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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林子倫 | zh_TW |
dc.contributor.advisor | Tze-Luen Lin | en |
dc.contributor.author | 吳疆泠 | zh_TW |
dc.contributor.author | Chiang Lead Woo | en |
dc.date.accessioned | 2024-02-26T16:23:44Z | - |
dc.date.available | 2024-02-27 | - |
dc.date.copyright | 2024-02-26 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91907 | - |
dc.description.abstract | 因應2050年的全球淨零排放(Net-Zero)目標,英國勞氏海運去碳中心在2022年7月更新之《零碳石油監測報告》中,將精煉油轉型的可及應用列為最優先項目,著重三個面向的討論:技術可及的量產技術(9等級)、獎勵投資的投資可及(6等級),和確保永續溝通的溝通可及(6等級)。 此外,挪威驗船協會2022年報告指出:相對海運重油(Heavy fuel oil)空氣污染物排放,當前三種更少污染的近潔淨海運燃料,有甲醇(Methanol)、氨(Ammonia)和液化天然氣甲烷(Liquefied natural gas, LNG);其中,可再生甲醇(Bio-methanol)之應用發展最易優先普及亦最為安全。以天然氣或氨氣作為燃料的技術,仍要3-8年才能達到量產規模。因此,本研究在海運用油轉型之際,提供3種以成本效益為主軸的空汙減排順序情境建議:相同減排量模式、人均減排量模式,和國家總減排量模式,用以降低決策偏誤,並累積碳交易或碳稅機制的論據基礎。 本研究在「共同但有差異原則」和「比較利益法則」的立論基礎下,將所有樣本國(丹麥、波蘭、瑞典、英國)視為一整體的減排單位,納入肩負不同歷史碳排的排放數據,期能為減排資金點出符合成本效益考量的減排順序。研究方法採用開放 STIRPAT 模型與嶺迴歸模型(Ridge Regression)解決空氣污染物排放之間的前驅(Precursor)效果和共線性(Multicollinearity)。資料取自國際認可之開放資料庫遴選空氣污染排放量,以R軟體分析空汙邊際治理收益,以得出最適空汙邊際治理成本(Marginal costs, MC),進行氣候變遷立法及碳交易市場之論述基礎,盼碳定價能如實反應可歸因空汙的福利成本。 因應2050年的全球淨零排放(Net-Zero)目標,英國勞氏海運去碳中心在2022年7月更新之《零碳石油監測報告》中,將精煉油轉型的可及應用列為最優先項目,著重三個面向的討論:技術可及的量產技術(9等級)、獎勵投資的投資可及(6等級),和確保永續溝通的溝通可及(6等級)。 此外,挪威驗船協會2022年報告指出:相對海運重油(Heavy fuel oil)空氣污染物排放,當前三種更少污染的近潔淨海運燃料,有甲醇(Methanol)、氨(Ammonia)和液化天然氣甲烷(Liquefied natural gas, LNG);其中,可再生甲醇(Bio-methanol)之應用發展最易優先普及亦最為安全。以天然氣或氨氣作為燃料的技術,仍要3-8年才能達到量產規模。因此,本研究在海運用油轉型之際,提供3種以成本效益為主軸的空汙減排順序情境建議:相同減排量模式、人均減排量模式,和國家總減排量模式,用以降低決策偏誤,並累積碳交易或碳稅機制的論據基礎。 本研究在「共同但有差異原則」和「比較利益法則」的立論基礎下,將所有樣本國(丹麥、波蘭、瑞典、英國)視為一整體的減排單位,納入肩負不同歷史碳排的排放數據,期能為減排資金點出符合成本效益考量的減排順序。研究方法採用開放 STIRPAT 模型與嶺迴歸模型(Ridge Regression)解決空氣污染物排放之間的前驅(Precursor)效果和共線性(Multicollinearity)。資料取自國際認可之開放資料庫遴選空氣污染排放量,以R軟體分析空汙邊際治理收益,以得出最適空汙邊際治理成本(Marginal costs, MC),進行氣候變遷立法及碳交易市場之論述基礎,盼碳定價能如實反應可歸因空汙的福利成本。 | zh_TW |
dc.description.abstract | To achieve the 2050 Net Zero carbon emission goal, the Lloyd’s Register Maritime Decarbonisation Hub published the report ‘Zero Carbon Fuel Monitor – July 2022 Update’ to facilitate discussions of fuels transformation. The top priority of progression in scaling technology, stimulating investment and community of ensuring sustainability focuses on the technology readiness level (TRL, 9 levels), investment readiness level (IRL, 6 levels), and community readiness level (CRL, 6 levels). Besides, according to the report published by Det Norske Veritas (DNV), updated cleaner marine energy include Methanol, Ammonia, and LNG; meanwhile, the safest bio-methanol is easily scaled-up rapidly to date. Otherwise, the scaling technology of LNG and ammonia will be available in 3 to 8 years. Hence, this study provides 3 diverse priorities of emissions reduction based on the cost-effectiveness analyses: the same reduction mode, the mean emissions per capita mode, and national total emissions mode to reduce biases of decision-making and accumulate fundamentals of carbon emission trade systems (ETS) and carbon tax schemes. The fundamental of this study is based on the principle of common but differentiated responsibilities and respective capabilities (CBDRRC) and the principle of comparable advantage. Meanwhile, the selected parties are regarded as a whole reduction unit, and the historical emissions are adopted to do cost-effectiveness analyses for reduction priorities of funding. This study selects the extended STIRPAT model with ridge regression to solve the precursor effects and multicollinearity between different emissions. The data is acquired from international recognised datasets and computed by R software version 4.2.0 to get suggestive marginal government spending from the equivalent welfare saving. These analyses can strengthen driving forces of climate change legislation and nestle up actual marine carbon price with a perspective of welfare costs attributable to emissions exposure. The observation period in this study is from 2005 to 2013 which the span of available continuous 9 years after the start of the EU Clean Air Programme for Europe (Café