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| 研究生： |
麥禾宋 Michael Alfonso Stancati |
|---|---|
| 論文名稱： |
加拿大安大略省北部地區萵苣垂直栽培與自美國進口傳統栽培萵苣之成本效益分析 A Cost-Benefit Analysis of Lettuce Vertical Farming in Northern Ontario Compared with Traditionally Farmed Lettuce Imported from the U.S. |
| 指導教授： |
李慧琳
Lee, Huey-Lin |
| 口試委員: |
吳文傑
Wu, Wen-Chieh 日引 聡 Hibiki, Akira |
| 學位類別： |
碩士
Master |
| 系所名稱： |
社會科學學院 - 應用經濟與社會發展英語碩士學位學程(IMES) International Master's Program of Applied Economics and Social Development(IMES) |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 151 |
| 中文關鍵詞： | 垂直農業 、傳統農業 、成本效益分析 、北安大略 、糧食安全 、地點選擇模型 、萵苣 、供應鏈風險 、受控環境農業 |
| 外文關鍵詞： | vertical farming, traditional farming, cost-benefit analysis, Northern Ontario, food security, location-choice model, lettuce, supply chain risk, controlled environment agriculture |
| 相關次數： | 點閱：11 下載：0 |
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本論文建構了一個地點選擇的成本效益模型，用以評估對於加拿大安大略省北部地區的社區而言，垂直農法是否能成為一種比從美國進口的傳統耕作萵苣更具經濟吸引力的替代方案。立基於 Moghimi 與 Asiabanpour於2023年發表的競爭市場架構文章，本研究比較了安大略省北部蘇聖瑪麗（Sault Ste. Marie）和蒂明斯（Timmins）的垂直農法營運，與加州沙利納斯（Salinas）及亞利桑那州尤馬（Yuma）的傳統農法營運在利潤率與避險吸引力上的差異。本研究並進一步將這個架構擴展，納入一階指數衰減函數的損耗成本，透過序數分級分數評估綜合風險因素，以涵蓋關稅不確定性、投入價格風險，以及跨加拿大和美國邊界的監管差異等指標量化的綜合風險成分。模型參數採用來自加拿大統計局、安大略省能源局、美國勞工統計局、美國能源資訊署及美國農業部國家農業統計局的 2024 基準年數據。在具競爭性的基準價格情境下，從 Yuma 運往 Sault Ste. Marie 的傳統農業供應鏈在八種土地租金與電力成本配置中，均取得最高的地點吸引力分數，其分數為 0.7266；儘管其農地成本較低，且能享有依規模設定的電價，垂直農業在任何情境下皆未達到成本效益，勞動力與能源價格仍是垂直農業成本結構中的兩大要素，但當垂直農業的營收溢價設定為 30% 時，兩個垂直農業地點在全部八種配置中皆進入地點吸引力排名的前兩名。在農地情境下，Sault Ste. Marie 的垂直農業得到最高的地點吸引力分數，達到 0.9017。在所有情境中，垂直農業的綜合供應鏈風險分數約比傳統農業低 64%。敏感度分析顯示，能源密集度是垂直農業成本中影響最大的因素；而若要使兩個垂直農業地點都進入地區吸引力排名的前兩名，所需的最低營收溢價約介於 18% 至 22% 之間。研究結果顯示，北安大略垂直農業的可行性並不只是單純的成本最小化問題，而是同時涉及風險管理、能源效率、經營規模，以及透過在地市場差異化進行營收定位等因素。
This thesis develops a location-choice cost-benefit model to evaluate whether vertical farming (VF) can serve as a more economically attractive alternative to traditionally farmed lettuce imported from the United States for communities in Northern Ontario, Canada. Building on the competitive-market framework of Moghimi and Asiabanpour (2023), the study compares the profit-margin and riskaversion appeal of VF operations in Sault Ste. Marie and Timmins against traditional farming (TF) operations in Salinas, California and Yuma, Arizona, extending the framework to incorporate spoilage costs from a first-order exponential decay function and composite risk components operationalized as ordinal tier scores capturing tariff uncertainty, input price exposure, and regulatory divergence across the CanadaUnited States border. Inputs are parameterized using 2024 reference-year data from Statistics Canada, the Ontario Energy Board, the U.S. Bureau of Labor Statistics, the U.S. Energy Information Administration, and the USDA National Agricultural Statistics Service. Under competitive baseline pricing, the Yuma-to-Sault Ste. Marie TF route achieves the highest Location Appeal score (0.7266) across all eight land-rent and