## 5 Results
### Spillover Effects Analysis
#### 5.3.3 Spatial Spillover Effects  
Based on prior spatial dependence tests, we employ both Random Effects (RE) and Two-Way Fixed Effects (FE) spatial SDM for empirical analysis; the results are reported in Table 8. In the FE SDM, the coefficient for GPP is \(-1.368\), and statistically significant at the 1% level. This suggests that a 1% increase in local GPP expenditures leads to a 1.368% reduction in urban carbon emissions intensity. Furthermore, the spatial interaction term coefficient (\(W\)\(\times\) Green public procurement) is \(-15.580\) and significant at the 1% level, thereby implying that local GPP may also contribute to carbon emissions intensity reduction in neighboring cities through policy diffusion and technology spillovers. These findings demonstrate the synergistic effect of GPP initiatives in promoting cross-regional carbon emissions intensity reduction.  
\begin{table}
\begin{tabular}{l l l} \hline  & **(1) Urban Carbon** & **(2) Urban Carbon** \\  & **Emissions Intensity** & **Emissions Intensity** \\ \hline Green public procurement & \(-1.451\) *** & \(-1.368\) *** \\  & (0.408) & (0.384) \\ Economic development & \(-4.035\) *** & \(-4.184\) *** \\  & (0.134) & (0.128) \\ Industrial structure & \(-2.173\) *** & \(-2.277\) *** \\  & (0.400) & (0.379) \\ Urban population & \(-2.880\) *** & \(-6.992\) *** \\  & (0.618) & (0.955) \\ Total retail sales & \(-0.782\) *** & \(-0.677\) *** \\  & (0.216) & (0.206) \\ Government intervention & 3.571 *** & 3.575 *** \\  & (0.431) & (0.406) \\ Energy structure & \(-2.360\) & \(-5.1360\) \\  & (7.569) & (7.410) \\ \(W\)\(\times\) Green public procurement & \(-14.930\) *** & \(-15.580\) *** \\  & (3.727) & (3.886) \\ \(W\)\(\times\) Economic development & \(-5.897\) *** & \(-5.784\) *** \\  & (1.174) & (1.611) \\ \(W\)\(\times\) Industrial structure & \(-0.372\) & \(-0.513\) \\  & (1.755) & (1.651) \\ \(W\)\(\times\) Urban population & 75.114 *** & 83.189 *** \\  & (10.852) & (15.055) \\ \(W\)\(\times\) Total retail sales & 2.134 *** & 2.112 *** \\  & (0.717) & (0.676) \\ \(W\)\(\times\) Government intervention & \(-20.820\) *** & \(-21.560\) *** \\  & (3.481) & (3.729) \\ \(W\)\(\times\) Energy structure & 400.1 ** & 418.8 ** \\  & (1779) & (1739) \\ Fixed-effects & No & Yes \\ \hline \end{tabular} Notes: *\(p\)\(<\) 0.1, ***\(p\)\(<\) 0.01.  
\end{table}
Table 8: Estimation results of the spatial Durbin model.  
From the perspective of spatial effects in the control variables, after applying spatial weight matrices to the independent variables, only the spatial interaction term of industrial structure (\(W\times\) Industrial structure) is statistically insignificant, while all other variables exhibit significant spatial spillover effects. For instance, the coefficient of urban population is significantly negative, while its spatial interaction term (\(W\times\) Urban population) is significantly positive. This indicates that local population growth may reduce carbon emission intensity through agglomeration effects, whereas population expansion in neighboring cities can exacerbate emission intensity through negative externalities such as regional energy interdependence spillovers.