programme). Also, the selective criteria include the LSCI over 40, positions and territories. The selection includes the UK with high LSCI, and the others with middle LSCI: Denmark, Poland, Sweden. The findings of this study contain the cost-effectiveness ratios of welfare costs attributable to marine emission exposure to land-based government spending in the air sectors. Such ratios are 12.50 times in Denmark, 20.67 times in Poland, and 75.88 times in the UK; however, only 0.18 times in Sweden. It can be inferred that (1) the effect of the equivalent government spending on marine reduction in nations with high LCSI was more cost-effective than those of nations with low LCSI; (2) the country with good air quality like Sweden had low welfare saving from emissions reduction under the comparable advantage principle. Additionally, the spending effect in Sweden could significantly reduce welfare costs attributable to emissions exposure, and that in Denmark also had negative effect but insignificantly. In terms of national emissions, only Polish welfare costs attributable to emission exposure had decoupled from its emissions of SO2, PM2.5, N2O, CH4, and CO. Besides, Polish share of fuels use in total energy consumption could significantly decrease welfare loss attributable to emissions exposure. Nonetheless, these OECD 4 suggestively needed to add their spending on tackling marine emissions from fuels combustion, and the most increase was in the UK (2,988 USD per capita), followed by Polish (148 USD per capita), Danish (114 USD per capita), and Swedish (5 USD per capita). Eventually, the suggestive priority of pooling emissions reduction is compiled in Table 4-6 to facilitate global, regional, and national reduction tasks for reference. | en |
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dc.description.tableofcontents | 中文摘要 i Abstract iii List of Abbreviations vi Chapter 1 Introduction 1 Chapter 2 Literature Review 12 2.1 Reduction of welfare cost (Gain of welfare saving) 12 2.2 Reduction of national share of fuels use and impurities of fuel 19 2.3 NOx limits and PM2.5 emissions 24 2.4 SOx limits and PM2.5 emissions 29 2.5 GHG, VOC, and CO emissions 38 2.6 VOCs and CO emissions 43 Chapter 3 Research Design and Evaluation 45 3.1 Modelling 48 3.2 The extended STIRPAT model 55 3.3 Selecting variables 57 3.3.1 Dependent variable 57 3.3.2 Emissions, 𝑻𝒊 58 3.3.3 Energy consumption, 𝑻𝒊 63 3.3.4 Fiscal and economic variables, Ai 64 3.3.5 Population density, 𝑷𝒊 65 3.3.6 Meteorological factors, Ti 66 3.3.7 Summary of variable selection 67 3.4 Multicollinearity adjustment 69 3.5 The test of goodness of fit 70 3.6 Selection of time span and nation 72 3.7 Sampling distribution 73 3.8 Linear regression models and correlations 83 3.9 The meaning of constants, exp(Intercept) 88 3.10 National performance 89 3.10.1 Denmark 89 3.10.2 Poland 90 3.10.3 Sweden 90 3.10.4 The United Kingdom 91 3.11 Recommended governance priority of reduction 92 Chapter 4 Fiscal and Economic Effects 95 4.1 Welfare saving from reduction in shipping, 2005-2013 97 4.2 Cost-effective ratio 99 4.3 Fossil fuel energy consumption (the proportion of total) 100 4.4 Comparative advantage under the CBDRRC scheme 105 Chapter 5 Discussion and Conclusion 110 5.1 The causes of two decarbonization pathways 112 5.2 Decision-making based on cost-efficient analyses 115 5.3 Stakeholders in shipping 118 5.3 Regulatory developments 120 5.4 Conclusions 123 Reference 128 Figure Content Figure 1 1 Potential loss of life averted by reduction of exposure to key risk factors in 2040. 3 Figure 1 2 The size comparison of PM2.5, PM10, a human hair (about 50-70 μm), and fine beach sand (about 90 μm). 4 Figure 1 3 Impacts of emissions in shipping on climate change. 8 Figure 2 1 Algorithm for determining what methodological approach to use for economic impact studies in health. 15 Figure 2 2 CO2 emissions from international marine bunkers, 1971-2019. 20 Figure 2 3 Global Sulphur limits timelines, MARPOL Annex VI. 32 Figure 2 4 Global emission control areas. 34 Figure 3 1 Boxplots: Emissions and WEC. 74 Figure 3 2 Boxplots: FCP and Policy instruments. 