IPCC第五次綜合報告(2014年)指出，若無減緩溫室氣體排放行為，大氣中二氧化碳等主要溫室氣體濃度，預計造成21世紀末全球平均氣溫提高4℃。經濟和人口成長造成的人為溫室氣體排放為氣候暖化的主要原因。聯合國氣候變化綱要公約(UNFCCC)努力促使公約締約方於2015年協議通過2020年之後約束排放的法律架構-國家自定預期貢獻(Intended Nationally Determined Contributions, INDC)，達成全球溫升2℃以下之目標。
最終能源需求部門包括工業、住宅、服務業、運輸及農業等部門，住宅與服務業部門成長速度僅次於工業部門，但政府此二部門規劃的節能措施效果不高，本論文針對住宅與服務業部門進行節電潛力評估。為評估國內住宅與服務業部門節能潛力，需先掌握前述二個部門能源需求量，再進行減碳策略效果與成本評估。國際文獻推估住宅與服務業部門電力需求考量的因素包括經濟面、社會面、環境面或技術面因素，評估模型包括經濟模型、能源工程模型及計量方法。實施的節能策略計有最低能效標準/標章制度、建築能源認證計畫/標章、高效率設備租稅減免及高效率設備研發等。
節能減碳是一個長期且不確定的行動，因其受行為、經濟、技術及環境的影響，它是具有動態和時間遞延的特性，也具有循環性和空間性的特點，本論文以系統模型建置住宅與服務業電力需求推估模型，運用家計生產函數理論與廠商生產函數理論建立前述模型中相關變數推估之理論基礎，電力需求模型之參數考量社會面(人口數)、經濟面(營業所得與能源價格)、技術面(設備種類與效率)及環境面(氣溫)，並納入智慧電表的技術學習效果，使用的資料為本論文進行全國住宅抽樣面訪問卷資料和能源局非生產性質行業能源用戶節約能源查核資料，模擬未來台灣2030年用電設備更換、建築改善、電價調整和能源管理的節電措施效果。
研究結果顯示，以設備減碳成本而言，設備平均減碳成本逐年遞減。住宅冰箱設備平均減碳成本高於照明和空調設備。住商部門建物屋齡和建材、地理位置與氣候的狀況皆為建築能耗重要影響因素，但不同的建築改善措施之減碳成本差異大，如綠屋頂的平均減碳成本低於窗戶玻璃更新和建築外殼改善的平均減碳成本。以部門平均減碳成本而言，服務業設備裝置成本較住宅設備成本高但其減碳量較大。以減碳措施方案而言，住宅部門電價調整方案減碳成本最低但減碳潛力不高；效率提昇方案的減碳成本最高但減碳潛力最大；能源管理系統方案雖減碳潛力低於節能窗戶、建築隔熱及效率提昇方案，但減碳成本低於前三方案；服務業亦有相類似的結果，若能加速智慧型電表基礎建設(Advanced Metering Infrastructure, AMI)以提高智慧電表裝設率，對於住宅與服務業部門減碳相當有助益。以減碳策略效果觀之，電價調整及能源管理屬於減碳無悔政策。
未來亦應進行環境-調適-減緩之間相互影響，如開啟空調的室內溫度、空調設定溫度及環境氣溫改變之間的關係，評估結合氣候變化減緩和調適策略。

The Synthesis Report (2014) of the IPCC’s Fifth Assessment Report pointed out that without additional mitigation efforts beyond those in place today, global warming is more likely than not to exceed 4℃ above pre-industrial levels by 2100, and that increases in anthropogenic greenhouse gas emissions have been driven mainly by economic and population growth. In order to limit global warming to below 2℃ relative to pre-industrial levels, the United Nations Framework Convention on Climate Change (UNFCCC) invited parties to submit their Intended Nationally Determined Contributions (INDCs), to outline the post-2020 climate actions they intend to take under a new international agreement expected to be adopted near the end of 2015.
The sectoral breakdown of final energy consumption typically includes industry, transport, households, services, etc., while the scope of this study covers only the households/residential and service sectors. To assess the carbon reduction potentials of Taiwan’s domestic residential and service sectors, it is necessary to first determine the energy demands for both sectors, then assess the costs and effects of their potential carbon reduction strategies. Based on a thorough review of international literature, the major factors considered while estimating electricity demands of residential and service sectors include economic, social, technical and environmental factors; while models adopted for evaluation include economic models, energy engineering models and econometric methods. Implemented carbon reduction strategies often involved minimum energy efficiency standards/labeling schemes, building energy certification/labeling programs, high-efficiency equipment tax incentives, and high-efficiency equipment research and development activities.
In this study, the systematic model was used to establish electricity demand model of residential and service sectors, with the household production function theory and manufacturer production function theory serving as the theoretical basis for estimating relevant variables considered by the model. Variables considered by the electricity demand model cover the social (number and composition of the population), economic (business incomes and energy prices), technical (equipment types and efficiency), and environmental (temperature) aspects, as well as the learning results of the “artificial intelligence metering (AIM)” technology theory. Data used in this study include data from the energy consumption survey on Taiwan’s residents conducted by this study, and annual report of energy audit for non-manufacturing industries published by the Bureau of Energy of the Ministry of Economic Affairs. These data were used to simulate the effects of equipment replacement, building improvements, and air temperature change in 2030 due to the implementation of carbon mitigation strategies.
The study results showed that for the costs of carbon reduction equipment, the average equipment costs decrease with the decline in equipment investment cost year over year, with the costs lower for higher installation rate; and the costs higher for residential refrigeration equipment versus lighting and air conditioning equipment. The age of residential buildings as well as building materials, geographic locations and weather conditions are all important factors affecting buildings’ energy consumption. But the cost of carbon reductions vary greatly among different building improvement measures, for example, green roofs has an average carbon reduction cost lower than window renovations or improvement projects for building envelop. For average sectoral carbon reduction cost, the equipment installation costs for the service sector are typically higher than the costs of residential equipment, though with higher amount of reduced carbon. Regarding carbon reduction and measures/programs, the residential sector’s electricity tariff adjustment program has the lowest carbon reduction cost, but also low carbon reduction potential; the energy efficiency improvement program has both the highest carbon reduction cost and the highest carbon reduction potential; and the energy management system has a carbon reduction potential lower than energy saving windows, building insulation and efficiency enhancement program, as well as lower carbon reduction costs. For the service sector, similar results can also be observed. Moreover, the accelerated deployment of the AIM system can benefit the carbon reduction progress for both the residential and service sector. Regarding the effect of carbon reduction strategies, the electricity tariff adjustment scheme and energy management measures can both be considered no-regret policies.
Subsequent studies will focus on the relationships between environment, mitigation and adaptation. For example, the interactions between room temperature of air-conditioned rooms, temperature settings of air conditioners and changes in ambient air temperature, in order to assess the effects of integrating climate change mitigation and adaptation strategies.