electricity configurations. VF does not reach cost parity in any scenario, with labour and energy representing the two largest VF cost components despite lower rural land costs and scale-specific electricity pricing. With a 30% VF revenue premium, both VF locations move into the top two across all eight configurations, with Sault Ste. Marie VF reaching the highest Location Appeal score (0.9017) in the rural land scenarios. VF carries a composite supply-chain risk score approximately 64% lower than TF across all scenarios. Sensitivity analysis identifies energy intensity as the largest individual VF cost sensitivity, while the minimum premium required for both VF locations to reach the top two ranges from approximately 18% to 22%. The findings suggest that VF viability in Northern Ontario is not solely a cost-minimization question, but one of risk management, energy efficiency, scale, and revenue positioning through local market differentiation.
List of Tables xi
List of Figures xiii
1 Introduction 1
1.1 Background 1
1.2 Research Motivation 3
1.3 Research Problems 4
1.4 Research Objective 5
1.5 Hypothesis 6
1.6 Framework 7
2 Literature Review 9
2.1 The Moghimi and Asiabanpour (2023) Competitive-Market Framework 9
2.1.1 Limitations of the Baseline Framework 10
2.2 Climate Risk and Resource Degradation in Agricultural Systems 11
2.2.1 Soil Degradation and Long-Run Production Stability 11
2.2.2 Water Scarcity and Aquifer Depletion 12
2.3 Vertical Farming Technology and Economics 13
2.3.1 Technology, Productivity, and Operating Costs 13
2.3.2 Commercial Viability and Industry Experience 14
2.4 Environmental Performance of Vertical Farming 14
2.5 Trade Policy, Institutional Risk, and Supply Chain Vulnerability 16
2.5.1 Spoilage, Perishability, and Cold-Chain Losses 16
2.5.2 Supply Chain Disruption and Institutional Risk 17
2.6 Northern Ontario Regional Context 18
3 Methodology: Adapting the Moghimi Framework for a Cross-Border Supply Chain 21
3.1 Introduction to the Baseline Framework 21
3.2 Rationale for Methodological Extension 21
3.3 Core Architectural Similarities (Retained Framework Elements) 22
3.3.1 The Baseline Scale 23
3.3.2 Data Normalization 23
3.3.3 The Final Decision Index 23
3.4 Shift from Localized Comparison to Origin-Destination Modeling 24
3.4.1 The Baseline Approach 24
3.4.2 The Proposed Approach 24
3.4.3 Currency and Policy Integration 25
3.5 Expanding the Cost Function: Logistics and Spoilage 25
3.5.1 Shipping Costs (p5, Q5) 25
3.5.2 Food Loss and Spoilage Costs (p6, Q6) 26
3.6 Rethinking Risk: From Production-Only to Systemic Composite Risk 26
3.6.1 The Baseline Risk Model 26
3.6.2 The Proposed Composite Risk Model 27
3.7 Summary Table of Methodological Choices 30
3.8 Model Framework & Overview 31
3.8.1 Parameters & Variables 32
3.9 Variables, Assumptions, and Parameters 36
3.10 Data Sources, Parameter Derivations, and Assumptions 36
3.10.1 Labour Cost Data 37
3.10.2 Energy Cost Data 41
3.10.3 Land & Rent Cost Data 44
