## 4 Results
### Influencing Factors of the Change in Life Expectancy  
A global OLS regression analysis was conducted first, and the results are shown in Table 4. Overall, the changes in average years of education, aging rate, and birth rate, which reflect the demographic structure, have clear impacts on the change in life expectancy. The positive coefficient of the change in average years of education indicates the association between education improvement and life expectancy extension ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). The standardized regression coefficient of the change in aging rate is positive, indicating that in areas where the aging level has increased, life expectancy has also significantly improved. An increase in the aging rate signifies achievements in family planning policy, changes in beliefs, and progress in economic development, which is beneficial to the improvement of health conditions. As to the birth rate, generally speaking, a lower birth rate signifies that the level of economic development and educational level have improved, which leads to the improvement of health level in the region. Regarding the natural environment, positive coefficients of the logarithm of average altitude and temperature indicate that regions with higher altitude and temperature increase more in life expectancy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Nationwide** & \begin{tabular}{c} **Low-Income** \\ **Cities** \\ \end{tabular} & \
\begin{tabular}{c} **Middle-Income** \\ **Cities** \\ \end{tabular} &
\begin{tabular}{c} **High-Income** \\ **Cities** \\ \end{tabular} \\ \hline \(\alpha\) & \(0.188\)*** & \(0.180\)*** & \(0.289\)*** & \(0.174\)*** \\  & \((19.467)\) & \((9.636)\) & \((13.847)\) & \((8.487)\) \\ \(\beta\) & \(-0.723\)*** & \(-0.667\)*** & \(-0.801\)*** & \(-0.616\)*** \\  & \((-18.854)\) & \((-9.257)\) & \((-13.570)\) & \((-8.245)\) \\ Adj-R\({}^{2}\) & \(0.521\) & \(0.440\) & \(0.638\) & \(0.374\) \\ F & \(355.455\)*** & \(85.700\)*** & \(184.146\)*** & \(67.987\)*** \\ \hline \hline \end{tabular} Notes: T statistic of the regression coefficient listed in parentheses. *** \(p\)\(<\) 0.01, ** \(p\)\(<\) 0.05, * \(p\)\(<\) 0.1.  
\end{table} 
Table 3: Absolute \(\beta\) convergence test of life expectancy. In order to identify different effects of factors for regions at different economic development level, we divided all cities into three groups according to their per capita GDP and conducted OLS regression separately. Results show obvious heterogeneity in the effects of these factors. In low-income cities, it is more affected by demographic structure and natural conditions. These cities are economically depressed, with a generally high birth rate and a low level of aging. The promotion of family planning and the improvement of demographic structure are highly significant to the improvement of health conditions. For the middle-income cities, the improvement of educational level and the logarithm of average altitude have significant effects on the increase in life expectancy. For the high-income cities, the improvement in regional economic development level and educational level indeed extends the life expectancy significantly, followed by the change in aging rate. These results further illustrate the necessity of GWR model for micro-scale analysis. Results of GWR model are shown in Table 5. The increase of adjusted R-square of model from 0.243 (OLS) to 0.285 indicates improved explanation by employing GWR model. Effects of variables are spatially heterogeneously distributed. Residuals of the GWR model are distributed randomly at 0.05 significance level, with only four cities failing to pass the residual test. The Moran's I (0.042) indicates almost no spatial autocorrelation of the GWR results. Heterogenous effects of each variable are elaborated in the following parts, after which is a brief discussion of underlying mechanisms. Among them, the impact of economic development and gender ratio on