Figure 1.
Relative rank of countries by proportional and absolute environmental impact.
Proportional environmental impact (179 countries; top panel) and absolute environmental impact rank (171 countries; bottom panel) (darker grey = higher impact) out of 228 countries considered are shown. Environmental impact ranks (proportional and absolute) combine ranks for natural forest lost, habitat conversion, marine captures, fertilizer use, water pollution, carbon emissions and proportion of threatened species (see text for details). The worst 20 countries (codes described in Tables 1 & 2) for each ranking are shown.
Table 1.
Twenty worst-ranked countries by proportional composite environmental (pENV) rank (lower ranks = higher negative impact).
Table 2.
Twenty top-ranked countries by proportional composite environmental (pENV) rank (higher ranks = lower negative impact).
Table 3.
Ten worst- and best-ranked countries by proportional composite rank (pENV) with each of the 7 composite metrics removed sequentially (i.e., pENV calculated from 6 metrics only).
Table 4.
Twenty worst-ranked countries by absolute composite environmental (aENV) rank (lower ranks = higher negative impact).
Table 5.
Ten worst- and best-ranked countries by absolute composite rank (aENV) with each of the 7 composite metrics removed sequentially (i.e., aENV calculated from 6 metrics only).
Table 6.
Ten worst- and best-ranked countries by proportional environmental metrics: proportional natural forest loss (NFL), proportional natural habitat conversion (HBC), proportional marine captures (MC), proportional fertilizer use (FER), proportional water pollution (WTP), proportion of threatened species (THR), and proportional carbon emissions (CO2).
Table 7.
Ten worst- and best-ranked countries by individual absolute environmental metrics: natural forest loss (NFL), natural habitat conversion (HBC), marine captures (MC), fertilizer use (FER), water pollution (WTP), total threatened species (THR), and carbon emissions (CO2).
Figure 2.
Bivariate correlations among environmental impact ranks and socio-economic variable ranks based on Kendall's τ.
Strength of the relationships for which evidence exists between relative and absolute environmental impact ranks (see text for details) and socio-economic variables (human population size, human population density and human population growth rate, wealth [purchase power parity-adjusted Gross National Income] and governance quality) as measured by τ are given in the Results.
Figure 3.
Tests for the environmental Kuznets curve (EKC) hypothesis.
The EKC asserts that environmental impact is a non-linear function of per capita wealth [10]. Top panel: the intercept-only, linear, and quadratic (on log10 scale) models fitted to the proportional environmental impact rank. The linear model had the highest Bayesian inference support (see Results). Bottom panel: the intercept-only model (i.e., no relationship) had the highest support for the absolute environmental rank. The ‘*’ indicates an opposite rank direction to that presented in Fig. 2 for mathematical convenience (i.e., fitting a nonlinear function to the data).
Figure 4.
Structural equation models for environmental impact ranks.
Top Bayesian Information Criterion- (BIC-) ranked structural equation models for the (i) proportional environmental impact rank (Model A; Table 4i) and (ii) absolute environmental impact rank (Model D; Table 4ii). Wealth (purchasing power parity-adjusted Gross National Income) had the highest correlation with proportional and absolute rank, with some additional contribution of total human population size to the absolute rank. Numbers shown are path coefficients with associated Type I error (P) probabilities. See full model rankings in Table 4.
Table 8.
Ranking of six candidate path models relating socio-economic variables to the (i) proportional (pENV) and (ii) absolute environmental impact rank (aENV) based on the Bayesian Information Criterion (BIC).