## 4 Empirical Analysis
### Results Analysis and Discussion
#### 4.2.3 Convergence Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Double Fixed Effect**} & \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Double Fixed Effect**} \\  & & **(\(t\) Value)** & & **(\(t\) Value)** \\ \hline \multirow{2}{*}{\(\beta\)} & \(-0.385\)******* & \multirow{2}{*}{\(\text{ln}gi\)} & \(-0.118\)******* \\  & & & (\(-3.075\)) \\ \hline \multirow{2}{*}{\(\text{ln}led\)} & 0.102 ** & \multirow{2}{*}{\(\text{ln}si\)} & 0.0198 ** \\  & (2.163) & & (0.207) \\ \hline \multirow{2}{*}{\(\text{ln}tids\)} & 0.149 *** & \multirow{2}{*}{\(\text{ln}ptse\)} & 0.024 ** \\  & (3.516) & & (2.249) \\ \hline \multirow{2}{*}{\(\text{ln}ti\)} & 0.214 *** & \multirow{2}{*}{\(R\)–squared} & \multirow{2}{*}{0.784} \\  & (3.843) & & & \\ \hline \multicolumn{4}{l}{\(Adj.R^{2}\)} & 0.318 & \multicolumn{2}{l}{_Log-likelihood_} & 658.498 \\ \hline \hline \end{tabular} Note: ** and *** indicate 5% and 1% significance levels, respectively.  
\end{table} 
Table 10: Conditional \(\beta\)-convergence SLM model estimation and test results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std. dev**} \\ \hline _led_ & 441.36 & 80,854.91 & 19,416.23 & 0.016 \\ _gi_ & 0.005 & 0.124 & 0.021 & 0.027 \\ _tids_ & 0.02 & 0.10 & 0.05 & 0.016 \\ _ta_ & 0.03 & 1.52 & 0.82 & 0.52 \\ _ti_ & 0.04 & 0.96 & 0.29 & 0.17 \\ _ptse_ & 1993.80 & 3290.60 & 2298.06 & 920.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Descriptive statistics of conditional variables. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**LM\_Error**} & \multirow{2}{*}{**Robust LM\_Error**} & \multirow{2}{*}{**LM\_Lag**} & \multirow{2}{*}{**Robust LM\_Lag**} \\ _(\(p\) Value)_ & & & **(\(p\) Value)** & **(\(p\) Value)** \\ \hline
8.419 & 7.492 & 6.994 & 5.012 \\ (0.021) & (0.018) & (0.011) & (0.005) \\ \hline \hline \end{tabular} Note: \(p\)-values are significant at the 1% level.  
\end{table} 
Table 9: Correlation estimation results of regional \(ITE\) convergence. Using Formula (7) to investigate the results of the spatial lag model of conditional \(\beta\)-convergence, the result of the \(\beta\)-convergence emerged as \(-0.385\). The significance level of 1% here indicates that there exists a conditional \(\beta\)-convergence of regional integrated transport efficiency in China. The convergence rate was found to be 6.007% and 11.41% for the half-life cycle. Compared with the absolute \(\beta\)-convergence, after the inclusion of conditional variables, the convergence rate was seen greatly to improve. In the estimation results, the coefficient of government intervention (\(gi\)) was negative and passed the 1% significance test, highlighting a negative correlation between this variable and \(ITE\). This implies that the greater the government's expenditure for transport as a percentage of GDP might make low utilization of capital input and the lower the \(ITE\). The relationship between the other conditional variables and transport efficiency was found to be positive. Moreover, the level of economic development, household per capita consumption of transport, increase in the size of the transport industry, technological advancement, and increase in traffic intensity were also all seen to have important implications for the convergence of regional integrated transport efficiency. Among them, the promotion of technological advancement emerged as most significant. An increase of 1% in technological progress means 21.4% increase in \(ITE\), which is mainly reflected in the greater added value in transport industry in terms of unit energy consumption. Estimates of the scale of development of the transport industry indicate that an increase in output factors will result in significant improvements in \(ITE\).