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題名 產業部門能源需求與碳排放之驅動力與效率的實證研究
Empirical Analysis on Driving Forces and Technical Efficiency of Energy Demand, Economic Growth and Carbon Emission
作者 單珮玲
Shan, Pei Ling
貢獻者 黃宗煌
Huang, Chung Huang
單珮玲
Shan, Pei Ling
關鍵詞 EKC假說
環境稅
拆解分析
隨機生產邊界
隨機成本邊界
技術效率
EKC hypothesis
Environmental taxes
Decomposition analysis
Stochastic production frontier
Stochastic cost frontier
Technical efficiency
日期 2010
上傳時間 11-Oct-2011 17:03:02 (UTC+8)
摘要 本研究包括3個研究議題。第1個議題旨為估算不同部門別（包括農業、工業、服務業與運輸業）的能源燃燒產生CO2排放之組成因素的貢獻量，係藉由拉氏指數法和算術平均迪氏指數法之加法型態，拆解5種不同的因素（包括：碳密集度、部門結構、能源密集度、人口及經濟規模等），觀察其對於CO2排放變動之影響。本文採用台灣1992-2008年的各部門別的資料作為分析的基礎，研究結果顯示，以上部門的經濟規模對於CO2排放的貢獻呈現巨幅的正向效果；人口因素則呈現微幅的正向或負向的影響；而碳密集度對於CO2排放減量有正面的影響，並發現此乃是構成改善能源結構並導致CO2排放減量的最重要因素；能源密集度因素的影響，除服務業以外，其餘部門均呈現負向影響，此一結果顯示，大部分部門要進一步改善其能源效率頗為困難，是以未來致力於減排的努力，應著重於使用乾淨能源，尤其是以再生能源作為替代能源 (Liaskas et al., 2000)；此外，值得注意的是，部門結構因素對於大部分的產業，如農業、工業和運輸業的CO2排放減量有正向的影響，據此可推論，我國的部門結構已漸趨向於低耗能產業（如服務業）發展。另外，本文採用近似不相關迴歸模型，探討各項政策工具（如環境稅、進口關稅）與經濟變數（如貿易條件及時間趨勢等）透過以上5種不同的組成因素，對於CO2排放變動的影響效果作一分析，其實證結果可供決策者制定減排政策的參考。
第2個議題係為建立節能減排的有效政策工具，須先詳實掌握各項政策工具對節能減排與經濟成長的影響，乃深入回顧相關文獻之理論與實證方法，據以建立適合台灣的3E聯立模型，並進行實證分析，藉以推估多項政策工具（如環境稅、關稅等及能源價格等）與經濟變數（如貿易條件、所得等）對於節能減排與GDP的影響。實證分析結果顯示，台灣之能源消費、CO2排放、及GDP對於各項政策工具與經濟變數之彈性不僅各異其趣，而且有些彈性並非固定不變，可隨時間經過動態調整。
第3個議題係利用台灣1992-2008年之農業、工業、服務業與運輸業等部門別的panel data，仿照Battese and Coelli (1995)提出之隨機邊界(Stochastic Frontier Analysis, SFA)模型，建構隨機生產邊界函數 (stochastic production frontier function)與隨機能源需求函數 (stochastic energy demand frontier function)，利用最大概似法估算出各部門的GDP與能源需求之隨機邊界與技術效率 (technical efficiency, TE)，並據此實證結果提出政策建議。
The thesis includes 3 issues of research. The first research aims at identifying the factors that have influenced change in the level of various sectors (agriculture, industry, service and transport) CO2 emissions from energy use. By means of both Laspeyres index method and the arithmetic mean weight scheme expressed separately in the additive form, the observed changes are analyzed into five different factors: CO2 intensity, structural change, sectoral energy intensity, sectoral employing population and output level. The application study refer to 4 sectors of Taiwan between 1992 and 2008. The obtained decomposition results indicate that the examined sectors the value calculated for the output level effect present the highest value appearing positive contribution of CO2, and the contribution from population is slightly increased or decreased, while CO2 intensity has beneficially influenced the reduction of CO2 emissions, as well as the improvement of fuel mix found to be the most important factor that lead to the reduction of emissions. In most of the examined sectors for the energy intensity factor present positive effect on CO2 emissions, the only exception is service sector showing negative impact on CO2 emissions, which can be stated as Liaskas et al. (2000) that as further improvements in energy efficiency in most sectors become more difficult, efforts to reduce CO2 emissions will be predominantly directed towards the use of clean energy forms and especially towards the deployment of renewable energies. It also should be noted that structural change has positively influenced the abatement of CO2 emissions for the most sectors such as agriculture, industry and transport. We conclude it shifts towards less energy-intensive service sector, due to have negative influenced the observed decrease in CO2 emissions for higher energy use sectors (industry and transport) and agriculture,. In this article, we also use a seemingly unrelated regression to further investigate the policy tools how to change in CO2 emissions level by the five different factors. The results indicate that policymakers may reduce emissions considerably through various policy instruments.
