## 4 Results
### Influencing Factors for UBLGUE
#### 4.4.3 Estimation Results of the Factors Influencing UBLGUE  
The panel data regression model includes the mixed model (MM), the fixed effect model (FEM), and the random effect model (REM) [59]. Theoretically, the F test and Hausman test are required to determine which regression model results are the most appropriate in the three models. The former is employed to determine which is the suitable model in both MM and the FEM, whereas the latter is adopted to determine which is the appropriate model in both the FEM and the REM. This study runs the regression model via the Stata 18 software. The F test statistic score is 2.34 within the significant level of 1%, which shows that the null hypothesis of the MM can be rejected, and the FEM can be further accepted. In addition, the Hausman test value and p value are 15.404 and 0.031, which reveals that we can reject the null hypothesis of the REM and further suggests that the FEM is appropriate for the three models. Furthermore, the R2 value of the FEM is 0.502; exceeding the REM illustrates that the fit degree of the FEM is better than that of the REM. Therefore, the FEM should be constructed as an appropriate model on the basis of the abovementioned analysis. The FEM regression results of the seven independent variables, displayed in Table 5, are further adopted to analyze the socioeconomic factors influencing UBLGUE. The estimation results illustrate that the economic growth (EG), industrial structure (IS), environmental regulation intensity (ERI), and energy consumption intensity (ECI) pass the 10% significance level. Specifically, the economic growth (EG) presents positive correlations during the observation period at the 10% level. Conversely, industrial structure (IS), environmental regulation intensity (ERI), and energy consumption intensity (ECI) are negatively correlated with UBLGUE at the 10% level. Regarding economic growth, the results of the panel estimation presented indicate that the estimation coefficients of economic growth are significantly positive within the 1% level and the economic growth positively impacts UBLGUE. The results show that, for every 1% increase in the economic development level, the UBLGUE will increase by 13.1%. The regression coefficient of the industrial structure of UBLGUE is \(-\)0.144 within the 10% level of significance, showing that there exists a negative influence on UBLGUE. This study finds that, for every 1% decrease in the industrial structure, the UBLGUE changes by 14.4%. For environmental regulation intensity, the results indicate that the estimation coefficients \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Variable** & **EG** & **_TP_ & **_IS_** & _FDI_ & _ERI_ & _ECI_ & _UC_ \\ \hline LLC & \(-9.71\)*** & \(-13.99\)*** & \(-5.94\)*** & \(-18.07\)*** & \(-13.99\)*** & \(-18.60\)*** & \(-14.62\)*** \\ IPS & \(-9.64\)*** & \(-12.33\)*** & \(-5.69\)*** & \(-16.27\)*** & \(-10.41\)*** & \(-13.29\)*** & \(-13.24\)*** \\ ADF-Fisher & 204.00*** & 253.54*** & 127.94*** & 329.15*** & 209.57*** & 268.94*** & 271.86*** \\ PP & 234.02*** & 306.69*** & 136.77*** & 323.89*** & 204.94*** & 280.24*** & 302.48*** \\ \hline \end{tabular} Note: *** indicates significance at the 1% level.  
\end{table} 
Table 4: Stability test results for the panel data.