## 3 Results
### Hot Spot Analysis Results
In other words, when a province exhibits high energy consumption, its neighboring provinces are also likely to  
Figure 9: Hot spot analysis. Source: Authors. experience high energy consumption, reflecting spatial clusters of economic growth in certain areas. Moreover, the COVID-19 outbreak occurred from 2020 to early 2022. A dummy variable is included in the model to assess its impact on energy consumption per capita. However, the estimation results show that this variable is insignificant. Similarly, the road density variable (_LnRoaddens_) is also insignificant. Nevertheless, the direction of the coefficient remains consistent with our initial hypotheses. After obtaining the benchmark model from Table 6, we apply this method to estimate the results for the other regional datasets, as shown in Table 7.  
Table 7 presents the estimation results of the Spatial Dynamic Panel Lag IV Fixed-Effects Models, separated by region: BK&VIC, CE, EA, WE, NE, NO, and SO. The regions BKK&VIC, CE, EA, and WE were grouped together because they exhibit a high concentration of energy consumption per capita, as indicated by the hot spot analysis. Additionally, since the number of provinces in each of these regions is relatively small, they were combined to increase the number of observations and enhance the policy relevance of the analysis. According to the estimation results, the coefficients of _LnGPPpc_ and _LnGPPpc\({}^{2}\)_ in the BKK&VIC, CE, EA, WE, and NE regions suggest that the relationship between economic growth and energy consumption per capita contradicts the E \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Variables:**} & \multicolumn{2}{c}{**Static Spatial Panel Lag**} & \multicolumn{2}{c}{**Spatial Dynamic Panel Lag IV Model**} \\
**LnEnergycons\_pc** & \multicolumn{2}{c}{**Model (Whole Region)**} & \multicolumn{2}{c}{**(Whole Region)**} \\ \hline  & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} & \multicolumn{2}{c}{**Model 4**} \\ \hline LnEnergycons\_pc(\(-1\)) & na & & \(-0.074\) & & \(-0.087\) & & \(-0.081\) & \\  & & & (\(-1.13\)) & & (\(-1.36\)) & & (\(-1.36\)) & \\  & 2.060 & *** & 0.372 & *** & & \(-1.979\) & ** \\  & (4.75) & & (5.53) & & & (\(-2.04\)) & \\  & \(-0.070\) & *** & & 0.016 & *** & 0.096 & ** \\  & (\(-3.92\)) & & & & (6.07) & (2.44) & \\  & \(-0.753\) & *** & 13.117 & & 9.108 & * & 6.336 & ** \\  & (\(-8.42\)) & & & (1.62) & & (1.69) & & (2.06) & \\  & \(-2.572\) & ** & na & & na & & na & \\  & (\(-2.10\)) & & & & & & \\  & 0.013 & & & & & & \\  & (1.01) & & na & & na & & na & \\  & 0.012 & & na & & na & & na & \\  & (0.46) & & & & & & \\  & (3.86) & & & (1.50) & & (1.52) & & (1.09) & \\  & \(-0.001\) & & & 0.011 & * & 0.011 & * & 0.006 & \\  & (\(-0.06\)) & & & (1.75) & & (1.78) & & (0.84) & \\  & 0.336 & *** & 0.372 & *** & 0.347 & *** & 0.327 & ** \\  & (7.39) & & (2.48) & *** & (2.34) & *** & (2.02) & ** \\ \hline Obs & \multicolumn{2}{c}{616} & \multicolumn{2}{c}{462} & \multicolumn{2}{c}{462} & \multicolumn{2}{c}{462} \\ Instruments nb. & & & 18 & & 18 & & 18 \\ Hansen J & & & & 14.147 & & 13.768 & 10.450 & \\ (Hansen J \(p\)-value) & & & & (0.291) & & (0.316) & & (0.490) \\ AIC & \(-1840.591\) & & & & & & \\ BIC & \(-1796.359\) & & & & & & \\ RMSE & 20.889 & & 12.703 & & 8.857 & 6.225 & \\ MAE & 20.862 & & 9.302 & & 6.678 & 4.860 & \\ \hline \hline \end{tabular} Note: z-statistics are reported in parentheses. * \(p<0.10\), ** \(p<0.05\), ** \(p<0.01\).  
\end{table} 
Table 6: Regression results of the Spatial Panel Lag and Spatial Dynamic Panel Lag IV Fixed-Effects Models. exhibit a U-shape relationship, suggesting that energy consumption per capita increases as economic growth continues beyond a certain threshold. This implies that long-term economic growth may lead to less efficient energy use in Thailand. In contrast, an insignificant relationship is found between economic growth and energy consumption per capita in the NO and SO regions.