3.10.4 Water Cost Data 48
3.10.5 Lettuce Market Price Data (pri) 51
3.10.6 Shipping Cost Data (p5, Q5) 53
3.10.7 Spoilage Costs 56
3.10.8 Production Risk (PR) 57
3.10.9 Market / Price Risk (MR) 62
3.10.10 Institutional Risk (IR). 63
3.11 Methodological Choices and Analytical Rationale 65
3.11.1 Cross-Sectional vs Time-Series Design 65
3.11.2 Common-Currency Conversion 66
3.11.3 Equal Weighting of Risk Components 66
3.11.4 Spoilage Modeling Assumptions 66
3.11.5 Proxy Data and Comparability 67
3.11.6 Energy Consumption Intensity Assumptions (q2 and Q2) 67
4 Analysis 69
4.1 Overview of the Analysis 69
4.2 Unit Cost Structure 70
4.2.1 Cost Components and Sources 70
4.2.2 Multi-Scenario VF Cost Summary 71
4.2.3 Cost Driver Analysis 73
4.3 Supply-Chain Risk Assessment 75
4.3.1 Risk Score Structure 75
4.4 Location Appeal Scores and Multi-Scenario Comparison 76
4.4.1 The LA Model Formula 76
4.4.2 Full Multi-Scenario LA Score Matrix 77
4.4.3 Scenario A Interpretation – Competitive Baseline 79
4.4.4 Scenario B Interpretation – VF Price Premium 79
4.4.5 Profit Margins and Scale Economics 81
4.5 Sensitivity and Robustness Analysis 83
4.5.1 Profit Weight Sensitivity (𝞱) 83
4.5.2 Labour Intensity Sensitivity 84
4.5.3 Electrical Cost Sensitivity 87
4.5.4 Production Risk Sensitivity 90
4.5.5 Risk Tier Sensitivity 93
4.5.6 ICI Pricing Sensitivity 93
4.5.7 VF Premium Sensitivity 95
4.5.8 Combined Stress Testing 97
4.5.9 Findings Robustness Matrix 97
4.6 Hypothesis Assessment 99
4.6.1 H1 – Profit-Margin Appeal (TF Cost-Competitive) 99
4.6.2 H2 — Risk-Aversion Appeal (VF Lower Risk) 99
4.6.3 H3 — Location-Appeal Superiority (Conditional) 100
4.6.4 H4 — Scale Sensitivity 100
4.6.5 H5 — Energy Price Sensitivity 100
4.7 Summary of Findings 101
5 Conclusion 103
5.1 What Depends on Assumptions and What Does Not 103
5.2 The Conditionality of the VF Advantage 104
5.3 Implications for Policy and Investment 105
5.4 Contributions of the Study 106
5.5 Limitations 107
5.5.1 Data Availability and Proxy Reliance 107
5.5.2 Geographic and Crop Scope 109
5.5.3 Cross-Sectional Design 110
5.5.4 Exclusion of Capital Costs 110
5.5.5 Exchange Rate Volatility 110
5.5.6 Equal Weighting Assumptions 111
5.5.7 Market Price and Demand Assumptions 111
5.5.8 Energy Source and Grid Carbon Intensity 112
5.6 Future Research Directions 112
5.6.1 Waste Heat Integration: Composting and Industrial Symbiosis 112
5.6.2 Data Centre Heat Recovery and Co-location 113
5.6.3 Dynamic and Multi-Year Modeling 113
5.6.4 Empirical Estimation of Market and Institutional Risk Indices 114
5.6.5 Local Distribution Cost Modeling 114
References 117
Appendix A Declaration of AI Use 129
Appendix B FX Rates – CAD to USD (OFX Annual Averages) 131
Appendix C Energy Costs 133
Appendix D Labour Costs 141
Appendix E Water Costs 143
Appendix F Land & Rent Costs 145
Appendix G Shipping Costs & Routes 147
Appendix H Spoilage Costs 149
Appendix I Market Price (Revenue) 151
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