Promoting active travel behavior and decreasing transport-related carbon dioxide （CO2） emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. Howev...Promoting active travel behavior and decreasing transport-related carbon dioxide （CO2） emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated C02 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was sig- nificant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with sev- eral built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-eeonomic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.展开更多
This study analyzed weekday shopping behavior from a home base to a destination using data from the 4th Keihanshin metropolitan area person trip survey.We first analyzed the relationships between occupation and transp...This study analyzed weekday shopping behavior from a home base to a destination using data from the 4th Keihanshin metropolitan area person trip survey.We first analyzed the relationships between occupation and transportation means,transportation means and travel time,and transportation means and duration of time at the destination.Results of a chi-square test,residual analysis,and correspondence analysis confirmed that employed persons tend to travel by cars while unemployed persons tend to travel by bus or walk.The relationship between travel time and duration of time at the destination was also revealed according to transportation means.Results of a cluster analysis then classified shopping behaviors to expose four patterns.Finally,multiple regression analyzed the degree to which certain variables were related to duration of time at the destination.Results confirmed a strong relationship between duration of time at the destination and travel time.The degree of this factor’s influence on other variables was also clarified.The value of these findings is that the relationship between travel time by means of transportation and the duration of time at the destination was clarified using statistical analysis.We then found a highly accurate equation that estimates the duration of time at a destination from the travel time.If the duration of time at a destination can be estimated,it may be possible to more accurately develop the structure of rest facilities,the number of parking lots,the degree of congestion,and so on,which can be associated with the customer’s usage behavior in a shopping site.This research in this paper contributes to the field of urban analysis and marketing by presenting a new effective method for person trip survey and analysis.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41201159,41571152,41401478,41201160,41001076)the Key Research Program of the Chinese Academy of Sciences(No.KSZD-EW-Z-021-03,KZZD-EW-06-03)
文摘Promoting active travel behavior and decreasing transport-related carbon dioxide （CO2） emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated C02 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was sig- nificant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with sev- eral built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-eeonomic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.
基金This work was supported by Kindai University under a Faculty Assistance and Development Research Grant in 2018[grant number SR09]This study was supported by Joint Research Program No.690 at Center for Spatial Information Science(CSIS),The University of Tokyo.
文摘This study analyzed weekday shopping behavior from a home base to a destination using data from the 4th Keihanshin metropolitan area person trip survey.We first analyzed the relationships between occupation and transportation means,transportation means and travel time,and transportation means and duration of time at the destination.Results of a chi-square test,residual analysis,and correspondence analysis confirmed that employed persons tend to travel by cars while unemployed persons tend to travel by bus or walk.The relationship between travel time and duration of time at the destination was also revealed according to transportation means.Results of a cluster analysis then classified shopping behaviors to expose four patterns.Finally,multiple regression analyzed the degree to which certain variables were related to duration of time at the destination.Results confirmed a strong relationship between duration of time at the destination and travel time.The degree of this factor’s influence on other variables was also clarified.The value of these findings is that the relationship between travel time by means of transportation and the duration of time at the destination was clarified using statistical analysis.We then found a highly accurate equation that estimates the duration of time at a destination from the travel time.If the duration of time at a destination can be estimated,it may be possible to more accurately develop the structure of rest facilities,the number of parking lots,the degree of congestion,and so on,which can be associated with the customer’s usage behavior in a shopping site.This research in this paper contributes to the field of urban analysis and marketing by presenting a new effective method for person trip survey and analysis.
基金supported by the National Natural Science Foundation of China(No.51308495)the Public Welfare Projects in Zhejiang Province of China(No.2015C31012)the Program for Key Science and Technology Innovation Team of Zhejiang Province(No.2013TD09),China