The second issue focuses on initiating effective policy to save energy and reduce emission, one needs to reasonably capture the potential impacts of various policy instruments on energy consumption, CO2 emission and economic growth, the second research, after extensively reviewing the literature, builds a locally ideal empirical model that facilitates the estimation of various policy elasticities. The empirical results indicate that policy elasticities may not only differ from one to the others, but also change dynamically, implying the 3E impacts of some policy instruments might be weakening over time.
The main goal of the third article is to provide a detailed analysis of productivity and efficiency measurement for panel data on four different sectors from Taiwan over the period 1992-2008. We use a stochastic frontier model set by Battese and Coelli (1995) to build a stochastic production frontier function and a stochastic energy demand frontier function, which are estimated by maximum likelihood to obtain a stochastic frontier of GDP and energy demand, as well as technical efficiency. On this empirical results, we suggest that policymaker may simultaneously make top-down policies (green tax reform, increasing environmental tax etc.) and bottom-up policies (fuel price in line with prices of gas in global markets) to increase energy efficiency in different sectors.
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描述 博士
國立政治大學
財政研究所
92255504
99
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0922555041
資料類型 thesis
dc.contributor.advisor 黃宗煌zh_TW
dc.contributor.advisor Huang, Chung Huangen_US
dc.contributor.author (Authors) 單珮玲zh_TW
dc.contributor.author (Authors) Shan, Pei Lingen_US
dc.creator (作者) 單珮玲zh_TW
dc.creator (作者) Shan, Pei Lingen_US
dc.date (日期) 2010en_US
dc.date.accessioned 11-Oct-2011 17:03:02 (UTC+8)-
dc.date.available 11-Oct-2011 17:03:02 (UTC+8)-
dc.date.issued (上傳時間) 11-Oct-2011 17:03:02 (UTC+8)-
dc.identifier (Other Identifiers) G0922555041en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/51630-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財政研究所zh_TW
dc.description (描述) 92255504zh_TW
dc.description (描述) 99zh_TW
dc.description.abstract (摘要) 本研究包括3個研究議題。第1個議題旨為估算不同部門別（包括農業、工業、服務業與運輸業）的能源燃燒產生CO2排放之組成因素的貢獻量，係藉由拉氏指數法和算術平均迪氏指數法之加法型態，拆解5種不同的因素（包括：碳密集度、部門結構、能源密集度、人口及經濟規模等），觀察其對於CO2排放變動之影響。本文採用台灣1992-2008年的各部門別的資料作為分析的基礎，研究結果顯示，以上部門的經濟規模對於CO2排放的貢獻呈現巨幅的正向效果；人口因素則呈現微幅的正向或負向的影響；而碳密集度對於CO2排放減量有正面的影響，並發現此乃是構成改善能源結構並導致CO2排放減量的最重要因素；能源密集度因素的影響，除服務業以外，其餘部門均呈現負向影響，此一結果顯示，大部分部門要進一步改善其能源效率頗為困難，是以未來致力於減排的努力，應著重於使用乾淨能源，尤其是以再生能源作為替代能源 (Liaskas et al., 2000)；此外，值得注意的是，部門結構因素對於大部分的產業，如農業、工業和運輸業的CO2排放減量有正向的影響，據此可推論，我國的部門結構已漸趨向於低耗能產業（如服務業）發展。另外，本文採用近似不相關迴歸模型，探討各項政策工具（如環境稅、進口關稅）與經濟變數（如貿易條件及時間趨勢等）透過以上5種不同的組成因素，對於CO2排放變動的影響效果作一分析，其實證結果可供決策者制定減排政策的參考。
第2個議題係為建立節能減排的有效政策工具，須先詳實掌握各項政策工具對節能減排與經濟成長的影響，乃深入回顧相關文獻之理論與實證方法，據以建立適合台灣的3E聯立模型，並進行實證分析，藉以推估多項政策工具（如環境稅、關稅等及能源價格等）與經濟變數（如貿易條件、所得等）對於節能減排與GDP的影響。實證分析結果顯示，台灣之能源消費、CO2排放、及GDP對於各項政策工具與經濟變數之彈性不僅各異其趣，而且有些彈性並非固定不變，可隨時間經過動態調整。
