Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China

Abstract

There is a growing recognition that optimizing urban form can reduce road transport-related carbon emissions, particularly in carbon emissions by daily travel. In this study, we proposed an improved bottom-up approach combining Vehicle Specific Power (VSP) model and Enhanced Two-Step Floating Catchment Area (E2SFCA) method to estimate the carbon emissions by daily travel. Then, a geographical weighted regression (GWR) model considering the urban residential density (RD) and land use mix level (LML) of urban form as independent variables was employed to explore the relationship between urban form and carbon emissions. A typical working day (June 5th of 2017) of Hangzhou was chosen as a case study in this paper. The results showed that (1) per hour carbon emission in the morning peak period of 6:30 a.m.-9:30 a.m. was higher than that in the evening peak of 22:00–24:00 by 33.34%; and distribution of the highest carbon emissions was in the eastern, northeastern and northwestern parts of Hangzhou; (2) The RD and LML were positively and negatively associated with carbon emissions. and the coefficients for Ln (RD) and (LML) ranged from 0.29 to 0.70 and from −9.01 to −6.06, respectively. (3) The spatial distribution of coefficients demonstrated that the highest effects of RD on carbon emissions were observed in the central parts of Hangzhou, and those of LML on carbon emissions were observed in southern Hangzhou featured by industrial parks. This study may provide insights to mitigate carbon emissions from daily travel with multiple public policies including mixed land-use policies, urban density control and spatial planning.