## 6 Empirical Results and Analysis
### Results of Baseline Model Regression  
Under the premise of using a two-way fixed effect model, we tested each variable individually to ensure the robustness of the regression results. The specific regression results are displayed in Table 3. All estimation results revealed that, after adding the control variables individually, the influence of the high-speed rail service frequency on urban green technology innovation always remained dramatically optimistic at the confidence level of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **VIF** & **1/VIF** \\ \hline lnbooks\_100\_1 & 1.55 & 0.645504 \\ sci\_GDP & 1.49 & 0.671654 \\ lnHSRFreq1 & 1.45 & 0.691791 \\ immobile1 & 1.42 & 0.703521 \\ Fin\_GDP & 1.37 & 0.731019 \\ unemp\_Pop & 1.14 & 0.873686 \\ gyfs\_GDP & 1.07 & 0.932161 \\ \hline Mean VIF & 1.36 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multicollinearity test. 1%, and there was little difference in magnitude. As shown by the coefficient in column (7), for every 1% increase in the frequency of high-speed rail services in a city, the number of green patent applications per 10,000 people increases significantly by about 9.1%. This implies that high-speed rail is a critical catalyst for urban green technology innovation improvement and is able to encourage the advance of green technology innovation effectively. By viewing the coefficients of the control variables, it can be found that government support and public cultural infrastructure have a meaningful positive effect on the output of green technology innovation. To some extent, government support can compensate for the higher research and development costs and external risks in the innovation activities of green innovative enterprises. Meanwhile, the per capital book collection in a city can reflect a city's basic amount of knowledge reserves. This shows that a city with a stronger public cultural foundation has a stronger ability to innovate in green technology. Conversely, the regression results show that the level of communication technology, financial development, economic system vitality, and the degree of environmental pollution have a considerable negative correlation with the number of green patent applications per 10,000 people. This can be clarified by the fact that, although the improvement of information and communication technology has improved the convenience of people's communication, it has reduced the demand for face-to-face communication among technicians, thus inhibiting the spread of knowledge related to green technology to a certain extent. Besides, despite the high level of financial development, funds may not flow to the green technology innovation market with high-risk characteristics, leading to the problem of financing constraints for enterprises in green innovation activities. Next, the increase in the unemployed population will cause local governments to shift their development focus from attracting high-tech personnel and developing green innovations to improving people's employment and welfare. Furthermore, cities with high levels of environmental pollution are generally those with secondary industries such as manufacturing and industry as the focus of local economic development; cities located in central and western China are also particularly affected by pollution. These cities usually have unclear goals for sustainable economic development