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

Abstract

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 R2 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.

Spatiotemporal evolution trend and decoupling type identification of transport carbon emissions from economic development in China

“…The Tapio decoupling model widely employed to evaluate the relationship between the energy efficiency of the economy and carbon emissions (Chen et al 2020;Ma et al 2020;Wang et al 2020a;Wang et al 2020c). For the analysis of spatial correlations and clustering intensity, several researchers have used the Moran's index (Wang et al 2020b;Cao et al 2019;Yaacob et al 2020) and geographic weighted regression (GWR) calculations to examine the geographical associations between the same variable and several locations, increasing the simulation's degree of fit in comparison to the linear regression model (Kilian et al 2022;Xu et al 2021).…”

“…Research on transport carbon emissions has focused mainly on statistical analyses and have explored emissions only from the national or provincial perspective (Kilian et al 2022;Nnadiri et al 2021;Pani et al 2021); specifically, most analyses of transport carbon emissions in China have been performed in provinces such as Guangdong (Zhao et al 2021), Shanghai (Zhu et al 2022) and Zhejiang (Liu et al 2023), while less research has explored spatial factors and regional transportation drivers. On the other hand, there is a lack of systematic analyses of the economic efficiency of transport carbon emissions in China.…”