中文摘要 1
英文摘要 3
第一章 前言 1
1.1研究背景與動機 1
1.2研究架構 3
第二章 文獻回顧 7
2.1家計生產函數理論 7
2.2廠商生產理論 11
2.3系統動態理論 13
2.4能源需求推估方法 17
2.5技術學習曲線 24
2.6國際節能減碳政策趨勢 34
2.7小結 49
第三章 研究方法 51
3.1整體研究方法 51
3.2住宅部門電力需求推估模型 63
3.3服務業部門電力需求推估模型 70
第四章 實證模型建構 87
4.1資料處理 87
4.2模型建構結果 101
第五章 節能方案模擬結果 111
5.1臺灣住宅與服務業節能策略 111
5.2模擬情境設計 114
5.3住宅部門模擬結果 121
5.4服務業部門模擬結果 141
5.5邊際減碳成本 155
5.6小結 160
第六章 結論與建議 163
6.1結論 163
6.2建議 165
參考文獻 參1
附錄 台灣地區家庭建築能源消耗調查問卷

圖目錄
頁次
圖1.2-1 本論文架構 5
圖2.5-1發電技術累積裝置量之邊際成本曲線 25
圖2.6-1 全球各種能源歷年消費量 34
圖2.6-2 全球各種能源供應占比之比較 34
圖2.6-3 全球各部門煤消費占比之比較 35
圖2.6-4 住宅與服務業各項設備節能量(2050年) 36
圖2.6-5 各部門減量潛力與平均減量成本 38
圖2.6-6 各國建築減碳措施減量潛力 39
圖2.6-7 全球減量措施平均成本曲線 42
圖2.6-8 美國住宅使用設備歷年耗能標準 45
圖2.6-9 美國商業歷年能源標準 46
圖3.1-1 部門別歷年最終能源消費趨勢 52
圖3.1-2 部門別歷年最終能源消費占比 52
圖3.1-3 住宅使用能源類別 53
圖3.1-4 臺灣住宅部門各類型建築物電力應用 55
圖3.1-5 服務業使用能源類別 55
圖3.1-6 大用戶(依建築用途分類)之電能消費分布統計 57
圖3.1-7 住宅與服務業電力需求推估架構 61
圖3.1-8 節電方案模擬作業操作流程 63
圖3.2-1 住宅空調電力需求架構圖 66
圖3.2-2住宅照明電力需求架構圖 67
圖3.2-3 住宅冰箱電力需求架構圖 68
圖3.2-4 住宅電視電力需求架構圖 69
圖3.2-5 住宅電腦電力需求架構圖 69
圖3.3-1 服務業(百貨)空調電力需求架構圖 70
圖3.3-2 服務業(旅館)空調電力需求架構圖 72
圖3.3-3 服務業(辦公大樓)空調電力需求架構圖 73
圖3.3-4 服務業(醫院)空調電力需求架構圖 74
圖3.3-5 服務業(學校)空調電力需求架構圖 75
圖3.3-6 百貨照明電力需求架構圖 76
圖3.3-7 旅館照明電力需求架構圖 76
圖3.3-8 辦公大樓照明電力需求架構圖 77
圖3.3-9 醫院照明電力需求架構圖 78
圖3.3-10 學校照明電力需求架構圖 78
圖3.3-11 百貨冷凍冷藏能源需求架構圖 79
圖3.3-12 旅館冷凍冷藏能源需求架構圖 80
圖3.3-13 辦公大樓冷凍冷藏能源需求架構圖 81
圖3.3-14 醫院冷凍冷藏能源需求架構圖 82
圖3.3-15 學校冷凍冷藏電力需求架構圖 82
圖3.3-16 辦公大樓飲水電力需求架構圖 83
圖3.3-17 辦公大樓電腦電力需求架構圖 84
圖3.3-18 學校電腦電力需求架構圖 85
圖4.1-1 家戶平均年收入 90
圖4.1-2 百貨公司各項設備用電占比 98
圖4.1-3 旅館各項設備用電占比 99
圖4.1-4 辦公大樓各項設備用電占比 99
圖4.1-5 醫院各項設備用電占比 100
圖4.1-6 學校各項設備用電占比 100
圖4.2-1 戶數與平均人口數 102
圖4.3-1 住宅部門2014年空調等設備電力需求量占比推估值 109
圖5.1-1 台灣無風管冷氣機MEPS與日本和美國標準之比較(2011年) 112
圖5.2-1 FARRELL效率前緣圖 117
圖5.2-2 有無屋頂綠化下進入室內的熱流量變化 120
圖5.3-1 住宅空調各項節能措施節電率 125
圖5.3-2 高效率照明設備節電率 127
圖5.3-3住宅照明設備能源管理節電率 129
圖5.3-4 高效率冰箱設備節電率 131
圖5.3-5 住宅電視設備能源管理節電率 133
圖5.3-6 住宅電腦設備能源管理節電率 135
圖5.3-7住宅能源管理系統節電率 137
圖5.3-8住宅建築節能節電率 139
圖5.3-9住宅綠屋頂節電率 140
圖5.4-1服務業空調電價調整節電率 143
圖5.4-2服務業空調設備效率提昇節電率 145
圖5.4-3服務業照明設備效率提昇節電率 148
圖5.4-4服務業冷凍冷藏設備效率提昇節電率 150
圖5.4-5 服務業能源管理各情境節電率 152
圖5.4-6 服務業建築改善各情境節電率 154
圖5.5-1 2016年住宅邊際減量成本曲線 155
圖5.5-2 2020年住宅邊際減量成本曲線 156
圖5.5-3 2025年住宅邊際減量成本曲線 156
圖5.5-4 2030年住宅邊際減量成本曲線 157
圖5.5-5 2016年服務業邊際減量成本曲線 157
圖5.5-6 2020年服務業邊際減量成本曲線 158
圖5.5-7 2025年服務業邊際減量成本曲線 158
圖5.5-8 2030年服務業邊際減量成本曲線 159

表目錄
頁次
表2.1-1家計生產函數研究文獻彙整表 10
表2.2-1 廠商生產函數研究文獻彙整表 13
表2.3-1系統動態模型研究文獻彙整表 16
表2.4-1 國際能源需求推估方法彙整表 23
表2.5-1 學習曲線相關文獻使用的評估模型與結果彙整表 32
表2.6-1 住宅與服務業部門節能技術 36
表2.6-2 各節能技術里程碑 37
表2.6-3 各部門溫室氣體減量措施建議 38
表2.6-4 各國建築減碳措施與預期減量效果 40
表2.6-5 IEA 25個能源效率提昇政策建議 43
表2.6-6 建築能源認證計畫執行措施建議 43
表2.6-7 中國與美國住宅與服務業部門節能設計標準規範項目之比較 47
表2.6-8 中國綠建築標籤制度 48
表3.1-1 服務業大用戶申報能源消費量(2012年) 56
表3.1-2電力需求推估模型的設備別與關鍵變數對照表 61
表3.2-1臺灣2050 能源供需模擬器模擬情境設定 64
表4.1-1住宅部門問卷回收樣本數 89
表4.1-2 不同職業經濟戶長全戶年所得情況 91
表4.1-3 各所得經濟戶長職業占比 91
表4.1-4 各經濟戶長職業全戶所得占比 92
表4.1-5 各種設備普及率-依地區及建築物類型分 93
表4.1-6 各種設備平均台數-依地區及建築物類型分 93
表4.1-7 高效率建材及設備使用情況-依建築物型態分 94
表4.1-8 五大耗能服務業分布區域 96
表4.1-9 服務業各地區總能源費用 97
表4.1-10 服務業各地區總樓地板面積 97
表4.1-11 服務業各地區單位面積能源支出 98
表4.2-1 戶數與每戶平均人口數推估值 102
表5.1-1 德國、英國與臺灣建築節能法規之比較 112
