## 3 Research Methodology
### Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **ca** & **re** & **en** & **gr** & **in** & **fdi** & **ur** & **ec** \\ \hline ca & 1.000 & & & & & & & & \\ re & \(-\)0.329 *** & 1.000 & & & & & & & \\ en & \(-\)0.237 ** & 0.103 & 1.000 & & & & & & \\ gr & \(-\)0.194 *** & 0.097 ** & 0.195 & 1.000 & & & & & \\ in & 0.243 *** & 0.159 & \(-\)0.118 ** & 0.213 * & 1.000 & & & & \\ fdi & \(-\)0.113 * & 0.303 & \(-\)0.106 *** & \(-\)0.245 & 0.467 ** & 1.000 & & & \\ ur & 0.259 *** & 0.143 * & \(-\)0.094 & 0.187 & 0.231 & 0.106 & 1.000 & & \\ ec & 0.502 ** & 0.238 *** & \(-\)0.173 *** & 0.432 *** & 0.386 *** & 0.268 ** & 0.211 ** & 1.000 \\ \hline \hline \end{tabular} Note.* 10% significance level; ** 5% significance level; 1% significance level.  
\end{table} 
Table 2: Results of correlation test. Furthermore, performing a cross-sectional dependency test is vital for this study to identify and mitigate potential correlations between cross-sectional units that may introduce bias into the econometric model's outcomes. Within the OECD context, economic and environmental policies, along with external shocks, frequently have interconnected effects across various countries. Detecting cross-sectional dependency enables the model to account for these interdependencies, thereby enhancing the precision and reliability of the empirical results. This procedure is crucial for validating the model's assumptions and ensuring that the derived conclusions are robust and accurately represent the genuine relationships among the variables under investigation. Table 3 delineates the results of the cross-sectional dependency test, revealing statistically significant values for all the variables analyzed. The high levels of significance indicate a pronounced cross-sectional dependence throughout the dataset. This suggests that economic and environmental factors in OECD countries are highly interconnected, thereby necessitating the incorporation of cross-sectional dependencies within the econometric model. Addressing these dependencies enhances the robustness of the model, ensuring that the estimated impacts of renewable energy, environmental taxes, and other control variables on CO2 emissions are accurately captured and not distorted by unaccounted interdependencies among the nations. Based on the findings in Table 3, the employment of the cross-sectional Im-Pesaran-Shin (CIPS) and modified cross-sectional Im-Pesaran-Shin (M-CIPS) unit root tests in this study is thoroughly justified, as they proficiently account for cross-sectional dependence within the panel data of OECD countries. These tests are indispensable for confirming the stationarity of the variables, which is a crucial prerequisite for sound econometric modeling. The CIPS test, through its cross-sectional augmentation, addresses potential biases stemming from cross-sectional dependencies, while the M-CIPS test further reinforces robustness by integrating multiple cross-sectional averages. Together, these tests provide a comprehensive assessment of the data's stationarity, thereby ensuring the validity and reliability of the ensuing econometric analyses and conclusions. The detailed results are presented in Table 4.  
Table 4 delineates the outcomes of the CIPS and M-CIPS unit root tests, demonstrating that all variables achieve stationarity upon first differencing, as indicated by the significant test statistics. These results confirm the absence of unit roots in the dataset, thereby fulfilling the stationarity prerequisite essential for reliable econometric modeling. Consequently, these findings ensure the robustness and validity of subsequent analyses, facilitating an accurate investigation of the relationships between CO2 emissions and the independent variables under examination, including renewable energy, environmental taxes, and other control variables within the OECD countries' context.