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Estimated cross-municipal relationships between a series of geo-demographic determinants and the CF holding other determinants constant

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posted on 2013-09-10, 00:00 authored by Jan Minx, Helga Weisz, Peter-Paul Pichler, Michael Förster, Kuishuang Feng, Felix Creutzig, John Barrett, Thomas Wiedmann, Giovanni Baiocchi, Klaus Hubacek

Figure 5. Estimated cross-municipal relationships between a series of geo-demographic determinants and the CF holding other determinants constant. It shows that mainly socio-economic rather than geographic and infrastructural factors strongly determine the CF. The CF increases with income, education (see footnote 13) and per household car ownership and decreases with population density and household size. Since the CF is expressed in mean deviation form, the smooth term function of the determinant, each plot represents how the CF changes relative to its mean, 12.5 t per capita, with changes in determinants. Confidence bounds, i.e., two standard errors above and below the estimate of the smooth function being plotted, are shown in each graph. The covariate to which the plot applies is displayed as a rug plot. Partial residuals, i.e., the residuals after removing the effect of all other determinants, are also plotted. The model explains 87% of the footprint variability. All coefficients are highly significant and we find no evidence of model misspecification. Detailed results and regression diagnostics are reported in the regression in the SI (available at


A growing body of literature discusses the CO2 emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO2 emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO2 emissions. Consumption-based CO2 emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO2 emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.