AUTHOR=Li Yuanjun , Wu Qitao , Zhang Yuling , Huang Guangqing , Jin Shuangquan , Fang Shun TITLE=Mapping Highway Mobile Carbon Source Emissions Using Traffic Flow Big Data: A Case Study of Guangdong Province, China JOURNAL=Frontiers in Energy Research VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.891742 DOI=10.3389/fenrg.2022.891742 ISSN=2296-598X ABSTRACT=

The continuously growing transportation sector has become the second largest, yet increasing, industrial emissions source of CO2, posing serious challenges to global environmental security. Among the various transport modes, road transportation yields the highest cumulative level of CO2 emissions. However, these emissions have not been sufficiently investigated in previous studies, especially with respect to analyses from the perspective of vehicle emission sources. This can make source management and emissions reduction difficult. To address these methodological issues, this study aims to build a highway traffic carbon emissions monitoring and spatial analysis system, employing the mobile carbon sources concept, and establish a carbon emissions model encompassing all types of passenger and freight vehicles based on interstation O-D traffic flow data recorded by the toll collection network, to calculate vehicle carbon emissions and create a mobile carbon source emissions map. Empirical analyses in Guangdong Province revealed that, compared with conventional studies, the mobile carbon source emission mapping approach can accurately identify vehicle types with higher emissions while assisting with source management. Of the average total daily carbon emissions from all types of vehicles that use highways (15,311 t), 57.10% originated from freight vehicles (8,743 t) while passenger vehicles contributed 42.90%. By specific vehicle type, emissions mainly originated from small and medium-sized vehicles, including Class I passenger vehicles (i.e., cars) and Class I and III freight vehicles. Further, the proposed method could locate road sections characterized by high carbon emissions. High-emission sections in Guangdong Province were mainly spatially autocorrelated, with peak aggregations on national highways; near economically developed and densely populated areas; and adjacent to surrounding airports, ports, and overpass roads. This study improves the scientific and spatial analytical accuracy for carbon emissions measurements of highway vehicles, thus informing source management and sustainable development, as well as providing technical support for attaining carbon neutrality in China.