第3個議題係利用台灣1992-2008年之農業、工業、服務業與運輸業等部門別的panel data，仿照Battese and Coelli (1995)提出之隨機邊界(Stochastic Frontier Analysis, SFA)模型，建構隨機生產邊界函數 (stochastic production frontier function)與隨機能源需求函數 (stochastic energy demand frontier function)，利用最大概似法估算出各部門的GDP與能源需求之隨機邊界與技術效率 (technical efficiency, TE)，並據此實證結果提出政策建議。		zh_TW
dc.description.abstract (摘要) The thesis includes 3 issues of research. The first research aims at identifying the factors that have influenced change in the level of various sectors (agriculture, industry, service and transport) CO2 emissions from energy use. By means of both Laspeyres index method and the arithmetic mean weight scheme expressed separately in the additive form, the observed changes are analyzed into five different factors: CO2 intensity, structural change, sectoral energy intensity, sectoral employing population and output level. The application study refer to 4 sectors of Taiwan between 1992 and 2008. The obtained decomposition results indicate that the examined sectors the value calculated for the output level effect present the highest value appearing positive contribution of CO2, and the contribution from population is slightly increased or decreased, while CO2 intensity has beneficially influenced the reduction of CO2 emissions, as well as the improvement of fuel mix found to be the most important factor that lead to the reduction of emissions. In most of the examined sectors for the energy intensity factor present positive effect on CO2 emissions, the only exception is service sector showing negative impact on CO2 emissions, which can be stated as Liaskas et al. (2000) that as further improvements in energy efficiency in most sectors become more difficult, efforts to reduce CO2 emissions will be predominantly directed towards the use of clean energy forms and especially towards the deployment of renewable energies. It also should be noted that structural change has positively influenced the abatement of CO2 emissions for the most sectors such as agriculture, industry and transport. We conclude it shifts towards less energy-intensive service sector, due to have negative influenced the observed decrease in CO2 emissions for higher energy use sectors (industry and transport) and agriculture,. In this article, we also use a seemingly unrelated regression to further investigate the policy tools how to change in CO2 emissions level by the five different factors. The results indicate that policymakers may reduce emissions considerably through various policy instruments.
The second issue focuses on initiating effective policy to save energy and reduce emission, one needs to reasonably capture the potential impacts of various policy instruments on energy consumption, CO2 emission and economic growth, the second research, after extensively reviewing the literature, builds a locally ideal empirical model that facilitates the estimation of various policy elasticities. The empirical results indicate that policy elasticities may not only differ from one to the others, but also change dynamically, implying the 3E impacts of some policy instruments might be weakening over time.
