Quantifying the drivers of CO₂ emissions across Canadian communities using quantile regression

Carbon dioxide (CO₂) emissions from community-based consumption are a major contributor to global greenhouse gas emissions. However, little is understood about how demographic, socioeconomic, and household factors may contribute to community CO₂ emissions variation. Using traditional regression methods to model community emissions does not discriminate which of these factors are responsible for CO₂ emissions in high carbon emitting communities versus low carbon emitting communities, leading to policy development that does not consider emission variation across communities. To address this issue, we used quantile regression to model these effects on different quantiles of community emissions for 1451 communities across Canada and each province in 2015, respectively. The results showed that population, followed by affluence, were the most important variables affecting total community emissions, while affluence was the most important factor affecting per capita community emissions. However, the effect sizes were not consistent across quantiles, decreasing for population and increasing for affluence from low to high emission communities. Population density was significant across all communities except the lowest quantile communities, with the effect size increasing from smaller to larger communities. Additionally, our measure of poverty was significantly associated with increases in total and per capita emissions for all quantiles at the national level. Our finding that factors responsible for CO₂ emissions varied across communities of different quantiles suggests that successful emission reduction policies must account for the diversity of community characteristics, particularly considering variation in population and affluence. Our study also shows poverty alleviation is an effective means for CO₂ emission reduction and should be considered when adopting emission reduction policies.