## 3 Results
### Regression Analysis of NPP and Driving Factors  
From the perspective of anthropogenic and natural factors, the factors with correlation were regressed with the NPP to construct the total NPP-anthropogenic factor regression model and the mean NPP-natural factor regression model. The independent variables of the total NPP-anthropogenic regression model are CO\({}_{2}\) emissions from fossil combustion, total population, non-agricultural population, fixed asset investment, usable grassland area and raw coal production, and the dependent variable is the total NPP value. The independent variables of the mean NPP-natural factor regression model are annual mean precipitation and annual mean temperature, respectively, and the dependent variable is the mean NPP value. As shown in Table 2, the total NPP-anthropogenic factor regression model was 0.955, indicating that the model may have a good fit, meaning that the independent variables have a strong explanatory power for the dependent variable. The model coefficients show that different independent variables have different degrees of influence on the dependent variable. The coefficients of CO\({}_{2}\) emissions from fossil combustion and usable grassland area are larger, indicating that they have a greater influence on the change in total NPP. The standard error of 11,423,367.71 indicates that the difference between the model prediction and the actual value of this model is large, which may be mainly due to the fact that raw coal production and usable grassland area are highly influenced by human fluctuations and are prone to outlier points. The mean NPP-natural factor regression model had a low R-value, indicating a mediocre fit. Combining the regression coefficients shows that annual mean temperature and annual mean precipitation have varying degrees of influence on mean NPP. The standard error of the model has a lower value compared to the total NPP-anthropogenic factor regression model, and the difference between the model predictions and the actual values is relatively small. This may be due to the fact that mean annual temperature and mean annual precipitation are less likely to produce outliers compared to anthropogenic factors. However, both models predict NPP values to some extent from different perspectives. Figures 6 and 7 show the regression models for the different factors. The total NPP-anthropogenic factor regression model produced better results than the mean NPP-natural factor regression model. \begin{table}
\begin{tabular}{c c c c c} \hline
**Driving Factors** & **Pearson Correlation Coefficient** & \(p\) & **Standard Error** & **Sig** \\ \hline Emissions of CO\({}_{2}\) from fossil fuel combustion & \(-0.618\) & \(p<0.05\) & 0.059 & 0.000 \\ Gross regional product & \(-0.197\) & \(p>0.05\) & 0.121 & 0.107 \\ Gross industrial product & \(0.093\) & \(p>0.05\) & 0.123 & 0.451 \\ Gross construction product & \(-0.186\) & \(p>0.05\) & 0.121 & 0.128 \\ Total population & \(-0.538\) & \(p<0.05\) & 0.095 & 0.000 \\ Agricultural population & \(-0.132\) & \(p>0.05\) & 0.112 & 0.244 \\ Non-agricultural population & \(-0.719\) & \(p<0.05\) & 0.079 & 0.000 \\ Fixed asset investment & \(-0.302\) & \(p<0.05\) & 0.121 & 0.015 \\ Total crop area sown & \(0.085\) & \(p>0.05\) & 0.113 & 0.453 \\ Total grain production & \(0.019\) & \(p>0.05\) & 0.113 & 0.870 \\ Total number of end-of-year livestock & \(0.069\) & \(p>0.05\) & 0.113 & 0.545 \\ Total output value of agriculture, forestry, & \(0.182\) & \(p>0.05\) & 0.111 & 0.107 \\ animal husbandry and fishery & \(0.163\) & \(p>0.05\) & 0.112 & 0.149 \\ Total power of agricultural machinery & \(0.731\) & \(p<0.05\) & 0.077 & 0.000 \\ Usable grassland area & \(-0.401\) & \(p<0.05\) & 0.124 & 0.002 \\ Raw coal production & \(0.546\) & \(p<0.05\) & 0.070 & 0.000 \\ Annual mean temperature & \(-0.158\) & \(p>0.05\) & 0.083 & 0.058 \\ \hline \end{tabular}
\end{table} 
Table 1: Correlation analysis of NPP factors in Ordos City.