## 4 Discussion
### Key Drivers of Land NPP Change  
Climate variability and human activity are commonly regarded as the two main components influencing land NPP change. OLS regression was firstly applied to identify potential explanatory variables. The dependent variable was the increase in land NPP in the Han River Basin from 2001 to 2019, and the independent variables were seven influencing factors: temperature, precipitation, elevation, slope, forest cover, population count, and human modification. OLS regression results revealed that none of the seven explanatory factors had a VIF value above 10, indicating that these independent variables had only minimal covariances. In addition, with the exception of temperature, slope, and forest cover, the other four independent variables passed the significance test at the 5% level. As a result, these four variables were included in the OLS regression. The presence of spatial correlation between the variables is a requirement for using the GWR model. [PERSON]'s \(I\) values were used to pre-test the spatial correlation of the associated variables. We examined the spatial autocorrelation of the standardized residuals for all grids and found that the standard deviations predicted by the GWR model were spatially randomly distributed, with a Moran's \(I\) value of 0.009 and a z-score of 0.956. Therefore, the overall simulation effect of the model is favorable. It is reasonable and feasible to apply the GWR model to evaluate the factors influencing land NPP change in the Han River Basin. Table 3 compares the key metrics associated with fit statistics for the global model (OLS), GWR, and MGWR. The MGWR model had a smaller AIC, a slightly larger adjusted \(R^{2}\), and richer bandwidths than OLS and GWR. Therefore, MGWR outperformed the other two models in fitting the drivers of land NPP change in the Han River Basin. The variable bandwidth measures the spatial scale of each process. It captures the difference in the scale of action of natural and socio-economic influences on land NPP. The larger the scale of action, the lower the spatial heterogeneity of influencing factors. On the contrary, the more pronounced the spatial heterogeneity. Based on the results of the MGWR  
Figure 8: Comparison of land productivity assessment results, Google Earth images, and data sources between the Trends.Earth method and the sustainability pattern method in the test region. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Metrics** & **OLS** & **GWR** & **MGWR** \\ \hline AIC & 14,234 & 12,440 & 12,355 \\ Adjusted \(R^{2}\) & 0.318 & 0.562 & 0.565 \\ Bandwidth & 150 & 150 & Intercept (578), Precipitation (51), Elevation (5794), Population Count (485), Human Modification (645) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fit statistics for OLS, GWR, and MGWR models. model, among the natural factors, the scales of action of precipitation and elevation were 51 and 5794 (Table 3), respectively. The effect of precipitation on land NPP change indicated a high degree of spatial heterogeneity. The scale of action of elevation was proportional to the sample size. It was a global variable with low spatial heterogeneity, and the effect of elevation on land NPP change was broadly consistent throughout the basin. There was significant spatial heterogeneity in the effect of human factors on land NPP change, with a scale of 485 for population count and 645 for human modification (Table 3). The violin plot of regression coefficients of the influencing factors of the MGWR model (Figure 9) shows both positive and negative effects on land NPP change. The increase in population count had an overall negative effect on land NPP (19.42% positive, 80.58% negative), and the negative effect was substantial. In contrast, the increase in elevation had an overall positive effect on land NPP (59.15% positive, 40.85% negative), though the positive effect was not strong, whereas the polarization was pronounced.