Abstract
A critical issue in the mitigation of transport CO2 emission and the development of low-carbon cities is the need to get a better understanding of factors that shape travel behavior, and resulting carbon emission. Using an activity diary survey and GIS-based land use data in Beijing, this research investigates how urban form characteristics at neighborhood and city scales impact individual's daily travel behavior and subsequent CO2 emission from work and non-work trips, respectively. Structural equation modeling (SEM) is adopted to examine the relationship between urban form, travel behavior, and CO2 emission, while accounting for residential self-selection and socio-demographic attributes. Results show that residents living in neighborhoods with higher job density, proximity to an employment sub-center and greater subway accessibility tend to travel shorter distance, choose low-carbon travel modes, and emit less CO2 from work related trips. People resident in neighborhoods with higher retail density or mixed land use tend to travel shorter distance and have less CO2 emission from non-work trips. The research also suggests that work related trips have larger variation than non-work trips across neighborhoods, indicating the job-housing spatial mismatch might be the main factor that drives up travel demand and transport CO2 emission in urban Beijing.
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