Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China

Abstract

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO₂ emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO₂ emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO₂ emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO₂ emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO₂ emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO₂ emission reduction is realized from both the source of industry and low-carbon modes of transport.

Keywords: Clustering analysis; Driving factors; Expressway vehicular CO2 emissions; Guizhou Province.