## 4 Discussion  
The results from the Moran's I statistical test and hot spot analysis indicate that energy consumption exhibits a spatial clustering pattern. This means that energy consumption is highly concentrated in regions with intense economic activity and established economic centers, such as BKK&VIC, CE, EA, and WE. In contrast, energy consumption is less concentrated in areas farther from these economic hubs, particularly in regions primarily driven by the agricultural sector, such as NE and SO. Furthermore, high energy consumption in one area tends to lead to increased energy consumption in surrounding areas due to spillover effects. Based on these findings, this study applies spatial econometric methods to analyze the spatial distribution of energy consumption and its impact on neighboring provinces. From model 4 (Spatial Dynamic Panel Lag IV Fixed-Effects Model), it is evident that the relationship between GPP per capita and energy consumption contradicts the EKC theory, which typically suggests an inverted U-shape relationship between economic growth and environmental impact [40; 41; 42; 43]. Instead, the results indicate a U-shape relationship between economic growth and energy consumption per capita, suggesting that after the economy reaches a certain level of growth, energy consumption increases rapidly. In other words, continued economic growth leads to rising energy consumption and inefficient energy use. Additionally, population density is statistically significant and positively correlates with energy consumption per capita, indicating that higher population density is associated with increased energy use. This is because areas with high population density tend to have large economic activities and are highly urbanized [59; 61; 62]. Moreover, the spatial autocorrelation coefficients (rho) from both the Spatial Lag Model (SLM) and the Spatial Dynamic Panel Lag IV Model (SDPD IV) are statistically significant, indicating a strong spillover effect. This means that an increase in energy consumption in one province also leads to increased energy consumption in neighboring provinces. In addition, the analysis of GPP per capita and its effect on energy consumption in regions such as BKK&VIC, CE, EA, WE, and NE reveals that the relationship between economic growth and energy consumption per capita contradicts the EKC theory. These regions exhibit a U-shape relationship, indicating that energy consumption per capita increases as economic growth surpasses a certain threshold. In contrast, no significant relationship was found between economic growth and energy consumption per capita in the NO and SO regions. Using the Spatial Dynamic Panel Lag IV Model (SDPD IV), we also estimated the effects of other variables on energy consumption per capita across different regions of Thailand. The results show that road density has a positive and statistically significant relationship with energy consumption per capita in most regions, particularly in BKK&VIC, CE, EA, WE, NE, NO, and SO. From the analysis of short-run and long-run impacts, the findings for the whole region suggest that the effect of economic growth on energy consumption per capita is greater in the short term than in the long term. This implies that as the economy continues to grow, energy consumption will initially increase more sharply but may gradually level off or decline over time. Moreover, population density is expected to have a more favorable impact on energy consumption in the long run, indicating improved efficiency over time. In contrast, no significant short- or long-term impacts of economic growth on energy consumption per capita were observed in the BKK&VIC, CE, EA, WE, NE, NO, and SO regions. From the GAM analysis, it can be concluded that provinces with high GPP per capita, land surface temperature at night, rainfall, wind speed, and road density also tend to have high energy consumption per capita.