## Appendix A  
According to the advice of reviewers, whether the regression results were robust to two different sub-samples (GNIPC values above/below median) was tested. The median value of GNIPC was 9990. The total 1728 observations were divided into 877 observations (GNIPC values above median), which covered 60 countries, and 851 observations (GNIPC values below median), which covered 62 countries. We conducted the tests by employing the same methods and procedures. The regression results of the sub-sample (GNIPC values above median) demonstrated that (1) urbanization, renewable energy consumption, and service industries had significant and negative impacts on ecological consumption, (2) internet usage and education were statistically insignificant, and (3) there was an inversed U-shaped relationship between ecological consumption and income (the EKC hypothesis was supported). As can be seen, the regression results were almost the same as those of the 42 developed countries. The regression results of the sub-sample (GNIPC values below median) demonstrated that (1) renewable energy consumption and service industries had significant and negative impacts on ecological consumption, (2) urbanization, internet usage, and education were statistically insignificant, and (3) there was a U-shaped relationship between ecological consumption and income (the EKC hypothesis was not supported). As can be seen, except for internet usage, the regression results of the variables were almost the same as those of the 48 developing countries. In conclusion, the regression results in this paper were robust to the two sub-samples (GNIPC values above/below median) generally. The details can be seen in Table A1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Sub-Sample (GNIPC Values above Median)**} & \multicolumn{2}{c}{**Sub-Sample (GNIPC Values below Median)**} \\ \hline  & **Model (3)** & **Model (4)** & **Model (3)** & **Model (4)** \\ \hline  & **Coefficient (RSE)** & **Coefficient (RSE)** & **Coefficient (RSE)** & **Coefficient (RSE)** \\ \hline  & \(-\)0.036 * & \(-\)0.032 * & \(-\)0.000 & 0.001 \\  & (0.02) & (0.02) & (0.01) & (0.01) \\  & \(-\)0.063 *** & \(-\)0.048 *** & \(-\)0.011 *** & \(-\)0.009 *** \\  & (0.01) & (0.01) & (0.00) & (0.00) \\  & \(-\)0.053 *** & \(-\)0.059 *** & \(-\)0.004 * & \(-\)0.004 ** \\  & (0.02) & (0.02) & (0.00) & (0.00) \\ INT & 0.001 & 0.003 & 0.001 & \(-\)0.000 \\  & (0.00) & (0.00) & (0.00) & (0.00) \\  & 0.065 & 0.049 & -0.037 & \(-\)0.023 \\  & (0.07) & (0.07) & (0.03) & (0.03) \\ \(Ln\)(GNIPC) & 10.505 *** & 12.745 *** & \(-\)1.584 *** & \(-\)1.812 *** \\  & (3.74) & (3.44) & (0.55) & (0.60) \\  & \(-\)0.500 *** & \(-\)0.638 *** & 0.117 *** & 0.132 *** \\ \(Ln\)(GNIPC)\({}^{2}\) & (0.19) & (0.18) & (0.04) & (0.04) \\  & \(-\)43.766 ** & \(-\)52.587 *** & 7.733 *** & 8.345 *** \\  & (18.09) & (16.70) & (2.07) & (2.27) \\ \hline  & \multicolumn{1}{c}{} & & & \\ \multicolumn{1}{c}{} & & & \\ \multicolumn{1}{c}{} & & & \\ \multicolumn{1}{c}{} & & & \\ \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular} Notes: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively; for Models (3) and (4), robust standard errors (RSE) are reported in parentheses.  
\end{table}
Table A1: Estimations of impacts of influencing factors and control variables for the two sub-samples (GNIPC values above/below median).