表5.2-1 電價調整方案 115
表5.2-2 綠屋頂私人和社會成本效益 120
表5.3-1 住宅空調設備容量推估值 121
表5.3-2 基準情境住宅空調設備效率假設值 121
表5.3-3 住宅空調設備基準情境電力需求推估值 122
表5.3-4 五種電價情境下節電量 122
表5.3-5 五種電價情境下CO2削減量 123
表5.3-6 高效率空調設備效率提昇率 124
表5.3-7 高效率空調設備節電量 124
表5.3-8 高效率空調設備CO2削減量 124
表5.3-9 高效率空調設備CO2平均減碳成本 124
表5.3-10 住宅能源管理(HEMS)-住宅空調節電量 125
表5.3-11 住宅能源管理(HEMS) CO2削減量 125
表5.3-12 住宅照明燈具設備容量推估值 126
表5.3-13 基準情境住宅照明燈具效率假設值 126
表5.3-14 住宅住宅照明燈具基準情境電力需求推估值 126
表5.3-15 住宅照明效率提昇率假設情境 127
表5.3-16 高效率照明設備節電量 127
表5.3-17 高效率照明設備CO2削減量 128
表5.3-18 高效率照明設備CO2平均減碳成本 128
表5.3-19 住宅照明設備HEMS措施節電量 128
表5.3-20 住宅照明設備能源管理CO2削減量 129
表5.3-21 住宅冰箱設備容量推估值 129
表5.3-22 基準情境住宅冰箱設備效率假設值 130
表5.3-23 基準情境住宅冰箱設備電力推估值 130
表5.3-24 住宅高效率冰箱節電量 130
表5.3-25 住宅高效率冰箱CO2削減量 131
表5.3-26 住宅高效率冰箱CO2平均減碳成本 132
表5.3-27 住宅電視設備容量推估值 132
表5.3-28 基準情境住宅電視設備效率假設值 132
表5.3-29 住宅電視設備基準情境電力需求推估值 133
表5.3-30 住宅電視設備節電量(HEMS) 133
表5.3-31 住宅電視設備CO2削減量 (HEMS) 134
表5.3-32 住宅電腦設備容量推估值 134
表5.3-33 基準情境住宅電腦設備效率假設值 134
表5.3-34 住宅電腦設備基準情境電力需求推估值 135
表5.3-35 能源管理系統-住宅電腦節電量(HEMS) 135
表5.3-36 能源管理系統-住宅電腦CO2削減量 136
表5.3-37 住宅能源管理系統節電量 (HEMS) 136
表5.3-38 住宅能源管理系統CO2削減量(HEMS) 137
表5.3-39 住宅能源管理系統CO2平均減碳成本 137
表5.3-40 建築節能效果假設 138
表5.3-41 建築節能設施安裝率假設 138
表5.3-42 住宅部門節能窗戶節電量估計 139
表5.3-43 住宅部門節能窗戶CO2削減量估計 139
表5.3-44 住宅部門隔熱牆體節電量估計 139
表5.3-45 隔熱牆體CO2減量估計 140
表5.3-46 綠屋頂節電量估計 140
表5.3-47 綠屋頂CO2減量估計 141
表5.3-48 綠屋頂單位減碳成本 141
表5.4-1 服務業GDP成長率 141
表5.4-2 服務業空調電力需求(基準情境) 142
表5.4-3服務業空調電價方案節電量 142
表5.4-4服務業空調電價方案CO2削減量 143
表5.4-5 服務業空調設備效率提昇率 144
表5.4-6 服務業空調設備效率提昇節電量 144
表5.4-7 服務業空調設備效率提昇CO2削減量 146
表5.4-8 服務業空調設備效率提昇CO2平均減碳成本 146
表5.4-9 服務業照明電力需求(基準情境) 147
表5.4-10 服務業照明設備效率提昇率 147
表5.4-11 服務業照明設備效率提昇節電量 147
表5.4-12 服務業照明設備效率提昇CO2削減量 148
表5.4-13 服務業照明設備效率提昇CO2平均減碳成本 149
表5.4-14 學校冷凍冷藏電力需求(基準情境) 149
表5.4-15 服務業冷凍冷藏設備效率提昇率 150
表5.4-16 服務業冷凍冷藏設備效率提昇節電量 150
表5.4-17 服務業冷凍冷藏設備效率提昇CO2削減量 151
表5.4-18 學校冷凍冷藏設備效率提昇平均CO2減量成本 151
表5.4-19 服務業能源管理節電量 151
表5.4-20 服務業能源管理CO2削減量 152
表5.4-21 服務業能源管理平均CO2減量成本 153
表5.4-22 節能窗戶和建築隔熱節電率假設 153
表5.4-23 服務業建築改善坪數 154
表5.4-24 服務業建築改善節電量預估 154
表5.4-25 服務業建築改善CO2減量預估 155





 

1.ABB, 2011. Trends in global energy efficiency.
2.Adelman, Irma and Zvi Griliches, 1961. On an Index of Quality Change. Journal of the American Statistical Association 56, 535-548.
3.Alexander, A. J. and B. M. Mitchell, 1985. Measuring Technological Change of Heterogeneous Products. Technological Forecasting and Social Change 27, 161-195.
4.American Council for an Energy-Efficient Economy (ACEEE) and Global Buildings Performance Network (GBPN), 2012. Building Energy Efficiency Policies in China-Status Report.
5.Anable, Jillian, Christian Brand, Martino Tran, and Nick Eyre, 2012. Modelling Transport Energy Demand: A Socio-technical Approach. Energy Policy 41, 125-138.
6.Anable, Jillian, Christian Brand, Nick Eyre, Russell Layberry, Noam Bergman, Neil Strachan, Tina Fawcett, and Martino Tran, 2011. Energy 2050 - WG1 Energy Demand Lifestyle and Energy Consumption. UK energy Research Center Working Paper.
7.Anisimova, Nataliya, 2011. The Capability to Reduce Primary Energy Demand In EU Housing. Energy and Buildings 43, 2747-2751.