The main goal of the third article is to provide a detailed analysis of productivity and efficiency measurement for panel data on four different sectors from Taiwan over the period 1992-2008. We use a stochastic frontier model set by Battese and Coelli (1995) to build a stochastic production frontier function and a stochastic energy demand frontier function, which are estimated by maximum likelihood to obtain a stochastic frontier of GDP and energy demand, as well as technical efficiency. On this empirical results, we suggest that policymaker may simultaneously make top-down policies (green tax reform, increasing environmental tax etc.) and bottom-up policies (fuel price in line with prices of gas in global markets) to increase energy efficiency in different sectors.		en_US
dc.description.tableofcontents 摘要 I
ABSTRACT III
目錄 V
表例 VII
圖例 IX
1. 緒論 2
1.1 背景與研究動機 2
1.2 研究內容 7
2. CO2排放組成因素拆解及其與政策變數之關聯 13
2.1 研究動機 13
2.2. 文獻回顧 16
2.2.1 拆解方法概述 16
2.2.2 拆解結果概述 23
2.3. 修正的拆解模式 29
2.3.1 拉氏指數法 29
2.3.2 算術平均迪氏指數法 32
2.4. 資料來源與實證結果 35
2.4.1 資料來源與處理 35
2.4.2 實證分析結果 36
2.5. 組成因素之貢獻量與政策工具的關聯分析 61
2.6 結論與建議 67
2.6.1 結論 67
2.6.2 建議 72
3. 政策工具對CO2排放與經濟的效果分析 77
3.1 緒論 77
3.1.1 研究目的 77
3.1.2 研究方法與架構 80
3.2 文獻回顧 81
3.2.1 政策工具與能源價格概況 82
3.2.2 政策工具與能源價格之3E影響 93
3.3實證模型設定與預期結果 99
3.3.1 模型架構 99
3.3.2 實證模型及其預期結果 101
3.4 資料來源與實證結果 107
3.4.1 資料來源與統計特性 107
3.4.2 時間序列的定態與共整合檢定 108
3.4.3 實證結果 112
3.5 結論與建議 123
4. 能源使用與產出技術效率的評估： 129
4.1 緒論 129
4.2 理論基礎 132
4.2.1 技術效率與配置效率之衡量 132
4.2.2 能源之需求效率的衡量 134
4.2.3 隨機邊界與效率的衡量 135
4.3 資料來源與設算 139
4.4 實證模型設定與結果分析 143
4.4.1 隨機邊界的實證模型設定 143
4.4.2實證結果分析 144
5. 總結 159



表例
表2-1. 挪威各污染物排放之組成因素的貢獻程度：1980至1996 14
表2-2. 排放量之組成因素的拆解文獻彙整表 25
表2-3. 變數定義及資料來源 36
表2-4. 農業部門CO2排放變動量的結構分解：拉氏指數拆解 37
表2-5. 工業部門CO2排放量變動的結構分解－拉氏指數拆解 39
表2-6. 服務業部門CO2排放變動量的結構分解－拉氏指數拆解 41
表2-7. 運輸業部門CO2排放變動量的結構分解－拉氏指數拆解結果 43
