運用階層線性模式分析都市交通運具選擇影響二氧化碳排放之研究-以台南市與高雄市為例

The Study on Urban Traffic Mode Choice Influencing CO2 Mitigation by Hierarchical Linear Modeling analysis–The Case Study in Tainan and Kaohsiung City

吳其為(Chi-Wei Wu)

指導教授 ： 吳永隆

長榮大學/管理學院/土地管理與開發研究所/碩士(2009年)

摘要

過去致力於經濟發展的台灣，對於環境保護仍待努力。然對於全世界重視的議題，我國也努力將二氧化碳排放量降低。目前國內許多大都市中在運具選擇方面仍舊以私人運具做為主要選擇，造成二氧化碳無法有效減少，而減少私人運具使用是達成二氧化碳減量目標之最有效方式。本研究以階層線性模式(Hierarchical Linear Modeling, HLM)分析運具選擇與二氧化碳排放量之關係，依照個人社經背景的不同瞭解選擇運具的偏好，並提供解決二氧化碳排放量過高之方向。研究中發現台南市與高雄市兩地區會因性別、年齡、職業、平均所得、居住地點、婚姻狀況以及子女數不同而造成運具選擇的不同；另外可發現大眾運輸系統、政策因素、汽車因素以及都市交通環境為有效預測因子，並且在考量因素、汽車因素、願付金額以及機車因素的選擇上是具有差異的。最後，利用完整模型建構出各社經資料的迴歸式，其有效預測因子與隨機係數模型分析多處相同，並瞭解各影響因素對民眾選擇運具之正負向關係以及利用跨層級交互作用，瞭解民眾在運具選擇因素上，有部分會受到居住地區不同的影響而有不同的選擇。從研究結果得知台南市與高雄市兩地區的民眾主要以私人運具為主，導致二氧化碳排放量難以降低。對此，本研究建議未來若調升停車費用或油價費率，亦或是提高大眾運輸系統之品質，將有效降低私人運具使用，減少二氧化碳排放量。

關鍵字

運具選擇 ； 二氧化碳排放 ； 階層線性模式

並列摘要

Taiwan has attended to economic development for a long time, ignoring environment protection. While facing this global important issue, our government still makes great effort to reduce CO2 emission. At present in Taiwan, private vehicle is still the primary choice of transportation in most metropolitans. However, Use reduction of private vehicle is the most efficient way to reach the goal of CO2 decrease. This paper employed HLM to analyze the relationship between mode choice and Co2 reduction, which determined by preference based on personal background, and provide the solution to the excessive high volume of CO2 emission. Found in Tainan City and Kaohsiung will be the two regions of gender, age, occupation, per capita income, place of residence, marital status and number of children caused by different choices of the different transportation; the other can be found in the public transport system, policy factors, vehicle factors as well as the urban traffic environment for the effective predictors, and considerations, vehicle factors, willingness to pay the amount as well as the locomotive of choice factors are different.
Finally, we use the full model to construct the different socio-economic information of the regression, and find the effective predictors. And it is the same with random coefficient regression analysis for effective predictors. We understand the impact of factors on the transport of choice of people to the relationship between positive and negative, as well as to explain the effect of the use of cross-level iteractopm, understanding a choice of the people factor in transportation, there will be some of the impact of different living areas and have a different choice.
The results from Tainan City and Kaohsiung City was informed that the two main areas of people with private transport, resulting in difficult to reduce CO2 emissions. In this regard, this study suggests that if the cost of raising parking rates or oil prices, but also improve the public transport system or the quality, will effectively reduce the use of private transport, to reach CO2 mitigation.

並列關鍵字

Mode Choice ； CO2 Mitigation ； HLM

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