A Gini approach to spatial CO2 emissions
Combining global gridded population and fossil fuel based CO2 emission data at 1 km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between these two datasets by a quasi-Lorenz curve which enables us to discern the individual contributions of densely and sparsely populated regions to the national CO2 emissions. We observe pronounced country-specific characteristics and quantify them using an indicator resembling the Gini-index. As demonstrated by a robustness test, the Gini-index for each country arise from a compound distribution between the population and emissions which differs among countries. Relating these indices with the degree of socio-economic development measured by per capita Gross Domestic Product (GDP) at purchase power parity, we find a strong negative correlation between the two quantities with a Pearson correlation coefficient of -0.71. More specifically, this implies that in developing countries locations with large population tend to emit relatively more CO2, and in developed countries the opposite tends to be the case. Based on the relation to urban scaling, we discuss the implications for CO2 emissions from cities. Our results show that general statements with regard to the (in)efficiency of large cities should be avoided as it is subject to the socio-economic development of respective countries. Concerning the political relevance, our results suggest a differentiated spatial prioritization in deploying climate change mitigation measures in cities for developed and developing countries.