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研究生:謝政哲
研究生(外文):Cheng-Che Hsieh
論文名稱(外文):Operational and Disaggregate Input Efficiencies of International Container Ports: An Application of Stochastic Frontier Analysis
指導教授:陳惠國陳惠國引用關係
指導教授(外文):Huey-Kuo Chen
學位類別:碩士
校院名稱:國立中央大學
系所名稱:土木工程學系
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:86
中文關鍵詞:隨機邊界分析共同邊界技術效率分項投入效率貨櫃港埠
外文關鍵詞:stochastic frontier analysismetafrontiertechnical efficiencydisaggregate input efficiencycontainer ports
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本研究針對2004至2011年期間之國際貨櫃港埠進行產出及分項投入效率分析。其中內含一個產出變量(貨櫃吞吐量)、四個投入變量(如:貨櫃儲存容量、橋式起重機數等)及六個環境變量(如:地理區域、水深、各國經濟成長率等)以反映港埠所面臨的不同條件及環境,並用以參數法中的隨機邊界法評估35座國際貨櫃港埠技術效率、共同邊界技術效率及分項投入效率。 投入效率部分將分別討論各個投入變量。 其中,我們發現在吞吐量產出效率及橋式起重機及車輛投入效率中,亞太群組效率顯著高於歐美群組效率;在貨櫃儲存投入效率則是歐美群組顯著高於亞太群組。 本研究亦發現於車輛投入效率環境變量中,兩類分析皆呈相同的顯著影響。
This research studies output and disaggregate input efficiencies of international container ports during 2004-2011. One output variable (container throughput), four input variables (e.g., container storage capacity, ship-to-shore container cranes) and six environmental variables reflecting different conditions or surroundings confronted by ports (e.g., geographic region, water depth, national economic growth rate) were used to evaluate technical efficiency, meta technical efficiency and disaggregate input efficiency for 35 international container ports using stochastic frontier analysis, a known parametric approach. Input efficiency is discussed separately for each selected input variable. We found the Asia-Pacific group is significantly superior to the Europe/America group in output efficiency for throughput and for input efficiency of ship-to-shore cranes and trucks; the Europe/America group is significantly superior in input efficiency of container storage. Significant effect of surroundings variables on input efficiency of trucks and vehicles was also found exactly the same in two types of analysis.
中文摘要 i
Abstract ii
誌謝 iii
Table of contents iv
List of figures vi
List of tables vii
1 Introduction 9
1.1 Problem description 9
1.2 Research motivation 10
2 Literature Review 12
2.1 Methods for measuring port efficiency 12
2.2 Literature concerning container port efficiency 15
2.3 Literature relevant to input efficiency measurement 19
3 Methodology 20
3.1 Deterministic Frontier Analysis 20
3.2 Stochastic Frontier Analysis 20
3.2.1 BC92 model 20
3.2.2 BC95 model 21
3.2.3 Production functions 21
3.2.4 Heterogeneity 22
3.3 Stochastic Metafrontier Analysis 22
3.4 Disaggregate input efficiency 24
4 Data Collection and Basic Statistical Analysis 26
4.1 Data Collection 26
4.2 Variable selection 27
4.2.1 Input and output variables 27
4.2.2 Environmental variables 27
4.3 Basic Statistical Analysis 27
4.4 Pearson’s Correlation Coefficient 28
5 Result from the BC95 model 29
5.1 portThroughput efficiency 29
5.2 Disaggregate Input Efficiency 33
5.2.1 Input Efficiency with respect to containerStorage 33
5.2.2 Input Efficiency with respect to stsCranes 36
5.2.3 Input Efficiency with respect to truckVehicle 39
5.2.4 Input Efficiency with respect to mobileQuayCranes 41
5.3 Managerial Implications from the BC95 Model 42
6 Result from Metafrontier Analysis 44
6.1 Output Metafrontier Analysis 44
6.1.1 Group-specific Frontier Analysis (in the first stage) 44
6.1.2 Output Metafrontier Analysis (in the second stage) 46
6.2 Input Metafrontier Analysis 49
6.2.1 Group-specific frontier analysis (in the first stage) 49
6.2.2 Input Metafrontier analysis (in the second stage) 51
6.3 Managerial Implications from the Metafrontier Model 54
7 Contributions and Limitations 56
7.1 Relationship between the BC95 Model and Metafrontier Model 56
7.2 Contributions 56
7.3 Limitations and suggestion 57
References 59
Appendix A: Average TE with respect to portThroughput during 2004 - 2011 64
Appendix B: Average Input TE during 2004 - 2011 66
Appendix C: Estimated results with respect to portThroughput for all 35 ports 72
Appendix D: MTR with respect to portThroughput during 2004 - 2011 73
Appendix E: MTE with respect to portThroughput during 2004 - 2011 75
Appendix F: Estimated results with respect to truckVehicle for all 35 ports 77
Appendix G: MTR with respect to truckVehicle during 2004 - 2011 78
Appendix H: MTE with respect to truckVehicle during 2004 - 2011 80
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