8.Baral, Ranju, George C. Davis, And Wen You, 2011. Consumption Time in Household Production: Implications for the Goods-Time Elasticity of Substitution. Economics Letters 112, 138-140.
9.Barker, Terry and Jonathan Köhler, 1998. Equity and Ecotax Reform in the EU: Achieving a 10 Percent Reduction in CO2 Emissions Using Excise Duties. Fiscal Studies 19(4), 375-402.
10.Barreto, Leonardo and Socrates Kypreos, 2004. Endogenizing R&D and Market Experience in the "Bottom-Up" Energy-systems ERIS model. Technovation 24(8), 615-629.
11.Barton, John, Sikai Huang, David Infield, Leach Matthew, Ogunkunle Damiete, Jacopo Torriti, and Thomson Murray, 2013. The Evolution of Electricity Demand and the Role for Demand Side Participation, in Buildings and Transport. Energy Policy 52, 85-102.
12.Bass, Frank M., 1980. The Relationship Between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations. Journal of Business 53, 51-67.
13.Becker, Gary S. (1981, Enlarged ed., 1991). A Treatise on the Family. Cambridge, MA, Harvard University Press.
14.Becker, Gary S. and Gilbert Ghez, 1975. The Allocation of Time and Goods over the Life Cycle. New York, Columbia University Press.
15.Becker, Gary S., 1965. A Theory of the Allocation of Lime. Economic Journal 75(299), 493-517.
16.Becker, Gary S., 1987. Family. The New Palgrave: A Dictionary of Economics v. 2, 281-286.
17.Berry, Steven, James Levinsohn and Ariel Pakes, 1995. Automobile Prices in Market Equilibrium. Econometrica 63(4), 841-890.
18.Bosetti, Valentina, Carlo Carraro, De Cian Enrica, Emanuele Massetti, and Massimo Tavoni, 2013. Incentives and Stability of International Climate Coalitions: an Integrated Assessment. Energy Policy 55, 44-56.
19.Boston Consulting Group, 1972. Perspectives on Experience, Boston, Mass.
20.Brasingtona, David M. and Diane Hite, 2005. Demand for Environmental Quality: a Spatial Hedonic Analysis. Regional Science and Urban Economics 35, 57- 82.
21.Brown, Rich, Sam Borgeson, Jon Koomey, and Peter Biermayer, 2008. U. S. Building-Sector Energy Efficiency Potential. Environmental Energy Technologies Division, Ernest Orlando Lawrence Berkeley National Laboratory University of California, Berkeley, California.
22.Castelnuovo, Efrem and Marzio Galeotti, 2003. Learning by Doing vs Learning by Researching in a Model of Climate Change Policy Analysis. FEEM Working Paper 11.
23.Chan, Andy T. and Victor C. H. Yeung, 2005. Implementing Building Energy Codes in Hong Kong: Energy Savings, Environmental Impacts and Cost. Energy and Buildings 37, 631-642.
24.Chaturvedi, Vaibhav, Jiyong Eom, Leon E. Clarke, and Priyadarshi R. Shukl, 2014. Long Term Building Energy Demand for India: Disaggregating End Use Energy Services in an Integrated Assessment Modeling Framework. Energy Policy 64, 226-242.
25.Chen, Wenying, 2005. The Costs of Mitigating Carbon Emissions In China: Findings from China MARKAL-MACRO Modeling. Energy Policy 33, 885-896.
26.Chen, Wenying, Hualin Li, and Zongxin Wu, 2010. Western China Energy Development and West to East Energy Transfer: Application of the Western China Sustainable Energy Development Model. Energy Policy 38, 7106-7120.
27.Chirinko, Robert S. and Debdulal Mallick, 2011. The Elasticity of Derived Demand, Factor Substitution, and Product Demand: Corrections to Hicks' Formula and Marshall's Four Rules. Labour Economics 18, 708-711.
28.Climate Protection Partnership Division in the U.S. Environmental Protection Agency’s Office of Atmospheric Programs, 2008. Reducing Urban Heat Islands: Compendium of Strategies Green Roofs. U.S. Environmental Protection Agency.
29.Coe, David T. and Elhanan Helpman, 1995. International R&D Spillovers. European Economic Review 39(5), 859–887.
30.Coe, David T., Elhanan Helpman and Alexander Hoffmaister, 1997. North-South R&D Spillovers. Economic Journal 107(440), 134–149.
31.Colpier, Ulrika Claeson and Deborah Cornland, 2002. The Economics of the Combined Cycle Gas Turbine-an Experience Curve Analysis. Energy Policy 30, 309-322.
32.Comodi, G., L. Cioccolanti, and M. Gargiulo, 2012. Municipal Scale Scenario: Analysis of an Italian Seaside Town with MARKAL-TIMES. Energy Policy 41, 303-315.
33.Cong, Rong-Gang, 2013. An Optimization Model for Renewable Energy Generation and Its Application in China: A Perspective of Maximum Utilization. Renewable and Sustainable Energy Reviews 17, 94-103.
34.Court, Andrew T., 1939. Hedonic Price Indexes with Automotive Examples. In the Dynamics of Automotive Demand, ed. Charles F. Roos 99-117. New York General Motors.
35.Davis, George C. and Wen You, 2011. Not Enough Money or Not Enough Time to Satisfy the Thrifty Food Plan? a Cost Difference Approach for Estimating a Money-Time Threshold. Food Policy 36, 101-107.
36.Davis, George C. and Wen You, 2013. Estimates of Returns to Scale, Elasticity of Substitution, and the Thrifty Food Plan Meal Poverty Rate from a Direct Household Meal Production Function. Food Policy 43, 204-212.
37.Desroches, Louis-Benoit, Karina Garbesi, Colleen Kantner, Robert Van Buskirk, and Hung-ChiaYang, 2013. Incorporating Experience Curves in Appliance Standards Analysis. Energy Policy 52, 402-416.
38.Diana, Urge-Vorsatz, Ksenia Petrichenko, Miklos Antal, Maja Staniec, Michael Labelle, Eren Ozden, and Elena Labzina, 2012. Best Practice Policies for Low Energy and Carbon Buildings: Based on Scenario Analysis. Research Report Prepared by the Center for Climate Change and Sustainable Policy (3CSEP) for the Global Best Practice Network for Buildings. Central European University (CEU) and Global Buildings Performance Network.
39.Dowlatabadi, Hadi (Eds.), 1998. Challenges in Climate Policy Assessment. Hadi Dowlatabadi.
40.Dubois, Marie-Claude and Åke Blomsterberg, 2011. Energy Saving Potential and Strategies for Electric Lighting in Future North European, Low Energy Office Buildings: A Literature Review. Energy and Buildings 43, 2572-2582.
