The promotion of sustainable mobility choices is a crucial element of transport decarbonization. It requires a fundamental understanding of the choices available to urban dwellers and of the equity and justice implications of green mobility solutions. In this study, we quantified personal mobility-related greenhouse gas (GHG) emissions in the Greater Toronto and Hamilton Area (GTHA) and their associations with various land use, built environment, and socioeconomic factors. Our study captured personal, household, and neighborhood-level characteristics that are related to high emissions and disparities in emissions across the study region. We observed that the top 30% of emitters generated 70% of all transportation GHG emissions. Household income, family size, and vehicle ownership were associated with increased mobility emissions, while increased population density was associated with lower emissions. The percentage of visible minorities in a neighborhood was associated with lower emissions, but this effect was small. We further contrasted the spatial distribution of traffic-related air pollution with mobility GHG emissions. The results suggest that individuals who emit less GHG live in areas with higher air pollution. A computer vision-based model was used to predict GHG emissions from aerial images of neighborhoods, demonstrating that areas with high land use mixture were linked to a lower generation of mobility-based GHG emissions.
Keywords: environmental justice; greenhouse gas emissions; machine learning; mobility justice; personal carbon footprint.