表2-8. 農業部門CO2排放成長率的結構分解－迪氏指數拆解結果 45
表2-9. 工業部門CO2排放成長率的結構分解－迪氏指數拆解結果 47
表2-10. 服務業部門CO2排放成長率的結構分解－迪氏指數拆解結果 49
表2-11. 運輸業部門CO2排放成長率的結構分解－迪氏指數拆解結果 51
表2-12. 農業部門CO2排放變動量的結構分解－迪氏指數拆解結果 54
表2-13. 工業部門CO2排放變動量的結構分解－迪氏指數拆解結果 56
表2-14. 服務業部門CO2排放變動量的結構分解－迪氏指數拆解結果 58
表2-15. 運輸業部門CO2排放變動量的結構分解－迪氏指數拆解結果 60
表2-16. 各政策工具之資料來源 62
表2-17. 政策工具對各組成因素之CO2排放貢獻度的影響：工業部門 63
表2-18. 政策工具對各組成因素之CO2排放貢獻度的影響：服務業 63
表2-19. 各部門1992-2008年CO2排放變動量各組成因素平均貢獻值： 68
表2-20. 各部門1992-2008年CO2排放成長率各組成因素平均貢獻值： 69
表2-21. 各組成因素對各部門CO2排放之平均貢獻值： 69
表2-22. 政策工具的有效性分析 72
表3-1. 台灣課徵環境稅費項目之比較 87
表3-2.　變數定義及資料來源與各項變數基本統計量 107
表3-3. GDP函數之ADF單根檢定 110
表3-4. 能源消費函數之ADF單根檢定 111
表3-5. CO2排放量函數之ADF單根檢定 111
表3-6. 各模型殘差項之ADF單根檢定 112
表3-7. 台灣GDP生產函數之推估結果：1992-2007年 112
表3-8. 台灣能源需求函數之推估結果：1992-2007年 113
表3-9. 台灣CO2排放量函數之推估結果：1992-2007年 113
表3-10. GDP、能源消費、及CO2排放量的各項彈性 115
表3-11. 台灣能源消費的所得及政策工具彈性 117
表3-12. 台灣CO2排放之各項彈性：1992-2007年 121
表4-1. 各部門關稅稅收模型推估結果 141
表4-2. 相關變數定義與資料來源 142
表4-3. 台灣各部門別GDP PANEL DATA之SPF推估結果：1992-2008 145
表4-4. 台灣各部門別能源需求PANEL DATA之SCF的推估結果：1992-2008 149


圖例
圖1-1. 本研究的三大議題及其內容 7
圖2-1. 研究架構 15
圖2-2. CO2排放量變動趨勢（以1992年為基期年）－拉氏指數法 17
圖2-3. 農業部門CO2排放變動量的因素組成拆解－拉氏指數法 38
圖2-4. 農業部門CO2排放量之組成因素的貢獻量趨勢－拉氏指數法 38
圖2-5. 工業部門CO2排放變動量的因素組成拆解－拉氏指數法 40
圖2-6. 工業部門CO2排放量之組成因素的貢獻量趨勢－拉氏指數法 40
圖2-7. 服務業部門CO2排放變動量的因素組成拆解－拉氏指數法 42
圖2-8. 服務業部門CO2排放量之組成因素的貢獻量趨勢－拉氏指數法 42
圖2-9. 運輸業部門CO2排放變動量的因素組成拆解－拉氏指數法 44
圖2-10. 運輸業部門CO2排放量之組成因素的貢獻量趨勢－拉氏指數法 44
圖2-11. 農業部門CO2排放成長率的因素組成拆解－拉氏指數法 46
圖2-12. 農業部門CO2排放成長率的組成因素拆解：迪氏指數法 46
圖2-13. 工業部門CO2排放成長率的因素組成拆解－拉氏指數法 48
圖2-14. 工業部門CO2排放成長率的組成因素拆解：迪氏指數法 48
圖2-15. 服務業部門CO2排放成長率的因素組成拆解－拉氏指數法 50
圖2-16. 服務業部門CO2排放成長率的組成因素拆解：迪氏指數法 50
圖2-17. 運輸業部門CO2排放成長率的因素組成拆解－拉氏指數法 52
圖2-18. 運輸業部門CO2排放成長率的組成因素拆解：迪氏指數法 52
圖2-19. 農業部門CO2排放變動量的結構分解－迪氏指數拆解結果 54
圖2-20. 農業部門CO2排放變動量的結構分解－迪氏指數拆解結果 55
圖2-21. 工業部門CO2排放變動量的結構分解－迪氏指數拆解結果 56
圖2-22. 工業部門CO2排放變動量的結構分解－迪氏指數拆解結果 57
