**3.2 PSTR discussion**

#### *3.2.1 The R-EKC curve discussion*

In **Table 7**, the results show that the direct effect of income on CO2 emissions, measured by the b0 coefficient, is positive and significant in all the PSTR estimates. This means that in early stage of development, more income means more pollution (such as in [31]). In the second and third regimes, the coefficients become negative and statistically significant for the PSTR 1 and PSTR 2 (in the second regime) and PSTR 3 and PSTR 4 (in the third regime), respectively.

The next rows in **Table 6** provide interesting insights: here the impact of income on pollution is conditional on the level of GDP per capita. More specifically, the b1 coefficient, associated with the nonlinear component of the model, is always negative and significant at the 1% level (PSTR 1 and PSTR 2 including renewable energy consumption), with values ranging between 0.343 and 0.307. According to the exponential function, this implies that the elasticity of CO2 emissions with respect to income changes from 0.34 or 0.42 (as b0 takes these values in PSTR 1 and PSTR 2) to b1, i.e., as pollution goes from high to low values. The shift between these two extreme regimes occurs around the associated endogenous threshold parameter c (shown in rows 6 and 7), which equals 25243\$–44494\$ (as in [14]). In the low pollution regime (indices ≥ 25243), the effect of income on environmental degradation is negative and statistically significant at the 1% level; in the extreme case (when g (sit; γ, c) = 1), all other things being equal, a 1% increase in the income gives a 0.31–0.34% reduction in CO2 emissions (the coefficient in the second regime being equal to β0 + β1). Given that there is a continuum of possible points between these two extreme regimes, the elasticity is a weighted average of the parameters β<sup>0</sup> and β1. This result implies that without a higher income level, advanced economies cannot optimally benefit from a sound environmental quality, and that any policy would be not so much effective.

The paper finds also that the impact of income on CO2 emissions is nonlinear. Based on the three statistics (LR, LMF, and LRT) reported in **Table 3** of Appendix, the hypothesis of a heterogeneous influence of income on pollution is accepted. Thus, income affects CO2 emissions in various ways. The test of remaining regimes (**Table 4** of Appendix) complements this finding and concludes on the existence of two thresholds for GDP per capita. Another argument in support of the nonlinear hypothesis is that the slope of the transition function differs between different regimes and the four PSTR. The higher the γ, the sharper the change from one extreme regime to another. For the PSTR 2, for example, results show that any effort in terms of decreasing pollution by a country just below the threshold value of 26132\$ is likely to result in a sharp decrease in the elasticity of pollution with respect to income (from 0.42 to 0.34). However, for a country that is far below this threshold, the same effort will have little effect on the elasticity of pollution. Conversely, in the PSTR 1 it is found a smooth transition. This means that, unlike with the sharp transition previously defined, any effort to combat pollution, even by a country far below the threshold value, will always be recompensated (by a gradual fall in the marginal effect of

pollution). Similar features are found for PSTR 3 and PSTR 4 including renewable electricity output among control variables.

### *3.2.2 The explanatory variables impact*

Next, consistent with previous empirical literature, control variables are integrated such as trade openness, FDI, urbanization, and renewable energy consumption or renewable electricity output. These explanatory variables were almost significant and had the expected signs (except for urbanization).

Trade openness exerts a negative and statistically significant effect on pollution, which resonates with the technological effect of trade on environmental quality (contrary to [31] where a positive significant influence of trade openness on carbon emissions is found, validating rather a scale effect of trade on environmental pollution). Foreign direct investments are essential for improving productivity and increasing the competitiveness of an economy. This coefficient is negative or positive but statistically insignificant in all PSTR models. A counterfactual result is found for the urbanization. Indeed, an increase in the urban population should increase the level of emissions, particularly, carbon dioxide emissions; thus, countries that have higher urban populations are expected to pollute the environment more than other countries.

The coefficients of renewable energy consumption or renewable energy output (used for robustness purposes) are always negative and statistically significant at 1% level. This means that clean sources of energy reduce greenhouse emissions and protect environment. Therefore, governments must support the development of this burgeoning energy sector as well as the implementation of carbon taxes to discourage the use of fossil fuels (as a conventional energy source) and to protect environment. Furthermore, such "new-generation" energy policies are expected to be key for a complete decarbonization of the energy sector to achieve sustainable development goals of the IPAC or EU Green Deal by 2030 (which is in line with findings of [22]).
