## 5 Discussion  
### Regression Analysis of Spatial Econometric Model  
In order to obtain more robust results, the estimation results of the SDM are compared with the results of the SLM and SEM. Corresponding regression results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Statistic** & **df** & \(p\)**-Value** \\ \hline Spatial error: & & & \\ Moran’s I & 68.691 & 1 & 0.000 \\ Lagrange multiplier & 4.195 & 1 & 0.041 \\ Robust Lagrange multiplier & 4.384 & 1 & 0.036 \\ Spatial lag: & & & \\ Lagrange multiplier & 0.004 & 1 & 0.018 \\ Robust Lagrange multiplier & 0.192 & 1 & 0.661 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of LM test. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & **Likelihood-Ratio Test** & \(p\)**-Value** \\ \hline SAR nested in SDM & 33.84 & 0.000 \\ SEM nested in SDM & 32.77 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of LR test. According to the estimation results in Table 6, it can be found that, there is not much difference between the regression results of the SDM and the results of other models, indicating that the SDM model is stable. The specific regression results of the SDM are as follows. Firstly, there is a spatial positive correlation in S&T innovation ability in China. According to the regression results of the SDM in Table 6, rho, which is the spatial correlation coefficient, is significantly positive at the level of 1%, indicating that China's regional S&T innovation ability has a significant positive correlation between 30 provinces. The result indicates that the improvement of S&T innovation ability of a province will have a significant impact on the improvement of S&T innovation ability of its neighboring provinces. For example, the outstanding S&T innovation ability in Shanghai has driven the improvement of S&T innovation ability of its surrounding provinces such as Jiangsu. Secondly, the effect of GPSTPS policy on improving regional S&T innovation ability is not obvious. It can be seen from Table 6 that the influencing coefficient of pol in regression results of the SDM is positive, but it is not significant. The result shows that GPSTPS policy is positively correlated with regional S&T innovation ability, but the effect between them is not obvious. That means there is no significant positive correlation between GPSTPS policy and the improvement of S&T innovation ability. The possible reasons are as follows. (1) From the macro perspective of policy system, the policy system for GPSTPS is not sound and perfect. Although laws and regulations at the national level such as \"Government Procurement Law of the People's Republic of China\" have mentioned to make extensive use of social forces to improve the supply efficiency of S&T public services and adopt the policy of GPSTPS, local governments rarely define the specific purchase contents, purchase objects and purchase methods of GPSTPS, which leads to low implementation efficiency and unsound implementation effect of GPSTPS. Therefore, due to the imperfect policies of GPSTPS, the regional S&T innovation ability cannot be improved. (2) From the micro perspective of purchase content, the purchase scope of GPSTPS policy is relatively narrow. Nowadays, the specific purchase contents in GPSTPS are mostly limited to basic fields such as S&T information, S&T appraisal and S&T personnel training, while there is little practice in purchasing more professional S&T services such as S&T finance, S&T promotion and so on. As a result, regional S&T innovation ability cannot be improved without professional and abundant S&T services supply. The typical case behind this econometric finding is the province of Anhui, where the S&T innovation ability is low. Through retrieving the GPSTPS policy in Anhui, it can be found that there are few regulations or policies that define the specific purchase contents, purchase objects and purchase methods. Imperfect GPSTPS policies in Anhui hinders the improvement of regional S&T innovation ability.