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系統識別號 U0026-0206201123294000
論文名稱(中文) 再生能源政策之效益評價模式建構與應用
論文名稱(英文) Construction and Application of Value Evaluation Models for Renewable Energy Policy
校院名稱 成功大學
系所名稱(中) 資源工程學系碩博士班
系所名稱(英) Department of Resources Engineering
學年度 99
學期 2
出版年 100
研究生(中文) 李珣琮
研究生(英文) Shun-Chung Lee
電子信箱 shunchunglee@gmail.com
學號 n4895102
學位類別 博士
語文別 中文
論文頁數 124頁
口試委員 指導教授-施勵行
口試委員-吳榮華
口試委員-廖肇寧
口試委員-王澤世
口試委員-余政達
中文關鍵字 再生能源  實質選擇權  政策效益  學習曲線  灰色預測  政策模擬 
英文關鍵字 Renewable energy  Real option  Policy value  Learning curve  Grey forecasting  Policy simulation 
學科別分類
中文摘要 政府近年將再生能源產業之發展視為主要施政目標之一,以做為達成低碳社會目標的方法,目前雖利用電能收購、設備補助等多項措施協助發展,但就投資而言整體仍不符合成本效益,造成再生能源之發展不如預期。也因此,政府仍持續投入資金及擬定推動措施,以扶持產業成長、降低研發成本與增加產業自製率,以使再生能源產業成為台灣未來與世界競爭之主要產業。如何使政府做出有效率的投資,令產業有效發展並扶持相關產業鏈之形成,又不致於造成政府財政負擔過重,即成為政府與再生能源產業間互動關係中,一項科技評價與管理的重要議題。
本研究主要目的為建構再生能源政策之效益評價模式,衡量再生能源政策之效益價值。研究方法主要以「實質選擇權」理論,建構政策效益之實質選擇權評價模式,量化影響政策效益變動的不確定性、影響因素與管理彈性價值,正確估計政策之實質價值。同時運用「學習曲線」理論,分析發電成本效率的學習效果與下降幅度,並模擬政府補助產生之知識存量的時間遞延效果。此外,針對再生能源技術之成本相關資料不易收集,造成預測未來成本變化誤差較大。此部份將結合「灰色預測模型」理論,建構灰成本效率預測模型,簡化資料收集困難度,同時提升學習曲線之模擬性。
實證分析以風能為例並搭配政策模擬,結果顯示,當以傳統淨現值法做為判斷依據時,因淨現值小於0,顯示目前政府對於風能發展政策屬於不可行的政策規劃。但若以實質選擇權法做為判斷依據時,由於將政策規劃時之不確定性、影響因素與政策制定者運用管理策略所產生的管理彈性價值納入考量,而正確評估出發展風力發電是具有投資效益,亦代表此項措施屬可行之政策規劃。在政策模擬方面,在本研究的參數設定與假設條件下,當收購電價金額逐漸提高時,會使得政府對於風能發展的政策投資報酬率逐漸降低,顯示目前風能之收購電價金額提高不符合投資效益原則。而在外部成本內部化的假設情況下,不論是傳統淨現值法或是實質選擇權法,均能為整體政策帶來正面的效益價值,除顯示此項規劃為可行的措施外,更能突顯出發展再生能源所帶來的政策效益價值。此外,針對灰成本效率預測模型的驗證結果,時間遞延效果為二年為最適的預測模型,表示當時間遞延效果為每二年遞延一次時,除對邊做邊學之學習因子有影響效果外,探索中學習之學習因子會對再生能源發電成本下降率有較明顯的增進效果,也顯示再生能源電能躉購制度對於提升發電成本效率具明顯效果。
研究成果上,除能運用本研究所建構的政策效益評價模式,分別模擬各種不確定性情形與施行政策,亦能運用灰成本效率預測模型改良傳統成本預測模型之準確性與簡化資料收集困難度,以做為再生能源發展政策制定的參考依據。
英文摘要 Due to energy depletion and global warming, the development of green industry has become a major energy policy issue and seeks to achieve the goals of energy conservation and reduction of carbon emissions. But nowadays, renewable energy (RE) investment was still not to achieve the cost-benefit. Therefore, the government must assess the return on investment of its policies in order to determine the effectiveness of those policies. Thus, the development policy of RE industry would not only yield economic and environmental benefits, but also positively impact renewable energy policy planning.
