Abstract
Motor vehicle travel is one of the causes of aggravation of CO2 emission, environmental issues and urban problems. The advocation of low-carbon travel is necessary for the achievement of low-carbon city construction and sustainable development in the future. Many studies have shown that built environment tends to influence residents’ travel behavior, and most studies are demonstrated from the macro level of metropolis. However, from the perspective of neighborhoods, much less attention has been paid, especially in developing countries including China. This study chooses 15 neighborhoods in the main districts of Nanjing in China, taking the location of neighborhoods and residents’ socio-economic attributes into consideration, to examine the effects of residential built environment on residents’ mode choice of different travel types, and to propose the recommended values for the most significant variables. The residential built environment attributes are from three dimensions of land use, road network system and transit facilities. The method of this study is three-step and successive. Primarily, a correlation analysis model is applied to initially examine the role that residents’ socio-economic attributes and residential built environment attributes play on residents’ low-carbon travel of three different travel types respectively. Primary significant attributes from these two aspects are preliminarily screened out for the re-screening in the next step. In addition, the study uses multivariate logit regression modeling approach, with significant socio-economic attributes as concomitant variables, to further re-screen out the key variables of built environment. Furthermore, a unary linear regression model is applied to propose the recommended values for the key built environment variables.
Highlights
The greenhouse effect has brought rigorous challenge for society and ecology in a global context
As Intergovernmental Panel on Climate Change (IPCC) reported in 2014 that transport accounted for 14% of total greenhouse gas emission worldwide [1]
Based on the data obtained from the low-carbon city questionnaire survey carried out in Wuhan in 2010, Huang, Du, Liu et al [8,9] conducted the multiple linear regression analysis and the results indicated that the carbon emissions of household daily traffic trips are significantly correlated to the monthly household income, the education level of female family members, the number of permanent residents of the family
Summary
The greenhouse effect has brought rigorous challenge for society and ecology in a global context. Based on the empirical study of five sample neighborhoods in San Francisco, Kitamura, Mokhtarian and Laidet [15] concluded that the variables such as residential density, public transit accessibility, mixed use of land and the presence of sidewalks were significantly related to residents’ travel behavior in built environments. The specific built environment variables and the characteristics of residents’ socio-economic attributes are regarded as independent variables and the bus sharing rate as a dependent variable On this basis, the multiple linear regression model was established and the results indicated that there is a respectively significantly positive correlation, positive correlation, positive correlation and negative correlation between the bus sharing rate and four variables respectively, including the mixed degree of land use, the density of cross intersection, transportation line overlap factor and the distance to the nearest subway station. In the formula, Pi represents the share of low-carbon travel in neighborhood i, which is the dependent variable; Xi represents a variable of the built environment of sample neighborhood i, which is an independent variable; β0 is the constant term; β1 is the coefficient
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
