## 4 Results
### Statistical Analysis and Model Assessment
#### 4.2.1 FVC and Major Driving Factors: OLS, GWR, and SGWR
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **2000** & **2005** & **2010** & **2015** \\ \hline Intercept & 0.470 * & 0.495 * & 0.404 * & 0.389 * \\ Elevation & 0.038 & 0.049 & 0.046 * & 0.050 * \\ Sunshine duration & 0.117 * & 0.114 * & 0.102 * & 0.099 * \\ Rainfall & 0.033 * & 0.027* & 0.031 * & 0.030 * \\ Urban land & \(-\)0.104 * & \(-\)0.112 * & \(-\)0.128 * & \(-\)0.138 * \\ Population & \(-\)0.050 * & \(-\)0.058 * & \(-\)0.069 * & \(-\)0.067 * \\ GDP per capita & \(-\)0.056 * & \(-\)0.059 & \(-\)0.070 & \(-\)0.074 * \\ Gross industrial output value & \(-\)0.036 & \(-\)0.038 & \(-\)0.045 * & \(-\)0.052 * \\ \hline \hline
**R\({}^{2}\)** & 0.462 & 0.481 & 0.537 & 0.579 \\ \hline \hline \end{tabular}  
* \({}^{*}\)_p_-value < 0.05.  
\end{table} 
Table 2: Global estimates from ordinary least squares (OLS) model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**2000**} & \multicolumn{3}{c}{**2005**} & \multicolumn{3}{c}{**2010**} & \multicolumn{3}{c}{**2015**} \\ \cline{2-10}
**Variable** & \multicolumn{1}{c}{**DIFF of**} & \multicolumn{1}{c}{**Geographical**} & \multicolumn{1}{c}{**DIFF of**} & \multicolumn{1}{c}{**Geographical**} & \multicolumn{1}{c}{**Difference of**} & \multicolumn{1}{c}{**Geographical**} & \multicolumn{1}{c}{**Variability**} & \multicolumn{1}{c}{**DIFF of**} & \multicolumn{1}{c}{**Geographical**} \\  & **Criterion** & **Variability** & **Criterion** & **Variability** & **Criterion** & **Variability** & **Criterion** & **Variability** & **Criterion** & **Variability** \\ \hline Intercept & \(-1.275\) & \(\surd\) & \(-1.432\) & \(\surd\) & \(-1.598\) & \(\surd\) & \(-1.341\) & \(\surd\) \\ Elevation & \(-0.122\) & \(\surd\) & \(-0.180\) & \(\surd\) & \(-0.127\) & \(\surd\) & \(-0.141\) & \(\surd\) \\ Sunshine duration & \(-0.489\) & \(\surd\) & \(-0.624\) & \(\surd\) & \(\surd\) & \(0.331\) & & \(-0.295\) & \(\surd\) \\ Rainfall & \(0.096\) & & \(-0.151\) & \(\surd\) & \(-0.076\) & \(\surd\) & \(-0.172\) & \(\surd\) \\ Urban land & \(-0.393\) & \(\surd\) & \(-0.306\) & \(\surd\) & \(-0.438\) & \(\surd\) & \(-0.273\) & \(\surd\) \\ Population & \(-0.258\) & \(\surd\) & \(-0.204\) & \(\surd\) & \(-0.351\) & \(\surd\) & \(-0.105\) & \(\surd\) \\ GDP per capita & \(0.096\) & & \(0.051\) & & \(0.062\) & & \(0.167\) & \\ Gross industrial output value & \(0.107\) & & \(0.128\) & & \(0.094\) & & \(0.024\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Geographical variability of the variables  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**2000**} & \multicolumn{3}{c}{**2005**} & \multicolumn{3}{c}{**2010**} & \multicolumn{3}{c}{**2015**} \\ \cline{2-13}  & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** & **Min** & **Max** & **Median** \\ \hline Intercept & \(0.227\) & \(0.595\) & \(0.419\) & \(0.284\) & \(0.670\) & \(0.452\) & \(0.195\) & \(0.542\) & \(0.388\) & \(0.168\) & \(0.496\) & \(0.370\) \\ Elevation & \(-0.005\) & \(0.049\) & \(0.034\) & \(0.003\) & \(0.063\) & \(0.0