Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

Recent years have seen an increased interest in demand-side mitigation of greenhouse gas emissions. Despite the oftentimes spatial nature of emissions research, links to social factors and infrastructure are often not analysed geographically. To reach substantial and lasting emission reductions without further disadvantaging vulnerable populations, the design of effective mitigation policies on the local level requires considerations of spatial and social inequalities as well as the context of well-being. Consequently, we explore spatial variations in the links between consumption-based transport emissions with infrastructural factors, such as workplace distance and public transport density, and with risk-factors of transport poverty, including income, age, ethnicity, mobility constraints in London. We find that linear models report significant spatial autocorrelation at p <= 0.01 in their model residuals, indicating spatial dependency. Using geographically weighted regression models improves model fits by an adjusted R-2 value of 9-70% compared to linear models. Here, modelling flight emissions generally sees the lowest improvements, while those models modelling emissions from cars and vans see the highest improvements in model fit. We conclude that using geographically weighted regression to assess the links between social factors and emissions offers insights which global, linear models overlook. Moreover, this type of analysis enables an assessment of where, spatially, different types of policy interventions may be most effective in reducing not only emissions, but transport poverty risks. Patterns of spatial heterogeneity and policy implications of this research are discussed.

Automated summary

In recent years, there has been a growing interest in demand-side mitigation of greenhouse gas emissions. However, the spatial nature of emissions research is often not geographically analyzed in relation to social factors and infrastructure. This study explores the spatial variations in the links between consumption-based transport emissions and infrastructural factors, as well as risk-factors of transport poverty, in London. It finds that using geographically weighted regression improves the model fits and provides insights that global linear models overlook.