[Calculation and Evolution of Traffic Carbon Emission in a Mixed Traffic Environment].

Abstract

With the extensive use of electric vehicles, it is of great significance to measure traffic carbon emissions and analyze its evolution law under the mixed traffic environment in order to effectively achieve traffic carbon reduction. Utilizing taxi GPS data from 2016, 2018, 2020, and 2022, we assessed the carbon emission levels of taxis in Xi'an and matched the results to a grid using map matching. The K-means algorithm was used to analyze the spatial clustering and spatial-temporal distribution of carbon emissions, and the gradient boosting iterative decision tree model （GBDT） was used to explore the influence of built environments on carbon emissions. The results showed that： Weekend carbon emissions were greater than weekday emissions in all years, and the difference between weekend and weekday carbon emissions decreased year by year with the increase in the proportion of electric cabs. The overall weekend carbon emissions in 2022 decreased by approximately 56%, and the overall weekday carbon emissions decreased by approximately 40% compared to those in 2016. The carbon emission region in Xi'an had experienced an evolution from a region-wide ring-shaped distribution in 2016 to 2022. The evolution process of the ring-shaped distribution of peripheral low-carbon emission regions, partial reticulation of medium-carbon emission regions, and reticulation of high-carbon emission regions. From the importance analysis of built environmental factors, it could be seen that residential land and population density had relatively high importance for carbon emissions in each year. The importance of public facilities land was higher on weekdays than that on weekends, while the importance of leisure and entertainment land was higher on weekends than that on weekdays. This work reveals the spatial and temporal distribution evolution of carbon emissions, which can provide a reference for the control and management of transportation carbon emissions under the mixed traffic state.