## 6 Empirical Results and Analysis
### Heterogeneity Test  
There are obvious regional differences in the level of green technology innovation in China, especially regarding the eastern cities, which are developing rapidly at a level higher \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** \\ \hline
**Variables** & **InGreenPatent** & **COAGG\_psSManu** & **InGreenPatent** \\ \hline lnHSRFreq1 & 0.091 *** & 0.048 ** & 0.089 *** \\  & (4.21) & (2.23) & (4.11) \\ COAGG\_ps8 Manu & & & 0.046 ** \\  & & & (2.13) \\ sci\_GDP & 0.581 * & 0.104 & 0.576 * \\  & (1.91) & (0.36) & (1.91) \\ Inbooks\_100\_1 & 0.144 *** & 0.027 & 0.143 *** \\  & (3.12) & (0.72) & (3.12) \\ Inmobile1 & \(-\)0.086 ** & \(-\)0.009 & \(-\)0.085 ** \\  & (\(-\)2.41) & (\(-\)0.62) & (\(-\)2.40) \\ Fin\_GDP & \(-\)0.121 ** & 0.062 & \(-\)0.124 ** \\  & (\(-\)2.33) & (1.55) & (\(-\)2.38) \\ unemp\_Pop & \(-\)0.085 ** & 0.045 * & \(-\)0.087 ** \\  & (\(-\)2.23) & (1.78) & (\(-\)2.27) \\ gyfs\_GDP & \(-\)0.068 *** & 0.000 & \(-\)0.068 *** \\  & (\(-\)2.60) & (0.03) & (\(-\)2.60) \\ Constant & \(-\)3.088 *** & 2.582 *** & \(-\)3.206 *** \\  & (\(-\)12.73) & (19.70) & (\(-\)13.27) \\ Observations & 2840 & 2840 & 2840 \\ R-squared & 0.360 & 0.016 & 0.361 \\ Number of cities & 284 & 284 & 284 \\ City FE & Yes & Yes & Yes \\ Time FE & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The empirical results of the mediating effect model. than the national average. So, it is necessary for this paper to further study whether there are considerable regional differences in the promoting force of high-speed rail. Specifically, this article divides the sample into eastern, central, and western cities on the basis of prefecture-level cities to separately explore the effect of high-speed rail service frequency. The results of sub-sample regression are displayed in Table 5. Column (1) lists the regression results of 115 cities located in eastern China. The regression coefficient is positive, but it is not statistically significant. This means that for eastern cities, high-speed rail is unable to stimulate the progress of their green technology innovation level effectively. [PERSON] and [PERSON] (2019) also observed similar results. Combined with their analysis, this article claims that there are two major explanations for this result. Firstly, the level of green innovation in most parts of the east has been relatively developed, which means that eastern cities have no additional room for significant growth in the short term. Secondly, a high-speed rail connection will cause more people to flock to developed cities that are already overloaded. This will encourage the government to fund the supply of basic social public services rather than green innovation, thereby constraining the city's green technology innovation. Column (2) reports the regression results of 108 cities in central China. It can be seen that the coefficient of high-speed rail service frequency is 0.057, and it is significantly positive at the 5% confidence level. This indicates that for the central region, the supply of high-speed rail services is an important factor in enhancing the level of green technology innovation. The governments of central cities have always been focused on providing a better employment environment to attract talented and high-skilled workers to stimulate their development. The transportation convenience brought by high-speed rail construction accelerates the transfer of some high-tech workers to middle and small-sized cities in the central region, which has a positive impact on the level of the city's green technology innovation. From the regression result of column (3), it is proved that high-speed rail has brought about a dramatic and positive influence on green technology innovation in 60 western cities. This may be because the green technology innovation level of western cities is relatively backward compared with that of central and eastern cities, and the gap is relatively large.