41.Eichhammer, Wolfgang, Tobias Fleiter, Barbara Schlomann, Stefano Faberi, Michela Fioretto, Nicola Piccioni, Stefan Lechtenbohmer, Andreas Schuring, and Gustav Resch, 2009. Study on the Energy Savings Potentials in EU Member States, Candidate Countries and EEA Countries. The European Commission Directorate-General Energy and Transport, Brussels, Belgium.
42.Energy Efficiency and Renewable Energy (EERE), 2008. Energy Efficiency Trends in Residential and Commercial Buildings. U.S. Department of Energy.
43.Engelbrecht, Hans-Jürgen, 1997. International R&D Spillovers among OECD Economies. Applied Economics Letters, 4(5), 315–319.
44.Eom, Jiyong, Leon Clarke, Son H. Kim, Page Kyle, and Pralit Patel, 2012. China’s Building Energy Demand: Long-Term Implications from a Detailed Assessment. Energy 46, 405-419.
45.Erika, Mata, Angela Sasic Kalagasidis, and Johnsson Filip, 2013. A Modelling Strategy for Energy, Carbon, and Cost Assessments of Building Stocks. Energy and Buildings 5, 100-108.
46.European Environment Agency (EEA), 2008. Greenhouse Gas Emission Trends and Projections in Europe.
47.Forrester, Jay W., 1969. Urban Dynamics. Waltham, MA: Pegasus Communications.
48.Fouquet, Roger, 2012. The Demand for Environmental Quality in Driving Transitions to Low-Polluting Energy Sources. Energy Policy 50, 138-149.
49.Gabriele, Comodi, Cioccolanti Luca, Polonara Fabio, and Brandoni Caterina, 2012. Local Authorities in the Context of Energy and Climate Policy. Energy Policy 51, 737-748.
50.GfK Home Audit/CLG, 2011. English House Condition Survey, English Housing Survey.
51.Giraudet, Louis-Gaëtan, Céline Guivarch, and Phillipe Quirion, 2011. Comparing and Combining Energy Saving Policies: Will Proposed Residential Sector Policies Meet French Official Targets? Centre International de Recherche sur l’Environnement et le Développement (C.I.R.E.D.) working paper.
52.Gómez, Antonio, Zubizarreta Javier, Dopazo César, and Fueyo Norberto, 2011. Spanish Energy Roadmap to 2020: Socioeconomic Implications of Renewable Targets. Energy 36, 1973-1985.
53.Gordon, Robert J., 1990. Macroeconomics. 5th ed. Glenview, IL: Scott, Foresman.
54.Goulder, Lawrence H. and Koshy Mathai, 2000. Optimal CO2 Abatement in the Presence of Induced Technological Change. Journal of Environmental Economics and Management 39, 1-38.
55.Goulder, Lawrence H. and Stephen H. Schneider, 1999. Induced Technological Change and the Attractiveness of CO2 Abatement Policies. Resource and Energy Economics 21, 211-253.
56.Gouveia, João Pedro, Patrícia Fortes, and Júlia Seixas, 2012. Projections of Energy Services Demand for Residential Buildings: Insights from a Bottom-Up Methodology. Energy 47, 430-442.
57.Greenpeace, 2010. Energy Revolution - A Sustainable World Energy Outlook. European Renewable Energy Council (EREC), Brussels, Belgium.
58.Griliches, Zvi, 1979. Issues In Assessing the Contribution of R&D to Productivity Growth. Bell Journal of Economics, 10(1), 92–116.
59.Gritsevskyi, Andrii and Nebojša Nakićenovi, 2000. Modeling Uncertainty of Induced Technological Change. Energy Policy 28, 907-921.
60.Grossman, Michael, 1972. On the Concept of Health Capital and the Demand for Health. Journal of Political Economy, 80, 223-255.
61.Grübler, Arnulf and Sabine Messner, 1988. Technological Change and the Timing of Mitigation Measures. Energy Economics 20, 495-512.
62.Hamermesh, Daniel S., 2008. Direct Estimates of Household Production. Economics Letters 98, 31-34.
63.Harvey, Danny L. D., 2010. Energy and the New Reality 1: Energy Efficiency and the Demand for Energy Services. Earthscan, London, and Washington, DC.
64.Ibenholt, Karin, 2002. Explaining Learning Curves for Wind Power. Energy Policy 30 (13), 1181-1189.
65.Intergovernmental Panel on Climate Change (IPCC), 2007. Climate Change 2007: Mitigation of Climate Change.
66.Intergovernmental Panel on Climate Change (IPCC), 2007. Climate Change 2007: Summary for Policymaker of the AR4 Synthesis Report.
67.Intergovernmental Panel on Climate Change (IPCC), 2014. Fifth Assessment Report (AR5).
68.International Energy Agency (IEA), 2002. Potential for Building Integrated Photovoltaics. Photovoltaic Power System Programme. Paris, France.
69.International Energy Agency (IEA), 2006. Energy Technology Perspecves-Scenarios to 2050.
70.International Energy Agency (IEA), 2010a. Energy Performance Certification of Buildings: A Policy Tool to Improve Energy Efficiency.
71.International Energy Agency (IEA), 2010b. Energy Technology Perspectives 2010: Pathways to a Clean Energy System.
72.International Energy Agency (IEA), 2010c. Policy Pathways: Energy Performance Certification of Buildings.
73.International Energy Agency (IEA), 2011a. 25 Energy Efficiency Policy Recommendations.
74.International Energy Agency (IEA), 2011b. Technology Roadmap Energy-efficient Buildings: Heating and Cooling Equipment.
75.International Energy Agency (IEA), 2011c. World Energy Outlook.
76.International Energy Agency (IEA), 2012a. Energy Technology Perspectives 2012: Pathways to a Clean Energy System.
77.International Energy Agency (IEA), 2012b. Key World Energy Statistic.
78.International Energy Agency (IEA), 2014a. Key World Energy Statistic.
79.International Energy Agency (IEA), 2014b. World Energy Outlook.
80.Izquierdo, Salvador, Carlos Montanes, César Dopazo, and Norberto Fueyo, 2011. Roof-top Solar Energy Potential Under Performance-Based Building Energy Codes: the Case of Spain. Solar Energy 85, 208-213.
81.Jaffe, Adam B., 1986. Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profit and Market Value. American Economic Review, 76(5), 984–1001.
82.Jia, N. X., R. Yokoyama, Y. C. Zhou, and Z. Y. Gao, 2001. A Flexible Long-Term Load Forecasting Approach Based on New Dynamic Simulation-GSIM. International Journal of Electrical Power & Energy Systems 23(7), 549-556.
83.Jiang, BinBin, Wenying Chen, Yuefeng Yu, Lemin Zeng, and David Victor, 2008. The Future of Natural Gas Consumption in Beijing, Guangdong and Shanghai: An Assessment Utilizing MARKAL. Energy Policy 36, 3286-3299.
84.Junginger, M., A. Faaij, and W. C. Turkenburg, 2005. Global Experience Curves for Wind Farms. Energy Policy 33, 133-150.
