## 5 Results  
### Panel Fixed-Effects Analysis  
This study ensures reliable model specifications through systematic econometric testing1. The regression results from a two-way fixed-effects model that effectively captures unobserved heterogeneity and temporal trends by including individual and time-fixed \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variables** & **Unit** & **Mean** &
\begin{tabular}{l} **Standard** \\ **Deviation** \\ \end{tabular} & **Min** & **Max** \\ \hline Dependent variable & & & & & \\ Urban carbon emissions intensity (log) & tons/CNY & 1.674 & 1.463 & 0.084 & 11.220 \\ Independent variables & & & & & \\ Green public procurement (log) & CNY & 10.043 & 3.622 & 0.519 & 15.320 \\ Mediating variables & & & & & \\ Urban green innovation (log) & / & 5.297 & 1.713 & 0.000 & 10.372 \\ Public ecological awareness (log) & / & 4.898 & 3.647 & 0.000 & 11.222 \\ Corporate sustainability performance & / & 3.915 & 0.713 & 1.000 & 6.750 \\ Control variables & & & & & \\ Economic development (log) & CNY & 10.935 & 0.514 & 9.797 & 12.119 \\ Industrial structure & \% & 41.670 & 10.343 & 10.680 & 73.033 \\ Urban population (log) & / & 9.447 & 1.155 & 3.827 & 13.056 \\ Total retail sales & \% & 39.428 & 10.967 & 15.220 & 70.446 \\ Government intervention & \% & 21.629 & 10.769 & 8.439 & 65.339 \\ Energy structure & \% & 34.797 & 12.353 & 1.930 & 63.713 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of key variables. effects are reported in Table 2. All regression analyses used clustered robust standard errors to validate statistical inferences. The panel fixed-effects regression results presented in Table 2 consistently demonstrate a negative and statistically significant coefficient for GPP across all model specifications, which indicates that GPP policies significantly reduce urban carbon emissions intensity. Columns (1) to (6) in Table 2 indicate that the absolute magnitude of the GPP coefficient gradually diminishes with the sequential inclusion of additional control variables. This pattern suggests that factors such as economic development and industrial structure partially account for the observed urban carbon emissions intensity reduction effects. Notably, the coefficient remains statistically significant at the 1% level throughout all specifications, which confirms the robust independent explanatory power of GPP. In the fully controlled Column (7), the estimated coefficient for GPP is \(-1.548\), which implies that a 1% increase in GPP expenditures is associated with a 1.548% reduction in urban carbon emissions intensity, ceteris paribus. This finding provides robust evidence supporting H1 such that GPP serves as an effective policy instrument for reducing urban carbon emissions intensity through demand-side market incentives. Regarding the control variables, both economic development and industrial structure exhibit statistically significant negative correlations with urban carbon emissions intensity, which provides empirical support for the Environmental Kuznets Curve hypothesis in the context of urbanization ([PERSON], 2018). This finding suggests that with the expansion of aggregate economic output and advancement of technological capabilities, carbon emissions per unit of GDP decline. Specifically, economic growth and technological progress facilitate a structural shift from energy-intensive industries toward a more service-oriented economy ([PERSON], 2018; 2018), thereby substantially reducing urban carbon emissions intensity. However, government intervention demonstrates an unexpected positive effect (\(\beta=3.420\), \(p<0.01\)), which implies that excessive administrative involvement may inadvertently increase the aforementioned intensity through multiple mechanisms. First, rapid government-driven urbanization and industrialization can lead to rigid energy consumption patterns ([PERSON], 2018), thereby elevating urban carbon emissions.