## 3 Results  
### Hot Spot Analysis Results  
Hot spot analysis is used to find the spatial cluster patterns within a region. The results of this analysis, along with spatial regression analyses, provide meaningful conclusions. Figure 9 illustrates the energy consumption per capita in each region of Thailand from 2015 to 2022. Observing the annual changes, it is evident that energy consumption per capita is highly concentrated in the BKK&VIC, CE, EA, and WE regions (indicated in red), which are densely populated areas with diverse economic activities, including shopping malls, business districts, and industrial states. The economies of these regions are primarily driven by the service and industrial sectors [11]. In contrast, the blue areas, representing  
the NE and SO regions, exhibit lower energy consumption per capita. This is because the agricultural and service sectors mainly support the economies of these regions. Most residents work as farmers, gardeners, or providers of tourism-related services, resulting in lower overall energy usage compared to the more industrialized and urbanized regions of BKK&VIC, CE, EA, and WE [11; 80; 81]. Next, we conduct spatial econometric analyses using both the SLM and SDPD IV models with the whole-region dataset (Table 6) to obtain the most accurate estimation results. The best-performing model is then used as a benchmark for modeling other regions (BKK&VIC, CE, EA, WE, NE, NO, and SO). Table 6 presents the estimation results from different model specifications. Model 1 is estimated using a Static Spatial Panel Lag Fixed-Effects model and serves as the benchmark for comparison. Models 2, 3, and 4 are estimated using the Spatial Dynamic Panel Lag IV Fixed-Effects model for the whole region. Models 2 and 3 separate the variables _LnGPPpc_ and _LnGPPpc_ to mitigate the effects of autocorrelation. However, the estimation results of models 2 and 3 do not differ significantly from those of model 4, which includes all variables simultaneously. To compare the accuracy of model 1 and model 4, we refer to the root mean square error (RMSE) and mean absolute error (MAE) values. Model 4 has the lowest RMSE and MAE among all models, indicating that it provides the most accurate estimation. The use of instrumental variables (IVs) in models 2, 3, and 4 is supported by the Hansen J-test \(p\)-values, which are greater than 0.05 but less than 0.90. This suggests that the IVs used do not lead to over-identification or overfitting of the model. Therefore, the SDPD IV model (model 4) is selected as the reference analytical method for the study and will be applied to estimate the econometric models for other regions (e.g., BKK&VIC, CE, EA, WE, NE, NO, and SO) as it provides the most accurate estimation results. From model 4, it can be observed that the coefficients of _LnGPPpc_ and _LnGPPpc_\({}^{2}\) contradict the EKC theory. The model indicates a U-shape relationship between economic growth and energy consumption per capita (_LnEnergycons_pc_), implying that energy consumption per capita increases rapidly as the economy grows beyond a certain point. In other words, in the long run, continuous economic growth in Thailand does not lead to more sustainable energy use; instead, it results in increased energy consumption in an inefficient manner. Population density (_LnPop_dens_) is statistically significant and has a positive effect on energy consumption per capita, indicating that as population density increases, energy consumption also rises. In addition, the spatial rho (\(\mathbf{\rho}\)) coefficient in model 4 indicates that energy consumption in one province affects energy consumption in neighboring provinces. This suggests a spillover effect, meaning that energy consumption is not isolated but spatially dependent. This finding is consistent with the results of the hot spot analysis, which show that energy consumption is concentrated in specific regions of Thailand.