85.Kesickin, Fabian and Gabrial Anandarajah, 2011. The Role of Energy-Service Demand Reduction in Global Climate Change Mitigation: Combining Energy Modelling and Decomposition Analysis. Energy Policy 39, 7224-7233.

86.Khanna, Nina Z., John Romankiewicz, Nan Zhou and Wei Feng, and Qing Ye, 2014. From Platinum to Three Stars: Comparative Analysis of U.S. and China Green Building Rating Programs. 2014 ACEEE Summer Study on Energy Efficiency in Buildings.
87.Kim, Seunghyok, Jamin Koo, Chang Jun Lee, and En Sup Yoon, 2012. Optimization of Korean Energy Planning for Sustainability Considering Uncertainties in Learning Rates and External Factors. Energy 44, 126-134.
88.Kouvaritakis, Nikolaos, Antonio Soria, and Stephane Isoard, 2000. Modelling Energy Technology Dynamics: Methodology for Adaptative Expectations Model with Learning-By-Doing and Learning-By-Searching. International Journal of Global Energy Issues 14 (1-4), 104-115.
89.Kristin, De Lucia, 2013. Domestic Economies and Regional Transition: Household Multicrafting and Lake Exploitation in Pre-Aztec Central Mexico. Journal of Anthropological Archaeology 32, 353-367.
90.Ladd, George W. and Veraphol Suvannunt, 1976. A Model of Consumer Goods Characteristics. American Journal of Agricultural Economics 58, No. 3, 504-510.
91.Laitner, John A. and Alan H. Sanstad, 2004. Learning-By-Doing on Both the Demand and the Supply Sides: Implications for Electric Utility Investments in a Heuristic Model. International Journal of Energy Technology and Policy 2 (1-2), 142-152.
92.Laitner, John A. S., Steven Nadel, R. Neal Elliott, Harvey Sachs, and A. Siddiq Khan, 2012. The Long-Term Energy Efficiency Potential: What the Evidence Suggests. American Council for an Energy-Efficient Economy, Washington, D. C.
93.Lindman, Åsa and Patrik Söderholm, 2012. Wind Power Learning Rates: a Conceptual Review and Meta-Analysis. Energy Economics 34, 754-761.
94.Liu, K. and B. Baskaran, 2003. Thermal Performance of Green Roofs Through Field Evaluation. National Research Council of Canada, NRCC-46412.
95.Lundvall, Bengt-Åke, 1988. Innovation as an Interactive Process: User-Producer Interaction to the National System of Innovation. In: Dosi, G. et al. (eds.), Technical Change and Economic Theory, London, Pinter Publishers.
96.Madden, Gary, Scott J. Savage, and Paul Bloxham, 2001. Asian and OECD International R&D Spillovers. Applied Economics Letters, 8(7), 431–435.
97.Martin, Weiss, Junginger Martin, K. Patel Martin, Blok Kornelis, 2010. A Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change 77, 411-428.
98.Martinsen, Thomas, 2010. Global Technology Learning and National Policy-an Incentive Scheme for Governments to Assume the High Cost of Early Deployment Exemplified by Norway. Energy Policy 38, 4163-4172.
99.Mattsson, Niclas and Clas-Otto Wene, 1997. Assessing New Energy Technologies Using an Energy System Model with Endogenized Experience Curves. International Journal of Energy Research 21, 385-393.
100.McDowall, Will, Gabrial Anandarajah, Paul E. Dodds, and Julia Tomei, 2012. Implications of Sustainability Constraints on UK Bioenergy Development: Assessing Optimistic and Precautionary Approaches with UK MARKAL. Energy Policy 47, 424-436.
101.McKinsey & Company, 2009. Pathway to Low Carbon Economy.
102.Mercure, J. F., H. Pollitt, U. Salas, P. Chewpreecha, A. M. Foley, P. B. Holden, and N. R. Edwards, 2014. The Dynamics of Technology Diffusion and the Impacts of Climate Policy Instruments in the Decarbonisation of the Global Electricity Sector. Energy Policy 73, 686-700.
103.Messner, Sabine, 1997. Endogenized Technological Learning in an Energy Systems Model. Journal of Evolutionary Economics 7(3), 291-313.
104.Mills, Evan, 2011. Building Commissioning: A Golden Opportunity for Reducing Energy Costs and Greenhouse Gas Emissions. Energy Efficiency 4, 145 - 173.
105.Molburg, John C., Gale A. Boyd, and J. D. Cavallo, 1995. A Learning Functions of New Electric Power Generating Technologies. Argonne National Laboratory working paper.
106.Morgan, Bazilian, Holger Rogner, Mark Howells, Sebastian Hermann, Arent Douglas, Dolf Gielen, Pasquale Steduto, Alexander Mueller, Paul Komor, Richard S.J. Tol, and Kandeh K. Yumkella, 2011. Considering the Energy, Water and Food Nexus: Towards an integrated modelling approach. Energy Policy 39, 7896-7906.
107.National Institute for Environmental Studies (NIES), 2008. A Dozen of Actions Towards Low-Carbon Societies (LCSs).
108.National Institute for Environmental Studies (NIES), 2009. Japan Roadmaps towards Low-Carbon Sociees (LCSs).
109.Nemet, Gregory F., 2009. Interim Monitoring of Cost Dynamics for Publicly Supported Energy Technologies. Energy Policy 37, 825-835.
110.Newell, Richard G., 2000. Incorporation of Technological Learning into NEMS Buildings Modules. Energy Information Administration.
111.Nguyen, Van Haa, Kantb Shashi, and Virginia Maclaren, 2008. Shadow Prices of Environmental Outputs and Production Efficiency of Household-Level Paper Recycling Units in Vietnam. Ecological Economics 65, 98-110.
112.Nuria, Garrido-Soriano, M. Rosas-Casals, A. Ivancic, and M. D. Alvarez-del Castillo, 2012. Potential Energy Savings and Economic Impact of Residential Buildings under National and Regional Efficiency Scenarios: A Catalan case study. Energy and Buildings 49, 119-125.
113.Pakes, Ariel, Steven Berry, and James A. Levinsohn, 1993. Applications and Limitations of Some Recent Advances in Empirical Industrial Organization: Prices Indexes and the Analysis of Environmental Change. American Economic Review 83, 240-246.
114.Pantong, K., S. Chirarattananon, and P. Chaiwiwatworakul, 2011. Development of Energy Conservation Programs for Commercial Buildings based on Assessed Energy Saving Potentials. In: 9th Eco-Energy and Materials Science and Engineering Symposium. Energy Procedia, Chiang Rai, Thailand. 70-83.

115.Park, Walter G., 1995. International R&D Spillovers and OECD Economic Growth. Economic Inquiry, 33(4), 571–591.
116.Pew Center, The 10-50 Soluon：Technologies and Polices for a Low-Carbon Future, 2004.
