English Abstract iii Table of Contents v List of Tables vii List of Figures viii 1 Introduction 1 1.1 EU Emissions Trading System (EU ETS) 1 1.2 Carbon Border Adjustment Mechanism (CBAM) 2 1.3 Clean Competition Act (CCA) 3 1.4 Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) 4 2 Literature Review 7 2.1 The Relationship Between Blockchain and Carbon Credits 7 2.2 The Relationship Between Smart Contracts and Carbon Credits 11 2.3 Two Reference Cases 13 2.3.1 Balkrishna Industries Limited Project 14 2.3.2 VTRM Renewable Energy 2 Project 17 2.4 Differences in Roles in The Carbon Credit Trading Market 20 2.4.1 Primary Carbon Credit Trading Market 20 2.4.2 Secondary Carbon Credit Trading Market 20 2.5 Kano Model Analysis Tool 21 2.6 Roles of Taiwanese Companies and Government in Carbon Credits 22 3 Research Methods 28 3.1 Research Context 28 3.2 Research Purpose 30 3.3 System Framework 30 4 Experimental Results 52 4.1 Platform Operation Process 52 4.2 Platform Benefit Verification 65 4.3 Experimental Evidence 68 4.3.1 Analysis of Functional Attributes 69 4.3.2 Function Optimization Sequence 77 5 Conclusion and Future Work 79 5.1 Conclusion 79 5.2 Future Work 80 References 83 1. Chinese Section 83 2. English Section 86

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