Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Understanding and predicting urban traffic carbon emissions constitute an urgent agenda in research and policy decision-making. Since the exhausted emissions vary in time and different urban settings, assessing the spatio-temporal distribution of carbon emissions is fundamentally important for land use planning. This paper attempts to identify the spatial and temporal heterogeneity of the impacts of land use and built environment on urban traffic carbon emissions. A spatial standard deviational ellipse (SDE) model and a geographically and temporally weighted regression (GTWR) model were developed to explore the spatiotemporal dependency of traffic carbon emissions on land use and built environment factors and applied to the core urban zones of Dalian, China. Results show the center of gravity of traffic carbon emissions have a footprint characterized by a shift to the southeast first and then to the northwest, with weekday and weekend performance being consistent. Compared to other periods, emissions are spatially agglomerated during internal hours (9:00-15:59), especially during weekdays. Land use and built environment factors affect carbon emissions differently across space and time whereas the effects of residential population density, employment density, medical, road network density on weekdays are larger than that on weekends. Furthermore, we found that increasing land use mix leads to a greater negative impact on weekday emissions. This supplements the important role of mixed land use planning in decarbon-ization. Based on the findings, we propose various policy interventions to support the development of carbon neutral cities.