117.Pukšec, Tomislav, Brian Vad Mathiesen, and Neven Duic, 2013. Potentials for Energy Savings and Long Term Energy Demand of Croatian Households Sector. Applied Energy 101, 15-25.
118.Radhi, H., 2009. Can Envelope Codes Reduce Electricity and CO2 Emissions in Different Types of Buildings in the Hot Climate of Bahrain? Energy 34, 205-215.
119.Sanquist, Thomas F., Heather Orr, Bin Shui, and Alvah C. Bittner, 2012. Lifestyle Factors in U. S. Residential Electricity Consumption. Energy Policy 42, 354-364.
120.Sartori, Igor, Bjørn Jensen Wachenfeldt, and Anne Grete Hestnes, 2009. Energy Demand in the Norwegian Building Stock: Scenarios on Potential Reduction. Energy Policy 37, 1614-1627.
121.Schimschar, Sven, Kornelis Blok, Thomas Boermans, and Andreas Hermelink, 2011. Germany’s Path Towards Nearly Zero-Energy Buildings-Enabling the Greenhouse Gas Mitigation Potential in the Building Stock. Energy Policy 39, 3346-3360.
122.Sebastiaan, Deetman, F. Hof Andries, Pfluger Benjamin, P. vanVuuren Detlef, Girod Bastien, and J. van Ruijven Bas, 2013. Deep Greenhouse Gas Emission Reductions in Europe: Exploring Different Options. Energy Policy 55, 152-164.
123.Seebregts, Ad, Tom Kram, Gerrit Jan Schaeffer, and Alexandra Bos, 2000. Endogenous Learning of Technology Clusters in a MARKAL model of the Western European Energy System. Int. J. Global Energy Issues14, 289-319.
124.Seifritz, Walter, 2003. An Endogenous Technological Learning Formulation for Fossil Fuel Resources. International Journal of Hydrogen Energy 28, 1293 - 1298.
125.Siller, Thomas, Michael Kost, and Dieter Imboden, 2007. Long-term Energy Savings and Greenhouse Gas Emission Reductions in the Swiss Residential Sector. Energy Policy 35, 529-539.
126.Sterman, John D., 1994. Learning in and about Complex Systems. System Dynamics Review10 (2-3, Summer-Fall), 291-330.
127.Sterman, John D., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin McGraw-Hill.
128.Sterman, John D., 2002. All Models Are Wrong: Reflections on Becoming a Systems Scientist. System Dynamics Review18, No. 4, 501-531.
129.Strachan, Neil, Steve Pye, and Ramachandran Kannan, 2009. The Iterative Contribution and Relevance of Modelling to UK Energy Policy. Energy Policy 37, 850-860.
130.Streimikiene, Dalia and Andzej Volochovic, 2011. The Impact of Household Behavioral Changes on GHG Emission Reduction in Lithuania. Renewable and Sustainable Energy Reviews 15, 4118-4124.
131.Sustainable Development Solutions Network, 2014. Pathway to Deep Decarbonization.
132.Taylor, Simon, Andrew Peacock, Phil Banfill, and Li Shao, 2010. Reduction of Greenhouse Gas Emissions from UK Hotels in 2030. Building and Environment 45, 1389-1400.
133.Tommerup, H. and S. Svendsen, 2006. Energy Savings in Danish Residential Building Stock. Energy and Buildings 38, 618-626.
134.Trajtenberg, Manuel, 1990. Economic Analysis of Product Innovation. Cambridge, MA: Harvard University Press.
135.U.S. Department of Energy (DOE), 2008. Energy Efficiency Trends in Residential and Commercial Buildings.
136.UK Energy Research Centre (ERC), 2009. Decarbonising the UK Energy System: Accelerated Development of Low Carbon Energy Supply Technologies.
137.UK HM Treasury, 2006. Stern Review on the Economics of Climate Change.
138.US Environmental Protection Agency (EPA), 2008. Reducing Urban Heat Islands: Compendium of Strategies-Green Roofs.
139.Vardimon, Ran, 2011. Assessment of the Potential for Distributed Photovoltaic Electricity Production in Israel. Renewable Energy 36, 591-594.

140.Wang, Pengyun, Juan Li, Huijie Li, and Shouzi Zhang, 2011. Differences in Learning Rates for Item and Associative Memories Between Amnestic Mild Cognitive Impairment and Healthy Controls. Behavioral and Brain Functions, 9-29.
141.Weiner, Mark, 2009. Energy Use in Buildings and Industry: Technical Appendix. Committee on Climate Change.
142.Weiss, Martin, Martin Junginger, Martin K. Patel, and Blok Kornelis, 2010. A Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change 77, 411-428.
143.Wene, Class-Otto, 2000. Experience Curves for Energy Technology Policy. OECD/ IEA, Paris, France.
144.Wu, Jung-Hua and Yun-Hsun Huang, 2014. Electricity Portfolio Planning Model Incorporating Renewable Energy Characteristics. Applied Energy 119, 278–287.
145.Yue Cheng-Dar and Guo-Rong Huang, 2011. An Evaluation of Domestic Solar Energy Potential in Taiwan Incorporating Land Use Analysis. Energy Policy 39, 7988-8002.
146.Zhou, Nan, David Fridley, Michael McNeil, Nina Zheng, Virginie Letschert, Jing Ke, and Yamina Saheb, 2011. Analysis of Potential Energy Saving and CO2 Emission Reduction of Home Appliances and Commercial Equipments in China. Energy Policy 39, 4541-4550.
