## 6 Results
### Total RE Installed Capacity vs. Different Policies
#### 6.3.1 Results of the Total RE Installed Capacity vs. Different Policies Analysis for China  
The linear model for China can be written as follows, due to the results provided in Table 13:  
\[y=94.165+5.192x_{1}+35.901x_{2}+3.392x_{3}. \tag{2}\]  
The coefficients of Equation (2) are positive and statistically significant, with \(p\)-values less than 0.05. The correlation factors are 0.986, 0.925, and 0.974, respectively. This depicts that there are positive and strong relations between the total installed RE capacity and the three variables. In addition, the adjusted \(\mathrm{R^{2}_{adj}}\) is 0.99, which indicates that 99% of the total RE capacity variation is explained by the RE policy variables and only 1% is due to random errors. Moreover, the significance level of F-stats is less than 0.01, which indicates that the null hypothesis is rejected and the model is good fit. Additionally, the standardized coefficients in Table 13 show that the third policy (PS + RI) has a larger weight with respect to the other policies. Therefore, the PS + RI category is more effective on the total RE capacity in China. This is due to the RE law released in China (revised 2009), which covers wind, solar, water, biomass, geothermal, and ocean energy (25). Additionally, it is due to the plan of the Ministry of Industry, 2012, that reduced the cost of solar power and increased the production of solar panels (31). In addition, the 13 th 5-year plan (2016-2020) provided a strategy for sustainable energy and focused on green actions and the implementation of energy-saving projects (30). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**China** & **Coefficients** & **Correlation** & **Fstats** & \(\mathbf{R^{2}adj}\) \\ \hline Hydro & 2.588 * & 0.95 * & 146.52 * & 0.895 \\ \hline Wind & 1.63 * & 0.99 * & 774.813 * & 0.979 \\ \hline Solar & 0.867 * & 0.846 * & 40.408 * & 0.699 \\ \hline Bio & 0.081 * & 0.968 * & 241.68 * & 0.934 \\ \hline
**Brazil** & **Coefficients** & **Correlation** & **Fstats** & \(\mathbf{R^{2}adj}\) \\ \hline Hydro & 2.718 * & 0.963 * & 204.172 * & 0.923 \\ \hline Wind & 0.689 * & 0.742 * & 19.549 * & 0.522 \\ \hline Solar & - & - & - & - \\ \hline Bio & 1.022 * & 0.948 * & 142.271 * & 0.893 \\ \hline
**USA** & **Coefficients** & **Correlation** & **Fstats** & \(\mathbf{R^{2}adj}\) \\ \hline Hydro & 0.0781 & 0.837 & 37.564 & 0.683 \\ \hline Wind & 1.576 & 0.863 & 46.628 & 0.729 \\ \hline Solar & 0.478 & 0.605 & 9.236 & 0.326 \\ \hline Bio & 0.095 & 0.862 & 46.2 & 0.727 \\ \hline \hline \end{tabular} Note: * \(p\) value \(<\) 0.01.  
\end{table} 
Table 12: Relationship between the total RE installed capacity for each technology and the total policies. In addition, the results in Table 13 indicate that there is no autocorrelation between the data because the Durbin-Watson factor equals 1.7 (as seen in Section 5). Moreover, the low values of VIF in the same table (\(<\)5) show that the multicollinearity does not occur in the regression analysis, which proves the fitness of the established model. The normality test has been performed on the predicted China model by examining the normal Predicted Probability (P-P) plot shown in Figure 6. It has been shown in the figure that the residuals are normally distributed and conform to the diagonal normality line. In order to test for the homoscedasticity, the scatterplot shown in Figure 7 of the residuals against the fitted values is examined. If the model is well fitted, the scatterplot of the residuals does not have an obvious pattern, which is obvious in the figure. Thus, the variances of the residuals in the China model are said to have homoscedasticity.