76 Figure 3 3 Boxplots: Meteorology variables. 77 Figure 3 4 Time series: WEC and Non-GHG emissions. 79 Figure 3 5 Time series: GHG emissions. 81 Figure 3 6 Time series: Meteorological variables. 82 Figure 4 1 GDP, ORD, and OHT in international shipping, 2005-2013. 95 Figure 4 2 National share of fuel oil consumption in total energy use. 100 Figure 5 1 Stakeholders within the maritime sector. Source: IMO-Norway, 2022. 118 Figure 5 2 Decarbonisation loop of stakeholders. 119 Figure 5 3 Current readiness levels of Methanol, Ammonia, Hydrogen, and CCS. 121 Table Content Table 1 1 AQG levels and interim targets 7 Table 1 2 AQG for NO2, SO2, CO that remain valid 7 Table 2 1 Monetisation approaches for the costs of environmental pollution 13 Table 2 2 PM2.5 annual average health impacts— core set from CAFÉ CBA 15 Table 2 3 The benefit-cost ratios under CAFÉ CBA 17 Table 2 4 Summary of advantages, disadvantages, and best practices for each approach 18 Table 2 5 International, domestic and fishing CO2 emissions by bottom-up method, 2007-2012 22 Table 2 6 The sector share of international, domestic and fishing CO2 emissions, 2007-2012 22 Table 2 7 The share of international, domestic and fishing CO2 emissions in total, 2007-2012 22 Table 2 8 Comparison of emissions from heavy truck and various types of cargo vessels 27 Table 2 9 The NOx limits of MARPOL Annex VI refers to rated engine speed 28 Table 3 1 Parameters based on the STIRPAT model in the air sector 48 Table 3 2 The uniform international shipping proportion in national total, 2005-2013 62 Table 3 3 The approximate international shipping proportion, 2005-2013 63 Table 3 4 The codebook 68 Table 3 5 The tests of goodness-of-fit 71 Table 3 6 The Kruskal-Wallis tests 71 Table 3 7 Sampling distribution: Emissions and WEC 73 Table 3 8 Sampling distribution: FCP and Policy instruments 75 Table 3 9 Sampling distribution: Meteorology variables 77 Table 3 10 The STIRPAT factors by Ridge Regression model—Exponential value, USD 86 Table 3 11 The national priority of marginal WEC per capita under the same amount of reduction 94 Table 4 1 Welfare saving from reduction in shipping, 2005-2013, USD 97 Table 4 2 Welfare costs, government spending, and recommended spending, 2005-2013 98 Table 4 3 The cost-effective ratio of welfare saving to government spending, 2005-2013 99 Table 4 4 The progress on the readiness levels of nearly zero-carbon shipping fuels 103 Table 4 5 Comparison of readiness levels across the supply chain for ammonia, hydrogen, and methanol between December 2021 and June 2022. Source: DNV, 2022. 104 Table 4-6 The priorities of pooling emissions reduction…………………………………...105 Table 5 1 Per ton welfare costs of emissions by Ridge Regression model, 2005-2013, USD 117 Table 5 2 A timeline with key regulatory processes and decisions from the IMO and EU 122 Table 5 3 The suggestive reduction priorities under the same reduction 125 Table 5 4 The suggestive reduction priorities under the mean emissions per capita 125 Table 5 5 The suggestive reduction priorities under national total emissions 126 | - |
dc.language.iso | en | - |
dc.title | 從空汙排放致生之福利成本探討國際海運減排效益 | zh_TW |
dc.title | The Benefits of Reducing Emissions in International Shipping: A Perspective of Welfare Costs Attributable to Air Pollution | en |
dc.type | Thesis | - |
dc.date.schoolyear | 110-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 陳正平;劉哲良 | zh_TW |
dc.contributor.oralexamcommittee | Jen-Ping Chen;Je-Liang Liou | en |
dc.subject.keyword | 防止船舶污染國際公約附錄六防止船舶空氣污染規則,細懸浮微粒暴露,福利成本,開放STIRPAT模型,政府空汙部門治理支出,成本效益分析,決策順序, | zh_TW |
dc.subject.keyword | MARPOL Annex VI,PM2.5 exposure,Welfare costs,the Extended STIRPAT model,Government spending in the air sector,Cost-effectiveness analysis,Priority of policy-making, | en |
dc.relation.page | 139 | - |
dc.identifier.doi | 10.6342/NTU202203663 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2022-09-29 | - |
dc.contributor.author-college | 理學院 | - |
dc.contributor.author-dept | 氣候變遷與永續發展國際學位學程 | - |
顯示於系所單位: | 氣候變遷與永續發展國際學位學程(含碩士班、博士班) |
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