147.工業技術研究院綠能與環境研究所，2012。住商建築能源消費調查。
148.工業技術研究院綠能與環境研究所，2014。「運用溫室氣體模擬模型進行區域節能與減碳策略分析專案工作計畫。環境保護署委辦計畫。
149.工業技術研院綠能與環境研究所，2013。2050能源供需情境模擬器及其再生能源供給情境。
150.內政部，2003。綠建築推動方案(核定本)。
151.內政部，2008。生態城市綠建築推動方案(核定本)。
152.內政部營建署，2011。住宿類建築物節約能源設計技術規範。
153.內政部營建署，2012。智慧建築設計技術參考規範。
154.王怡舜、湯宗益，2000。企業總體經濟環境決策支援之研究以系統動力學為建模方法。中華管理評論 3 (4), 67-82。
155.主計總處，2014。家計收支調查。
156.台灣電力公司綜合研究所，2010。99年臺灣地區家用電器普及狀況調查報告。
157.台灣綜合研究院，2010。99年度住宅與商業部門能源消費調查研究。經濟部能源局委辦計畫。
158.石大明、鄭澄寅，2007。應用限制理論於策略規劃模型之系統動態分析。中華民國品質學會第43屆年會暨第13 屆全國品質管理研討會。
159.江育賢，2012。綠屋頂的功能及技術概論-水泥沙漠中的生態綠洲。台灣綠屋頂暨立體綠化協會。
160.李怡婷，2005。大眾運輸導向發展策略對捷運站區房地產價格之影響分析。國立成功大學都市計劃研究所碩士論文。
161.李皇照、林素娟，1996。雙薪夫妻家庭外食消費支出之分析。農業經濟半年刊 59, 159-173。
162.周婉玲，2009。休閒與健康內生化之環境稅的雙紅利效果分析。中國文化大學經濟學研究所博士論文。
163.林唐裕、葛復光、謝智宸、曾禹傑、林忠漢，2010。我國2010-2030 年住宅及服務業部門能源需求推估。台電工程月刊739期（3月號）， 75-91。
164.林師模、留偉榮、張四立，2005。機車車用油品品質管制策略及其對我國空氣品質影響之探討。經濟成長與永續發展模型研討會：系統動態與一般均衡方法之應用，中央大學台灣經濟研究發展中心。
165.林麗甄，2012。運用系統動態方法評估再生能源推動機制對電力部門之影響。能源知識庫，經濟部能源局。
166.邱家守，2011。桃園煉油廠營運策略研究。國立清華大學高階經營管理碩士在職專班碩士論文。
167.哈冀連、蔡水安，2014。台灣智慧電網市場發展趨勢之研究。遠東學報第二十七卷第四期。
168.胡士文，1997。特徵隱含價格及需求函數之推估－以台灣省肉類、魚介類為例。台灣土地金融季刊 3(4), 219-239。
169.張四立，2009。談能源價格合理化。2009年05月能源月刊。
170.張家榮，2004。能源效率對臺灣汽車價格之影響－特徵價格模型之應用。國立中央大學產業經濟研究所碩士論文。
171.許志義、吳仁傑，2014。論電力需量反應與虛擬電廠發展趨勢。經建專論，國家發展委員會。
172.許菁君，2004。台灣冷氣機產業技術創新與能源政策。國立中央大學產業經濟研究所博士論文。
173.陳宗玄，2010。臺灣家庭外食消費支出影響因素之研究-世代分析之應用。朝陽學報 15, 45-68。
174.陳重仁，2012。集合住宅綠建築外殼節能技術與案例。集合住宅節能技術研討會。
175.陶在樸，1999。系統動態學-直擊第五項修鍊奧秘。五南圖書出版公司。
176.陸怡蕙、施國珍，2005。國際研發外溢效果對生產力的貢獻—臺、日、韓三國之比較分析。中央研究院經濟研究所，臺灣經濟預測與政策 36:1, 103-30。
177.彭作奎，1992。台灣嬰兒奶粉營養素含量與價格差異之研究。農業經濟半年刊 51, 19-77。
178.黃以倫，1997。浮動油價機制對台灣汽柴油市場之影響─系統動態方法之應用。國立成功大學資源工程研究所碩士論文。
179.黃國倉，2011。綠建築的屋頂綠化。科學發展460期。
180.黃萬傳、劉欽泉、楊新怡，1999。台灣地區帶殼鮮蛋特徵價格之計量分析。農業金融論叢 41, 1-48。
181.經濟部能源局，2012。中華民國101年能源平衡表編製說明。
182.經濟部能源局，2013。中華民國102年能源統計手冊。
183.經濟部能源局，2014。中華民國103年能源統計手冊。
184.經濟部能源局，2014。能源需要表。
185.經濟部能源委員會，1994。探索能源世界。臺北:經濟部能源委員會。
186.鄒德祥，1996。國產自用轎車特徵價格之研究。私立東海大學企業管理所碩士論文。
187.綠色生產力基金會，2012。2011年非產性質行業能源查核年報。
188.劉雅莉，2002。特徵價格理論之應用－澳洲葡萄酒之分析。國立中興大學行銷學系碩士論文。
189.劉蕙瑜，2013。再生能源階層成本學習曲-以台灣風力與太陽光電為例。國立清華大學工業工程與工程管理學系碩士論文。
190.蔡孟航，1997。台灣乳品特徵價格之研究。國立中興大學農業經濟學研究所碩士論文。
191.鄭志忠，1993。臺灣地區汽車享用價格函數之推估。私立逢甲大學經濟學研究所碩士論文。
192.蕭代基，1987。旅遊成本法：投入需求函數或產出需求函數。七十六年中國經濟學會年會論文集，161-173。
193.龐雅文和蕭代基，2007。健康效果對最適環境稅之影響。中央研究院經濟研究所，經濟論文 35(1), 1-19。
194.蘇炯龍，2009。永續發展評估模式之延伸探討-能源要素融入的應用。國立臺北大學自然資源與環境管理研究所博士論文。
195.Coast, R. H., 1937. The Nature of the Firm. http://www3.nccu.edu.tw/~jsfeng/CPEC11.pdf
196.DIGITIMES中文網，中央監控機制催生智慧節能建築 http://www.digitimes.com.tw/tw/iot/shwnws.asp?CnlID=15&cat=70&id=0000332297_MBI3IHOV8IJPK886FP6LL&ct=1&PACKAGEID=7275#ixzz3RQ9jNaIx
197.http://book.tndais.gov.tw/Other/2013green/6%E7%B6%A0%E5%B1%8B%E9%A0%82%E7%9A%84%E5%8A%9F%E8%83%BD%E5%8F%8A%E6%8A%80%E8%A1%93%E6%A6%82%E8%AB%96.pdf(下載時間：2015年1月2日)
198.http://www.abri.gov.tw/utcPageBox/CHIMAIN.aspx?ddsPageID=CHIMDCA&EprHadDBID=65&DBID=1384
199.德國聯邦經濟及科技部 (Bundesministerium für Wirtschaft und Technologi)， 2011。Typology Approach for Building Stock Energy Assessment。
200.工業技術研究院，臺灣2050能源供需情境模擬器，http://my2050.twenergy.org.tw/
201.中央氣象局，2015。氣候統計http://www.cwb.gov.tw/V7/climate/dailyPrecipitation/dP.htm。
202.內政部戶政司，內政部統計月報，http://sowf.moi.gov.tw/stat/month/list.htm(下載日期 2014. 03.05)
203.內政部建築研究所，http://www.abri.gov.tw/utcPageBox/CHIMAIN.aspx?ddsPageID=CHIMDCA&EprHadDBID=65&DBID=1384(2015.03.07下載)
204.王塗發，如何促進能源價格合理化，http://www.peoplenews.tw/news/ee249839-6fbb-4093-8c26-c250b9918819
205.台灣電力公司，http://www.taipower.com.tw/content/new_info/new_info-a02.aspx?LinkID=22 (下載時間：2015年2月10日)
206.台灣電力公司，http://www.taipower.com.tw/content/new_info/new_info-a02.aspx?LinkID=22 (下載時間：2015年2月10日)
207.黃郁棻、葉芳瑜、蔡宛栩、蘇芳儀，2014。各國需量反應推動現況及對我國電力政策建議。國家實驗研究院科技政策研究與資訊中心。http://thinktank.stpi.narl.org.tw/Chinese/Column/Pages/FR20141103.aspx。