本論文包含了三篇與能源經濟相關的實證研究，其主旨分別探討原油期貨市場的投機行為對原油價格的影響，台灣能源系統的脆弱度指標系統的建構，與台灣住宅部門電力需求函數的非線性關係，分述如下：

第一章出於對近年原油越為金融化的同時，原油價格亦出現大幅波動的現象，其背後原因的好奇心。回顧現有文獻，大體以原油市場的供需平衡與期貨市場的投機行為的兩種解釋最為可信，但也尚未形成共識。為提供更多證據以供進一步討論，本文以非交易商的淨多頭部位做為代理變數來捕捉期貨市場的投機行為，這個代理變數相較文獻中常以原油存貨作為投機行為的代理變數更為符合一般大眾的認知。另外，本文建立一個符號限制的因素擴充向量自我回歸模型（Structural Factor Augmented Vector Autoregression Model with Sign Restriction）模型，透過該模型的實證結果發現，儘管原油的供給與需求面因素確實對於油價造成影響，但期貨市場的投機行為亦對油價波動造成了立即且持續長達半年的顯著影響，更是2004~2007年、2011~2013年與2015~2016年間原油價格出現巨幅波動的主要原因。

在2011年福島核能電廠事故之後，台灣的能源政策方向大幅調整，後在2016年後訂定出一套高度依賴再生能源與天然氣，並逐步淘汰核能發電的能源轉型政策。考量能源設施多屬鄰避設施，且其建設往往耗時長久，故能源政策大幅度的轉向可能會導致台灣本已不夠完善的能源系統更為脆弱，故本文第二章以台灣為例，建立一套可全面評估能源系統脆弱程度的模型框架。這套脆弱度指標分成三個主要構面與十五個次指標，分別考量初級能源供應面、能源基礎設施面與最終能源消費面。而實證結果表明，台灣目前處於1990-2017年最脆弱的時期之一，另外提出（1）重新務實的評估當前的能源轉型政策的進程與配套方案；（2）完善現有能源基礎設施；（3）設計相關誘使能源用戶改變行為等建議。

承襲前一章的建議，本文第三章嘗試估計台灣住宅部門的電力需求函數，以提供可靠資訊，讓政府得以建立適當的能源政策工具。然而，現有文獻中對能源需求的估計和政策含義歧異頗大，並常常相互矛盾。舉例來說，文獻中常假設能源需求與其影響變數間的關係唯一常數，亦即線性關係。第三章應用一追蹤資料平滑轉換回歸（Panel Smooth Transition Regression Model）模型，來估計台灣住宅部門電力需求函數的非線性關係。其結果拒絕了電力需求為線性的假設，而該假設往往是目前許多政策工具的理論根據。此外，模型的實證結果也表明電力用戶對價格和收入變化的反應往往是不對稱的。換句話說，價格和收入彈性的數值大小係取決於先前的電價水平的高低。當前一年電價處於較高水平時，電力用戶對價格調漲更為敏感，電力消費量減少更多；當前一年電價處於較低水平時，電力用戶對價格調低的敏感度降低，電力消費量增加較少。這個發現也剛好能夠解釋為什麼近年隨著實質電價的走低，台灣住宅部門單位電力消費量沒有大幅增加的現象。

This doctoral dissertation comprises three empirical studies on energy economics aiming to explore the nature of issues related to “the speculation’s effect on international oil price,” “the vulnerability of Taiwan’s energy system,” and “the asymmetric residential electricity demand in Taiwan,” which are summarized as follows:

In the first chapter, an empirical analysis, motivated by the curiosity of the reasons behind the considerable oil price fluctuation since the oil market’s financialization, is conducted. Reviewing the existing literature, market fundamentals and speculation are the two most popular explanations, but no consensus has been reached on both fronts. To provide more evidence for further discussion, a more convincing proxy variable, the non-commercial traders’ position rather than oil inventory, to mimic actual speculators’ behavior in the oil futures market is suggested, and the Sign Restricted Structural Factor Augmented Vector Autoregression (SFAVAR) model, which includes information as much as possible, is applied. The empirical results suggest that while supply-side and demand-side determinants play their roles in oil price fluctuation, the futures market’s speculation not only did exhibit significantly positive non-accumulated and accumulated effects on the crude oil price change but also dominated the oil price fluctuation during the periods of 2004-07, 2011-13, and 2015-16, respectively.

