关键词: “双碳”目标, 交通流大数据, 高速公路碳排放, 碳排放计量模型, 空间格局, 广东省

Abstract:

The transportation sector has become one of the largest industrial emissions source of greenhouses gases, such as CO₂. What's worse, carbon emissions from this industry has continued to grow in recent years, posing serious challenges to human survival and global environmental security. Among the various transport modes, road transportation yields the highest levels of energy consumption and CO₂ emissions. Therefore, scientifically measuring highway carbon emissions and analyzing their spatial differences are of great significance for energy conservation and emission reduction in the transportation sector. Taking Guangdong Province as an example, this study constructs a full-samples and high-precision carbon emissions model, which encompasses Class I~IV passenger cars and Class I~VI freight vehicles based on origin-destination traffic flow data recorded by the highway networking toll system. Finally, the study explores the spatial difference in carbon emissions of highways in Guangdong Province by using geospatial methods. The conclusions are as follows.Firstly, carbon emissions from highways in Guangdong Province mainly came from trucks, which accounted for 57.1% of the total carbon emissions; passenger cars accounted for 42.9%. To be specific, the carbon emissions mainly originated from small and medium-sized vehicles, including Class I passenger vehicles (i.e., cars) and Class I and III freight vehicles. Secondly, the high carbon emissions road sections were spatially auto-correlated, with peak aggregations on national highways, near economically developed and densely populated areas, and adjacent to airports and ports. Road sections with high carbon emissions in Guangdong Province were concentrated along national highways (9,477 t; 61.9%); the carbon emissions of provincial road sections were relatively low (5,834 t; 38.1%). The high-emission sections of passenger vehicles were mainly concentrated in the Pearl River Delta and radially distributed outwards along Guangzhou City. The high-emission sections of freight vehicles were mainly distributed in national highways. The smaller volume of trucks, the more concentrated the spatial distribution of carbon emissions. Furthermore, at the city scale, the cities with higher carbon emissions were mostly concentrated in the Pearl River Delta urban agglomerations, and Guangzhou had a evident primary city effect. The cities with lower carbon emissions were mainly concentrated in coastal areas, such as Zhuhai. At the county scale, the spatial non-equilibrium characteristics of the carbon emissions were significant. The counties with higher carbon emissions were located in the northern part of Guangdong Province and the center and east coast of the Pearl River Delta.Finally, different types of vehicles had differentiated carbon emission characteristics and emission reduction paths. For example, based on the large quantity and significant carbon emissions of Class I passenger vehicles, we must optimize the energy structure to increase the proportion of vehicles using renewable energy sources. Owing to the high unit fuel consumption of Class VI freight vehicles, improving their operation efficiencies is crucial to avoid empty carriages (i.e., no cargo) and we must optimize their driving routes. This research improves the scientificity and spatial analytical accuracy of measuring traffic carbon emissions, thus enriching the sustainable development theory of the transportation, practically promoting the precise emission reduction and green development of the transportation industry, and providing technical and strategic support for attaining dual carbon targets in China.

Key words: dual carbon targets, traffic flow big data, highway carbon emissions, emissions estimation model, spatial pattern, Guangdong Province