Fig 1.
Population and CO2 emissions for the contiguous USA.
(a) Gridded Population of the World, version 4, GPWv4 [17] at 1 × 1 km2 spatial resolution in 2010 and (c) total anthropogenic CO2 emission from ODIAC data [18] for the same region, year, and resolution. (b) and (d) depict magnified views of population and CO2 emission for the New York metropolitan region, respectively. Visually, large agglomerations of population coincide with large amounts of emissions. To which extent they relate proportionally is the subject of this paper.
Fig 2.
Illustration of the inhomogeneity index Ge.
Quasi-Lorenz curves (solid lines) and the calculation of Ge, which is inspired by the Gini coefficient: Ge+ = A/(A + B) and Ge− = −A′/(A′ + B′).
Fig 3.
Quasi-Lorenz curves and corresponding inhomogeneity index Ge for selected countries.
The country-specific curves are drawn by plotting the accumulated population (in ascending order) on the horizontal axis against the accumulated share of CO2 emissions of the corresponding grid cells. The panels show the curves for (a) USA, (b) UK, (c) Brazil, (d) France, (e) Germany, (f) China, (g) Kenya, and (h) Uganda. If the curves follow the diagonal, then low and high densities have the same emissions per capita. If the curves are bent to the lower right corner, then cells of small density exhibit relatively low emissions and high population cells exhibit relatively high emissions. Curves in the upper left corner indicate the opposite behavior. The inhomogeneity index Ge is positive or negative. It can be seen, that various countries exhibit non-proportional relations between population and emissions. The inhomogeneity index Ge seems to be related to the development of the country.
Fig 4.
Development dependence of CO2-population-inhomgeneity.
The inhomogeneity index Ge is plotted vs. Gross Domestic Product (GDP) per capita (PPP) for 94 countries on a semi-logarithmic scale. For better readability only the symbols of a sub-set of countries are labeled. As can be seen, the Ge correlate with the economic development. The Pearson correlation coefficient between Ge and GDP on a logarithmic scale is ρ = −0.71 (p ≤ 0.01). In more developed countries high population densities have lower emissions as low densities. The GDP data were obtained from the World Bank (http://data.worldbank.org), measured in USD of the year 2010.
Fig 5.
Comparison of CO2-population-inhomgeneity for different CO2 datasets and nightlights.
(a) ODIAC, (b) FFDAS, (c) EDGAR, (d) nightlights [32]. Each panel is analogous to Fig 4, but for consistency of spatial resolution the underlying ODIAC data in (a) has been aggregated to 10 km resolution.
Fig 6.
Sub-national inhomogeneity index Ge.
We calculated the Ge on the province level for China. In (a) the Ge-values are plotted against the corresponding province GDP per capita values on a logarithmic scale, analogous to Fig 4. The dashed line indicates the country-level mean Ge. Panel (b) shows a map of China where the provinces are color-coded according to the inhomogeneity index Ge. It can be seen that the development dependence as found in Fig 4 does also hold on the sub-national scale—at least in China. Provinces with lowest and highest Ge-values are Hong Kong and Tibet, respectively. Note, however, that for the USA we do not find sub-national correlations (S1 Fig in S1 File). (Data source of China level-1 administrative boundaries: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-1-states-provinces/).
Fig 7.
In order to illustrate the robustness of the curves in Fig 3 we compare them with curves when the data is sub-sampled or shuffled. The panels for (a) Germany and (b) UK include the curves for the full samples (Fs), median and envelop for the sub-sampled data (Sb, green), and the median and envelop for the shuffled data (Sf, orange). The insets show histograms of the corresponding inhomogeneity indices. It can be seen that sub-samples of the data lead to similar results as for the full sample so that the results are not due to individual pixels. The Ge values from the shuffling approach −1, as the correlations between population and CO2 are destroyed.