## 6 Discussion  
The Bivariate Local Moran's I test results indicate that the PM\({}_{2.5}\) data across the three years exhibit a spatial clustering pattern. In other words, the PM\({}_{2.5}\) concentration in one province influences the PM\({}_{2.5}\) concentration in nearby provinces, forming spatial clusters. Therefore, this study applies spatial econometric methods to analyze the spread of PM\({}_{2.5}\) pollution and its impact on neighboring provinces. Since all provinces have a spatial relationship with each other, this study employs both non-spatial and spatial panel econometric models. The results indicate that the findings from both models are consistent, with similar statistically significant variables. The findings show that population density and the energy consumption per capita have a positive relationship with the PM\({}_{2.5}\) concentration. In contrast, industrial density has a negative relationship with the PM\({}_{2.5}\) concentration, meaning that as industrial density increases, the PM\({}_{2.5}\) concentration decreases. Since the PM\({}_{2.5}\) pollution problem is not confined to a single province but has spatial impacts, this study's use of provincial-level data without considering geographical differences and climate variations in each area may lead to limitations. Specifically, it could result in the incorrect estimation of industrial density, which could yield a negative outcome. However, as shown in Table A2 of Appendix B, which categorizes the regions with the highest concentration of manufacturing industries, the estimation results indicate a positive relationship between regions with large manufacturing industries and those heavily polluted by PM\({}_{2.5}\). Moreover, the spatial autocorrelation coefficients (rho and lambda) from the SLM and SEM are statistically significant at the 1% level, indicating that the PM\({}_{2.5}\) concentration exhibits a spillover effect. In other words, if the PM\({}_{2.5}\) concentration in one province increases, the PM\({}_{2.5}\) concentration in nearby provinces will also increase. In the SDM, the weighted variables--PM\({}_{2.5}\), GPP per capita, and energy consumption per capita--were statistically significant. This indicates that an increase in these variables in one province will lead to a rise in PM\({}_{2.5}\) concentration in nearby provinces. The Gross Provincial Product (GPP) per capita and energy consumption per capita can be explained by the principle of spillover effects, as PM\({}_{2.5}\) pollution is a spatial issue. When a province generates a high level of pollution due to its economic development, topographical and climatic factors can cause PM\({}_{2.5}\) pollution to spread to neighboring provinces. This aligns with our research findings, suggesting that PM\({}_{2.5}\) pollution is a transboundary problem. The non-spatial and spatial panel econometric model analysis of the effect of the GPP per capita on PM\({}_{2.5}\) concentration found a U-shaped pattern, which is in contrast to the Environmental Kuznets Curve (EKC) theory. This means that, in the initial phase, economic development tends to reduce environmental degradation. However, at a certain point, as the economy grows larger, it leads to accelerated environmental deterioration. This result is aligned with those of previous studies [32; 39; 40]. The reason is that the PM\({}_{2.5}\) problem has been accumulating over a long period, making its impact more noticeable. Additionally, this issue has a broad spatial impact. Moreover, Thailand has not carefully designed policies to address PM\({}_{2.5}\) pollution. As a result, although its economic development initially had a