圖2-23. 服務業部門CO2排放變動量的結構分解－迪氏指數拆解結果 58
圖2-24. 服務業部門CO2排放變動量的結構分解－迪氏指數拆解結果 59
圖2-25. 運輸業部門CO2排放變動量的結構分解－迪氏指數拆解結果 60
圖2-26. 運輸業部門CO2排放變動量的結構分解－迪氏指數拆解結果 61
圖3-1. 研究架構 81
圖3-2. 歐盟國家總環境稅收占GDP比例 84
圖3-3. 歐盟國家環境稅收占總稅收比例 85
圖3-4. 台灣環境稅費占GDP比例：1994-2007年 88
圖3-5. 國際原油價格趨勢：1991-2008年 89
圖3-6. 台灣與亞洲主要國家柴油價格比較（2009年6月） 90
圖3-7. 台灣與歐盟國家 (EU-6)柴油價格比較（2009年6月） 91
圖3-8. 台灣與歐盟國家 (EU-6)歷年高級無鉛汽油油價之比較 91
圖3-9. 台灣原油進口總值占國內生產毛額比例：1999-2008年 92
圖3-10. 台灣歷年（1990-2007）人均CO2排放量趨勢 92
圖3-11. 台灣歷年CO2排放量及能源消費量趨勢：1998-2007 93
圖3-12. GDP、能源消費量及CO2排放量之關係 99
圖3-13. 時間序列資料的定態性檢定流程 110
圖3-14. 主要國家全球競爭力的變動趨勢 116
圖3-15. 能源消費的環境稅（費）及關稅彈性：1992-2007年 118
圖3-16. 能源消費之國內能源價格指數彈性：1992-2007年 119
圖3-17. 能源消費之貿易條件彈性：1992-2007年 119
圖3-18. 能源消費及CO2排放的所得彈性：1992-2007年 120
圖3-19. CO2排放之環境稅(費)彈性及關稅彈性：1992-2007年 121
圖3-20. CO2排放的能源價格指數彈性：1992-2007年 122
圖3-21. CO2排放的貿易條件彈性：1992-2007年 123
圖4-1. 台灣的能源密集度與能源生產力走勢 131
圖4-2. FARREL的技術、分配與成本效率之衡量 133
圖4-3. 能源需求曲線與需求效率的衡量 134
圖4-4. 各部門1992-2008年之GDP的平均技術效率 147
圖4-5. 各部門不同期間之GDP的平均技術效率之比較 148
圖4-6. 各產業部門1992-2008年之能源需求的平均技術效率 151
圖4-7. 各產業部門不同階段之能源需求的平均技術效率 151		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0922555041en_US
dc.subject (關鍵詞) EKC假說zh_TW
dc.subject (關鍵詞) 環境稅zh_TW
dc.subject (關鍵詞) 拆解分析zh_TW
dc.subject (關鍵詞) 隨機生產邊界zh_TW
dc.subject (關鍵詞) 隨機成本邊界zh_TW
dc.subject (關鍵詞) 技術效率zh_TW
dc.subject (關鍵詞) EKC hypothesisen_US
dc.subject (關鍵詞) Environmental taxesen_US
dc.subject (關鍵詞) Decomposition analysisen_US
dc.subject (關鍵詞) Stochastic production frontieren_US
dc.subject (關鍵詞) Stochastic cost frontieren_US
dc.subject (關鍵詞) Technical efficiencyen_US
dc.title (題名) 產業部門能源需求與碳排放之驅動力與效率的實證研究zh_TW
dc.title (題名) Empirical Analysis on Driving Forces and Technical Efficiency of Energy Demand, Economic Growth and Carbon Emissionen_US
dc.type (資料類型) thesisen
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