| 中文摘要 |
本研究目的為探討氣候條件、車輛變項、區域因素與餐飲物流碳排放之關係。研究採質化研究之次級資料分析方法，彙整氣象資料與W食品公司餐飲物流相關碳排放數據，並以SPSS統計軟體之敘述性統計分析、單因子變異數分析與多重比較、以及相關性分析與多元迴歸統計方法，探討餐飲物流之碳排放決定因素，並進而推論碳排放預測性分析模型。研究結果顯示，W食品公司不同營業區之氣溫、雨量、車輛里程數、車輛配送戶數、銷額、及餐飲物流之碳排放均有顯著不同（p＜0.05）；雨量、車輛里程數、及車輛配送戶數對餐飲物流之碳排放與銷額具顯著相關（p＜0.05）。進一步以多元迴歸統計方法推論餐飲物流之碳排放量預測模型，所得餐飲物流碳排放量模型方程式為：【餐飲物流碳排放量本研究目的為探討氣候條件、車輛變項、區域因素與餐飲物流碳排放之關係。研究採質化研究之次級資料分析方法，彙整氣象資料與W食品公司餐飲物流相關碳排放數據，並以SPSS統計軟體之敘述性統計分析、單因子變異數分析與多重比較、以及相關性分析與多元迴歸統計方法，探討餐飲物流之碳排放決定因素，並進而推論碳排放預測性分析模型。研究結果顯示，W食品公司不同營業區之氣溫、雨量、車輛里程數、車輛配送戶數、銷額、及餐飲物流之碳排放均有顯著不同（p＜0.05）；雨量、車輛里程數、及車輛配送戶數對餐飲物流之碳排放與銷額具顯著相關（p＜0.05）。進一步以多元迴歸統計方法推論餐飲物流之碳排放量預測模型，所得餐飲物流碳排放量模型方程式為：【餐飲物流碳排放量＝0.323×車輛里程數＋0.225×銷額＋2779.375】。綜合本研究結果可提供食品產業擬定降低餐飲物流碳排量策略之參考。
The purpose of this study was to investigate the relationship between climate conditions,vehicle variables, regional factors and food logistic carbon emission. The research collected data of foodlogistic related carbon emissions and meteorological data through qualitative research methods, andused the SPSS Statistics to discuss the determinants of food logistic carbon emissions and food logistic carbon emissions predictive model. The results showed that there is significant correlation betweenvehicle mileage, transport shops, sum of business and the amount of food logistic carbon emissions (p<0.05), and rainfall has negative correlation between vehicle mileage and food logistic carbon emissions (p<0.05). Meanwhile, the climate conditions, vehicle variables, and food logistic carbon emission indifferent business area are significant differences (p<0.05). This study further used regression analysisto investigate the food logistic carbon emissions determinants, the regression model of food logistic carbonemission is: Carbon Emissions = 0.323× [vehicle mileage] +0.225× [sum of business] +2779.375.Based on the above conclusions, it provides references for the food logistic industry to formulate a strategyfor reducing carbon emissions. |
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