## 3 Results
### Analysis of ESV Drivers
#### 3.4.2 Gradient Characteristics of the Effect of Each Driver on the ESV in the Study Area  
The combination of fitting factors selected for the study included 17 driving factors. Since the regression coefficients for some of the factors are too small, the regression coefficients for all the factors were multiplied by 100 to increase the sensitivity of the parameter and to better reflect the differences between the situations. Among these, the four driving factors, GDP, population density, nighttime lighting, and evapotranspiration, were fitted to the ESV in the study area with an effect of less than 0.5, which is a very weak influence. Meanwhile, the lowest regression coefficient of the elevation factor was \(-0.14\), the highest regression coefficient was 0.51, and the spatial difference of factor influence was not obvious. The lowest regression coefficient of the factor of electricity energy consumption of industrial enterprises that was above scale for the whole year was \(-6.34\), and the highest regression coefficient was 0. The spatial difference of the factor influence was not obvious. In order to ensure the scientific nature of the analysis, a regression coefficient of \(\pm 1.00\) was selected, and the spatial difference for the impact of the driving factor for gradient analysis, in which the temperature, humidity, industrial exhaust emissions, and industrial wastewater emissions factor had a greater relative impact, is shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Driving Factor** & **Average** & **Standard** & **Maximum** & **Minimum** \\  & **Value** & **Deviation** & **Value** & **Value** \\ \hline  & Temperature/\({}^{\circ}\)C & 23.14 & 0.77 & 24.10 & 18.90 \\  & Humidity/\%rh & 78.62 & 1.27 & 82.80 & 75.70 \\ Natural envi- & NDVI & 0.71 & 0.19 & 1.00 & 0.00 \\  & Elevation/m & 213.59 & 218.56 & 973.00 & 0.00 \\ factors & Slope/\% & 12.34 & 6.01 & 66.45 & 0.00 \\  & Annual rainfall/mm & 1737.83 & 86.36 & 2004.25 & 1564.5 \\  & Annual evapotranspiration/kg\(\cdot\)m\({}^{-2}\) & 1082.19 & 145.78 & 1411.75 & 544.25 \\ \hline  & GDP per unit area/CNY\(\cdot\)km\({}^{-2}\) & 8645.12 & 5954.76 & 153,465.00 & 501.00 \\  & Population density/population\(\cdot\)km\({}^{-2}\) & 1115.68 & 1306.44 & 122,797.00 & 1.50 \\  & Proportion of primary industry/\% & 14.41 & 11.49 & 37.9 & 0.03 \\ Socio- & Proportion of secondary industry/\% & 43.61 & 11.88 & 75.60 & 22.70 \\ economic & Proportion of tertiary industry/\% & 40.78 & 11.24 & 70.80 & 21.54 \\ factors & Total electricity consumption/\({}^{10}\) kW\(\cdot\)h & 271.16 & 193.74 & 710.30 & 78.56 \\  & Industrial emissions/\({}^{10}\) t & 2481.89 & 753.73 & 3691.97 & 1057.7 \\  & Discharge of industrial wastewater/\({}^{10}\)\({}^{8}\)\(\cdot\)t & 6185.73 & 3354.09 & 11,455.08 & 1581.97 \\  & Night light/nWcm\({}^{-2}\)sr\({}^{-1}\) & 24.86 & 18.41 & 59.52 & 0.01 \\  & Annual power energy consumption of industrial & & & & \\  & enterprises above designated size/\({}^{10}\) kW\(\cdot\)h & 303,192.90 & 329,690.49 & 1,079,429 & 29,301 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical analysis of the driving factors.