## 5 Empirical Results and Discussion
### Regression Results
The results also show that the energy consumption per capita (_LnEnergy_cons_pc_) has a positive relationship with PM\({}_{2.5}\) concentrations. Provinces with a higher energy consumption per capita tend to have higher PM\({}_{2.5}\) levels, as the energy consumption per capita is a key indicator of economic activity [55]. Provinces with a high energy consumption per capita typically have strong economic activities, large industrial parks,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dependent** & \multicolumn{3}{c}{**Random Effects**} & \multicolumn{3}{c}{**SLM**} & \multicolumn{3}{c}{**SEM**} \\ \hline
**Variable** & _LnPPM\({}_{2.5}\) & & & & & & & & \\ \hline
**Variables** & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** & **M7** & **M8** & **M9** \\ \hline _LnPop\_dens_ & \((4.12)\) & \((4.17)\) & \((3  
tourist attractions, business districts, and residential areas. However, industrial density (_LnIndus_dens_) was found to be insignificant in explaining PM\({}_{2.5}\) concentrations. From the spatial regression results of both the Spatial Lag Panel and Spatial Error Panel Random Effects Regression, it is clear that the findings remain consistent across models. M4 and M5 and M7 and M8 (Table 7) are estimated by including _LnGPPpc_ and _LnGPPpc\({}^{2}\)_ separately to avoid multicollinearity issues. However, their estimation results are not different from those of M6 and M9, which include all variables together. The estimation results from M6 and M9 indicate that provinces with a high population density (_LnPop_dens_) tend to have higher PM\({}_{2.5}\) concentrations because they are consistent sources of pollution. In other words, an increase in population density within a province leads to a rise in PM\({}_{2.5}\) concentrations. Specifically, if the population density increases by 1%, the PM\({}_{2.5}\) concentration will increase by 12% for the case of M6 but 5% in M9. Additionally, M6 and M9 reveal a U-shaped relationship between PM\({}_{2.5}\) concentration and economic growth (_LnGPPpc_ and _LnGPPpc\({}^{2}\)_), which is in contrast to the Environmental Kuznets Curve (EKC) theory. This suggests that while economic development initially reduces environmental degradation, at a certain point, as the economy expands further, it begins to negatively impact the environment, leading to rapid deterioration. In other words, in the early stages of economic growth, its effect on environmental degradation is minimal. However, when the economy reaches a large scale, further expansion intensifies environmental damage. Regarding the energy consumption per capita (_LnEnergy_cons_pc_), the results show that a higher energy consumption per capita leads to increased PM\({}_{2.5}\) concentrations. Specifically, if the energy consumption per capita increases by 1%, PM\({}_{2.5}\) concentration will increase by 17% in M6 and 6% in M9 models, respectively. This is because the energy consumption per capita serves as a key indicator of economic activity within a province. Provinces with high energy consumption per capita typically have strong economic activity, large industrial parks, tourist attractions, business districts, and residential areas. From the Spatial Lag Panel Random Effects Regression (M6), we can see that industrial density (_LnIndus_dens_) is statistically significant at the 1% level but has a negative sign, meaning that as industrial density increases, the PM\({}_{2.5}\) concentration will decrease. 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. Both the rho (\(\rho\)) and lambda (\(\lambda\)) variables from M6 and M9 are statistically significant at the 1% level, indicating that PM\({}_{2.5}\) concentration spills over from one province to its surrounding provinces.