## 5 Results
### Result of The Spatial Durbin Model for Hypothesis 1
#### 5.2.3 The Empirical Analysis  
This section examines the impact of transportation integration in urban agglomerations on industrial agglomeration. Based on statistical data at the municipal level, 41 cities in urban agglomerations in the Yangtze River Delta were selected as research samples, and the Spatial Durbin Model (2) was used to conduct regression analysis from 2011 to 2019 and 2011 to 2020, respectively, considering the impact of COVID-19. The model estimation results are shown in the table below. As Table 6 suggests, \(\lambda\) had ideal statistics, indicating that SDM can accurately reflect the spatial correlation between the transportation integration of urban agglomerations and industrial agglomeration. According to SDM's spatial regression coefficient, \(\lambda\), apart from Model 2 for the 2011-2020 period, all other model estimations passed the significance test, implying the crucial role of industrial agglomeration in other cities on the local industrial agglomeration. When the spatial effect was considered, the improved industrial agglomeration in neighboring areas had a negative spillover effect on the local industrial agglomeration, suggesting strong spillovers from relatively developed regions, either geospatially or industrially. It can also be observed by comparing the model estimations over the two time periods that the statistics for 2011-2020 are higher than those for 2011-2019, indicating the reduced inhibition by industrial agglomeration in other regions on the local industrial agglomeration due to COVID-19. The Ti estimations were significantly positive for all the models, hinting at the crucial constructive contribution toward the level of local industrial agglomeration by the local transportation integration. The comparison showed that Ti estimations for 2011-2020 were smaller than those for 2011-2019, indicating the diminishing positive impact of local transportation integration on local industrial agglomeration due to COVID-19. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **2011–2019** & **2011–2020** \\ \hline \(\lambda\) & \(-\)0.0504 *** & \(-\)0.0311 * \\ Ti & 0.3527 *** & 2.7496 \(\times\) 10\({}^{-1}\) *** \\ Fin & \(-\)0.3038 *** & \(-\)3.5618 \(\times\) 10\({}^{-1}\) *** \\ Fdi & 0.0303 * & 2.3403 \(\times\) 10\({}^{-1}\) *** \\ Ur & 0.3073 *** & 3.9739 \(\times\) 10\({}^{-1}\) *** \\ EX & \(-\)0.0173 & \(-\)2.8875 \(\times\) 10\({}^{-2}\) * \\ W*Ti & \(-\)0.0379 & 6.4033 \(\times\) 10\({}^{-3}\) \\ W*Fin & 0.0353 *** & 4.9990 \(\times\) 10\({}^{-2}\) *** \\ W*Fdi & \(-\)0.0233 *** & \(-\)6.4925 \(\times\) 10\({}^{-3}\) *** \\ W*Ur & \(-\)0.0109 & \(-\)3.1770 \(\times\) 10\({}^{-2}\) \\ W*EX & 0.0043 & 6.6491 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular} Note: the significance of **and *** are 10% and 1%, respectively.  
\end{table} 
Table 6: The Spatial Durbin Model (2) regression estimation results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Parameters** & **Statistics (2011–2019)** & **Statistics (2011–2020)** \\ \hline LM-ERR & 0.49 & 154.38 *** \\ LM-LAG & 35.17 *** & 22.19 *** \\ RLM-ERR & 0.63 & 134.55 *** \\ RLM-LAG & 35.31 *** & 2.36 \\ \hline \hline \end{tabular} Note: the significance of *** is 1%.  
\end{table} 
Table 5: Test of spatial autocorrelation of industrial location entropy. The estimations for W*Ti varied across models. In 2011-2020, the W*Ti estimations for both Models 1 and 2 were significantly positive but failed the significance test. In 2011-2019, the W*Ti estimations also failed the significance test. This indicates that at present, transportation integration in other regions positively affects industrial agglomeration in the Yangtze River Delta, albeit insignificantly. The comparison between the W*Ti estimations showed that the high level of transportation integration in other regions mitigated the effect of COVID-19 to a certain extent and gave a greater boost to the local industrial agglomeration.