6.4 Discussion
105
countries, we argue that the complementary distribution of nations between emissions and resource use (water and land use) presented in this study can offer important
information to policy makers. To that end, a correlation analysis is undertaken by a
regression of log-transformed data to measure the strength of potential linear relationship between the ESR and three variables: (1) Gross Domestic Product (GDP)
per capita; (2) renewable water resources (RWRs) per capita; and (3) population
density (PD), as will be illustrated below.
As seen in Fig. 6.4a, very few countries have already achieved a decoupling
of economy from carbon emissions, as evident from the highly significant positive
correlation between GDP per capita and the carbon ESR (R
2
= 0.8622). By contrast,
the ESRs of water and land use have no correlation with GDP per capita since their
correlation coefficients do not pass the significance test of the regression with 95percentile confidence intervals (p < 0.05). An opposite situation is observed when
it comes to Fig. 6.4b, in which per capita RWRs have a non-significant correlation
coefficient with the carbon ESR, while being closely correlated to that of land use
(R
2
= 0.5958) and, not surprisingly, significantly to that of water use (R
2
= 0.8756).
Figure 6.4c exposits positive significant correlations between the PD and ESRs of
water use (R
2
= 0.6310) and land use (R
2
= 0.6157), while the latter is not as strong
as commonly thought. One reason for that might be the salient differences across
land types and nations in bioproductivity—the key parameter for determining the
land footprint and land boundary.
The correlation coefficient between each pair of the explanatory variables indicates that, for the 28 countries on average, approximately 86% of the variation in the
log(ESR) of carbon emissions can be explained by log(GDP/capita). This is quite
different from the cases of water use and land use, where between 53 and 96% of
the variation can be explained by log(RWRs/capita) or log(PD). We further test the
three variables together in a multiple regression (Table 6.2), and see that all the three
ESRs are explained very well by the three variables. GDP is a major driving factor
for the carbon ESR, but not for that of water and land. For water, the RWRs are
highly significant, and for land, the interplay turns out to be more complicated and
there is no clear driving factor.
In summary, the unsustainability of carbon emissions is largely driven by the stage
of economic development on which a nation finds itself, while resource endowments
play an important role in the degree of unsustainable water and land use (PD can be
seen as the inverse of per capita land resources). These findings confirm the inherent
distinction between the systemic and aggregated processes. In addition, Fig. 6.5
shows that the global ESR values for the three environmental issues agree well with
other estimates of the ratios of pressures to thresholds in the literature, supporting
the conclusions of earlier studies on the transgression of planetary carbon boundary
and on the reserve of planetary water and land boundaries. Nevertheless, a major
divergence is encountered in the case of carbon emissions, where the planetary carbon
boundary is measured based either on joint use of two control variables—atmospheric
CO 2 concentration and radiative forcing—not allowing to downscale, as is done by
105
countries, we argue that the complementary distribution of nations between emissions and resource use (water and land use) presented in this study can offer important
information to policy makers. To that end, a correlation analysis is undertaken by a
regression of log-transformed data to measure the strength of potential linear relationship between the ESR and three variables: (1) Gross Domestic Product (GDP)
per capita; (2) renewable water resources (RWRs) per capita; and (3) population
density (PD), as will be illustrated below.
As seen in Fig. 6.4a, very few countries have already achieved a decoupling
of economy from carbon emissions, as evident from the highly significant positive
correlation between GDP per capita and the carbon ESR (R
2
= 0.8622). By contrast,
the ESRs of water and land use have no correlation with GDP per capita since their
correlation coefficients do not pass the significance test of the regression with 95percentile confidence intervals (p < 0.05). An opposite situation is observed when
it comes to Fig. 6.4b, in which per capita RWRs have a non-significant correlation
coefficient with the carbon ESR, while being closely correlated to that of land use
(R
2
= 0.5958) and, not surprisingly, significantly to that of water use (R
2
= 0.8756).
Figure 6.4c exposits positive significant correlations between the PD and ESRs of
water use (R
2
= 0.6310) and land use (R
2
= 0.6157), while the latter is not as strong
as commonly thought. One reason for that might be the salient differences across
land types and nations in bioproductivity—the key parameter for determining the
land footprint and land boundary.
The correlation coefficient between each pair of the explanatory variables indicates that, for the 28 countries on average, approximately 86% of the variation in the
log(ESR) of carbon emissions can be explained by log(GDP/capita). This is quite
different from the cases of water use and land use, where between 53 and 96% of
the variation can be explained by log(RWRs/capita) or log(PD). We further test the
three variables together in a multiple regression (Table 6.2), and see that all the three
ESRs are explained very well by the three variables. GDP is a major driving factor
for the carbon ESR, but not for that of water and land. For water, the RWRs are
highly significant, and for land, the interplay turns out to be more complicated and
there is no clear driving factor.
In summary, the unsustainability of carbon emissions is largely driven by the stage
of economic development on which a nation finds itself, while resource endowments
play an important role in the degree of unsustainable water and land use (PD can be
seen as the inverse of per capita land resources). These findings confirm the inherent
distinction between the systemic and aggregated processes. In addition, Fig. 6.5
shows that the global ESR values for the three environmental issues agree well with
other estimates of the ratios of pressures to thresholds in the literature, supporting
the conclusions of earlier studies on the transgression of planetary carbon boundary
and on the reserve of planetary water and land boundaries. Nevertheless, a major
divergence is encountered in the case of carbon emissions, where the planetary carbon
boundary is measured based either on joint use of two control variables—atmospheric
CO 2 concentration and radiative forcing—not allowing to downscale, as is done by