## 4 Results and Discussion  
Table 1 contains summary statistics that clarify the nature of the dataset used in this inquiry. The mean values are significantly greater than the standard deviation values. This outcome suggests that the data distribution has a reduced spread, implying that data cluster around the mean. The Kurtosis and Jarque-Bera coefficients, as well as their p-values, are also given in Table 1. The Kurtosis values are in the range of 2-3, indicating that the data distribution is normal. This information is supported by the Jarque-Bera \(p\)-value, which is greater than 0.05, indicating that the datasets are normally distributed. The [PERSON], [PERSON], and [PERSON] group unit root test (Table 2) was utilized in this investigation to verify that the datasets were all stationary at level or order zero. The goal was to guarantee that the regression result was not deceptive owing to temporal delays, which is why unit root testing was required. The Ramsey RESET test (Table 3) and the CUSUM test (Figure 1) validate the stability of the regression model. The results of the two tests show that the model used in this inquiry is consistent and realistic. In the case of the CUSUM test, the appearance of the blue line between the two red dotted lines denotes the 5 percent level of significant bounds, implying model robustness. When the blue line crosses the red dotted lines from either direction, it means that the model is unstable and should not be trusted. However, in this situation, the model is proven to be reliable and pertinent to the investigation. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **CO\({}_{2}\)** & **LCX** & **LRX** \\ \hline Mean & 4.600120 & 4.465793 & 5.584906 \\ \hline Median & 4.608013 & 4.990734 & 6.257139 \\ \hline Maximum & 4.893802 & 6.332516 & 7.267246 \\ \hline Minimum & 4.361441 & 1.406097 & 2.636912 \\ \hline Std. Dev. & 0.139971 & 1.555252 & 1.711317 \\ \hline Skewness & 0.375890 & \(-\)0.748760 & \(-\)0.625676 \\ \hline Kurtosis & 2.514722 & 2.197719 & 1.835295 \\ \hline Jarque–Bera & 0.934113 & 3.367261 & 3.409492 \\ \hline Probability & 0.626845 & 0.185699 & 0.181819 \\ \hline Sum & 128.8033 & 125.0422 & 156.3774 \\ \hline Sum Sq. Dev. & 0.528984 & 65.30786 & 79.07232 \\ \hline Observations & 28 & 28 & 28 \\ \hline Authors’ computation, 2022. & & & \\ \end{tabular}
\end{table} 
Table 1: Summary statistics. Table 3 displays the results of the numerous diagnostic tests that confirm the applicability of our regression model. As depicted in Table 3, there is no Heteroskedasticity in the model, implying that the model has homogeneous variance across the board. There is also no multicollinearity, inferring that the independent variables are not in any way connected in operation. That is, there is no interconnection between the predictor factors that explains CO\({}_{2}\) emissions in Nigeria's local government regions. Figure 2 depicts the normality of the histogram, which helps to establish that the model is standard. The statistical data stress Kurtosis, which is about 3, and the Jarque-Bera test, which has a \(p\)-value larger than 0.05. When the Kurtosis is 3, the data are normally \begin{table}
\begin{tabular}{l c c c} \hline \hline Series: Ln\_CO\({}_{2}\), Ln\_LCX, Ln\_LRX & & \\ Sample: 1993 2020 & & & \\ \hline Method & Statistic & Prob. ** & Cross \\  & & & \\ \hline Null: Unit root (assumes common unit root process) & & & \\ \hline [PERSON], Lin, and Chu t * & \(-\)2.4868 & 0.0064 & 3 & 79 \\ \hline \hline \end{tabular}  
* indicate that the unit root test tool applied is propounded by [PERSON], Lin, and Chu t. ** \(p\)-value is significant at level 1% < 5%. Authors’ computation, 2022.  
\end{table} 
Table 2: Unit root test summary. Figure 1: Stability test confirmation. distributed, and when the \(p\)-value of the Jarque-Bera test is greater than the 5% level of significance, the model and dataset are both normal. Table 4 explains the amount of connection between the variables (dependent and independent) employed in this research.