## 5 Results
### Robustness Tests
#### 5.4.2 Robustness Tests of the Spatial Effects  
Table 11 presents the robustness test results. First, to mitigate the unusual impact of the COVID-19 pandemic, we run a regression analysis excluding samples from 2020. Column (1) reveals that both the direct effect (\(-1.754\)) and spatial spillover effect (\(-50.20\)) of GPP remain significant, and exhibit an increased intensity in urban carbon emissions intensity reduction compared to the panel fixed-effects model. This finding indicates that the pandemic reduced policy effectiveness. Second, after performing 2% tail trimming on key variables ([PERSON] et al., 2020), neither the direction nor significance of the coefficients changed (direct effect: \(-0.961\); indirect effect: \(-13.330\)), which confirms that outliers did not influence the main findings. Finally, using an economic geography nested matrix (including interaction terms for GDP and distance), the spatial autocorrelation coefficient \(\rho\) remains significantly negative (\(I=-0.071\); \(p<0.01\)). Additionally, the direction of policy effects (direct: \(-1.553\); indirect: \(-2.256\)) align with those observed in the panel fixed-effects model.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Urban Carbon Emissions** & \multicolumn{3}{l}{**(1) Excluding 2020**} & \multicolumn{3}{l}{**(2) Trim the Tail**} & \multicolumn{3}{l}{**(3) Replace with Carbon Emission**} \\
**Intensity** & \multicolumn{3}{l}{**(1) Excluding 2020**} & \multicolumn{3}{l}{**(2) Trim the Tail**} & \multicolumn{3}{l}{**Intensity per Unit of Secondary Output**} \\ \hline Green public procurement & \(-1.992\)\({}^{***}\) & \(-1.735\)\({}^{***}\) & \(-1.655\)\({}^{***}\) & \(-1.312\)\({}^{***}\) & \(-2.617\)\({}^{***}\) & \(-2.047\)\({}^{***}\) \\ Control Variables & No & Yes & No & (0.453) & (0.859) & (0.472) \\  & 98.906\({}^{***}\) & \(-7.518\)\({}^{**}\) & 91.656\({}^{***}\) & 0.456 & 21.845\({}^{***}\) & \(-9.182\)\({}^{***}\) \\ Constant & (5.605) & (3.047) & (5.391) & (2.508) & (8.277) & (2.930) \\ N & 2568 & 2568 & 2568 & 2568 & 2568 & 2568 \\ R-squared & 0.336 & 0.641 & 0.322 & 0.604 & 0.081 & 0.715 \\ \hline \hline \end{tabular} Notes: \({}^{**}p<0.05\), \({}^{***}p<0.01\).  
\end{table}
Table 10: Results of robustness tests for baseline regression.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Urban Carbon Emissions** & \multicolumn{3}{l}{**Direct Effect**} & \multicolumn{3}{l}{**Indirect Effect**} & \multicolumn{3}{l}{**Total Effect**} \\
**Intensity** & \multicolumn{3}{l}{**Coefficient**} & \multicolumn{3}{l}{**t-Statistic**} & \multicolumn{3}{l}{**Coefficient**} & \multicolumn{3}{l}{**t-Statistic**} & \multicolumn{3}{l}{**Coefficient**} & \multicolumn{3}{l}{**t-Statistic**} \\ \hline Green public procurement & \(-1.360\)\({}^{***}\) & \(-3.53\) & \(-14.510\)\({}^{***}\) & \(-5.29\) & \(-15.87\)\({}^{***}\) & \(-5.66\) \\ Economic development & \(-4.194\)\({}^{***}\) & \(-38.46\) & \(-4.990\)\({}^{***}\) & \(-5.19\) & \(-9.184\)\({}^{***}\) & \(-9.60\) \\ Industrial structure & \(-2.239\)\({}^{***}\) & \(-5.56\) & \(-0.401\) & \(-0.26\) & \(-2.640\)\({}^{*}\) & \(-1.74\) \\ Urban population & \(-6.957\)\({}^{***}\) & \(-6.77\) & \(77.720\)\({}^{***}\) & 6.44 & \(70.763\)\({}^{***}\) & 5.78 \\ Total retail sales & \(-0.715\)\({}^{***}\) & \(-2.98\) & \(2.035\)\({}^{***}\) & 2.89 & \(1.320\)\({}^{*}\) & 1.82 \\ Government intervention & \(3.591\)\({}^{***}\) & \(9.52\) & \(-20.470\)\({}^{***}\) & \(-6.60\) & \(-16.87\)\({}^{***}\) & \(-5.31\) \\ Energy structure & \(-5.210\) & \(-0.71\) & \(418.9\)\({}^{***}\) & 2.67 & \(413.7\)\({}^{***}\) & 2.62 \\ \hline \hline \end{tabular} Notes: \({}^{*}p<0.1\), \({}^{***}p<0.01\).  
\end{table}
Table 9: Decomposition results of spatial spillover effects.