After the nuclear accident in Fukushima on 11th March 2011, Taiwan entered the energy transition era, featuring a high dependency on renewable energy and natural gas, and a nuclear power phase-out progress. In chapter 2, a framework to evaluate energy vulnerability is established. Taking Taiwan’s unique energy situation into account, the scope of this energy vulnerability indicator, initially proposed by Frondel, Ritter & Schmidt (2009), Frondel & Schimdt (2014), and WEC (2010), is divided into three categories, including primary energy supply, energy-related infrastructure, and final energy consumption, and consists of 15 sub-indicators. The empirical results indicate that Taiwan is now in one of the most vulnerable times during 1990-2017. The suggestion to Taiwan’s government, (1) pragmatically reassess the current energy transition policy, (2) improve energy-related infrastructure, and (3) induce energy users to change their behavior, is made at the end of the chapter.

Following the previous chapter’s suggestion, the third chapter attempts to estimate electricity demand appropriately to provide the government with reliable information to set up proper policy tools. However, the estimation and policy implication of energy demand in existing literature varies widely and often contradict one another. One common drawback is the assumption that the estimated relation between energy demand and its determinants is a constant, i.e., a linear relation. To relax this constraint, a generalized estimation method, the Panel Smooth Transition Regression (PSTR) model recommended by González, Teräsvirta & van Dijk (2005), is employed in this study to analyze the nonlinearity in Taiwan’s residential electricity demand. The results reject the electricity demand’s linearity hypothesis, on which many current policies are based. There does exist evidence that electricity consumers’ response to price and income changes is asymmetric. In other words, the price and income elasticities vary depending on the previous price levels. The electricity consumers respond stronger to a price increase when the previous year’s price is high than to a price decrease when the price is low in the previous year. This finding also explains why the average householder’s electricity consumption in Taiwan’s residential sector has not increased with the decline in real electricity price as much as expected with the assumption of constant price elasticity in recent years.

Preface----------------------------------------------------------1
Chapter 1. The Effect of Speculation in Futures Market on Oil Price------------------------------------------------------------3
1.1 Introduction----------------------------------------------3
1.2 Literature Review-----------------------------------------5
1.3 Modeling--------------------------------------------------8
1.3.1 Theoretical Model---------------------------------------8
1.3.2 Estimation---------------------------------------------10
1.4 Data-----------------------------------------------------12
1.5 Empirical Results----------------------------------------16
1.5.1 Sign Restriction Setting-------------------------------17
1.5.2 Impulse Response---------------------------------------18
1.5.3 Historical Decomposition of the Oil Price--------------21
1.6 Findings-------------------------------------------------24
1.7 References-----------------------------------------------25
Appendix 1.1 Dynamic Factor Analysis-------------------------28
Appendix 1.2 The Dataset for Structural Factors--------------30
Appendix 1.3 Accumulative Impulse Response of Main Variables-34
Chapter 2. The Energy Vulnerability: a Taiwan’s Case---------35
2.1 Introduction---------------------------------------------35
2.2 Literature Review----------------------------------------38
2.2.1 Definition of Energy Security--------------------------38
2.2.2 Energy Security Indicators-----------------------------40
2.3 Methodology----------------------------------------------42
2.3.1 Primary Energy Supply Vulnerability (PEV)--------------44
2.3.2 Infrastructure Vulnerability (IV)----------------------47
2.3.3 End-use Energy Consumption Vulnerability (EEV)---------52
2.3.4 Energy Vulnerability Indicators (EV)-------------------53
2.3.5 Standardization, Weight Setting, and Data--------------53
2.4 Empirical Results----------------------------------------57
2.4.1 PEV Results--------------------------------------------57
2.4.2 IV Results---------------------------------------------61
2.4.3 EEV Results--------------------------------------------66
2.4.4 EV Results---------------------------------------------68
2.5 Findings-------------------------------------------------69
2.6 Reference------------------------------------------------71
Chapter 3. The Asymmetric Residential Electricity Demand in Taiwan: a PSTR Approach-----------------------------------------75
3.1 Introduction---------------------------------------------75
3.2 Literature Review----------------------------------------79
3.3 Modeling-------------------------------------------------82
3.3.1 Theoretical Model--------------------------------------83
3.3.2 Linearity Test-----------------------------------------87
3.3.3 Estimation---------------------------------------------89
3.4 Data-----------------------------------------------------93
3.5 Empirical Results----------------------------------------97
3.6 Findings------------------------------------------------110
3.7 Reference-----------------------------------------------112
Appendix 3.1 Lagrange Multiplier Statistics for Linearity Test-117
Conclusion-----------------------------------------------------119

Chapter 1
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