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Reducing CO2 emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.
Zhang et al. (Thu,) studied this question.