This study presents a value evaluation model that integrates learning curve model and grey forecasting model on renewable power generation technologies into real option analysis (ROA) methods. The proposed model evaluates quantitatively the policy value provided by developing RE in the face of uncertain fossil fuel prices and RE policy-related factors. The economic intuition underlying the policy-making process is elucidated, while empirical analysis illustrates the option value embedded in the current development policy in Taiwan for wind power. In addition, the study employs learning curve to explore the learning effect of power generation, and examines whether firms can actually boost power generation cost efficiency through government subsidies and R&D. Due to the difficulty of obtaining data, the grey system is used to ease data collection difficulties.
The policy value of developing in wind power is assessed by calculating the NPV, in which a traditional valuation model is used. Of which, NPV < 0 reflects that the policy is unprofitable and inappropriate. However, a situation in which the ROA is adopted for assessment yields the opposite result. In addition, this study also compares policy values in terms of internalized external costs and varying feed-in tariff (FIT). Policy simulation results demonstrate that the RE development policy with internalized CO2 emission costs is appropriate policy planning from sustainability point of view. Furthermore, relationship between varying FIT and policy values can be shown quantitatively and appropriate FIT level could be determined accordingly. The analytical results of grey-based cost efficiency (GCE) model indicate that the two-factor cost efficiency curve with a 2-year time lag reaches the goal of minimizing forecasting error and has highly accurate forecasting power. Empirical results also demonstrate that the cost reduction for power generation achieved by knowledge stock required 2 years.
In summary, the proposed policy value evaluation model measures uncertainty and other factors affecting RE industry development policy. The evaluation model can shed light on the value of policy implementation. Additionally, the proposed GCE model can forecast cost efficiencies of new technologies when available data are limited, especially in the case of RE technologies.
論文目次 目錄
摘要…………………………………………………………………………I
Abstract…………………………………………………………………II
致謝…………………………………………………………………………III
目錄…………………………………………………………………………IV
圖目錄……………………………………………………………………VII
表目錄……………………………………………………………………IX
第一章 緒論……………………………………………………………1
1.1 研究動機…………………………………………………………2
1.2 研究目的…………………………………………………………6
1.3 研究流程…………………………………………………………8
1.4 研究範圍與限制…………………………………………………11
第二章 文獻探討………………………………………………………12
2.1 世界主要國家再生能源發展現況………………………………12
2.1.1 我國再生能源發展之歷程、現況與獎勵措施………………12
2.1.2 世界主要國家再生能源政策推動方向………………………17
2.1.3 世界主要國家風能發展政策與策略規劃……………………20
2.2 實質選擇權評價模式……………………………………………28
2.2.1 傳統評價模式…………………………………………………28
2.2.2 實質選擇權評價模式…………………………………………33
2.2.3 實質選擇權評價模式與R&D投資計畫………………………39
2.3 實質選擇權評價模式與再生能源發展策略……………………42
2.3.1 再生能源投資決策與發展策略………………………………42
2.3.2 再生能源發展政策與選擇權種類:以風力發電為例………47
2.4 灰色預測理論……………………………………………………50
第三章 時間遞延之灰成本效率預測模型……………………………53
3.1 灰色預測模型……………………………………………………53
3.2 學習曲線…………………………………………………………54
3.3 時間遞延之灰成本效率預測模型………………………………59
3.4 灰成本效率預測模型之驗證分析………………………………62
第四章 政策效益評價模式之建構……………………………………67
4.1 模式架構…………………………………………………………67
4.2 二項式實質選擇權評價模式……………………………………69
4.3 政策效益評價模式建構…………………………………………75
4.3.1 基本理論架構…………………………………………………76
4.3.2 傳統化石燃料電力成本波動性………………………………78
4.4 影響因素探討……………………………………………………83
第五章 實證分析與政策模擬…………………………………………86
5.1 參數估計…………………………………………………………86
5.2 基本情境分析……………………………………………………89
5.3 政策模擬分析……………………………………………………91
5.4 小結………………………………………………………………95
第六章 結論與建議……………………………………………………96
6.1 結論………………………………………………………………96
6.2 建議………………………………………………………………99
參考文獻…………………………………………………………………101
附錄I 灰色預測模型GM(1,1)推導過程……………………………114
附錄II 政策效益價值試算範例………………………………………118
附錄III 作者簡介……………………………………………………120
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網站部份
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4. 臺灣期貨交易所,2011,http://www.taifex.com.tw/chinese/home.asp。
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