## 3 Results
### Spatial and Temporal Variation Drivers of Carbon Storage  
The factor detection results of the Geodetector (Figure 6) showed that altitude, slope, NDVI, temperature, and other factors have different degrees of influence on the spatial distribution of carbon storage in ecosystems of the Taihang Mountains. Figure 6 showed that slope and NDVI were the main driving factors affecting the spatial differentiation of carbon storage in ecosystems. Their explanatory power was significantly greater than that of other factors. This indicated that during this period, the change in the geographical \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**2005–2009**} & \multicolumn{2}{c}{**2010–2014**} & \multicolumn{2}{c}{**2015–2019**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & **Storage** & **Pct** & **Storage** & **Pct** & **Storage** & **Pct** & **Storage** \\ \hline farmland & \(-2.19\) & \(-0.13\) & \(-2.26\) & \(-0.14\) & \(-26.41\) & \(-1.71\) & \(-30.87\) \\ grassland & \(0.64\) & \(0.06\) & \(-0.34\) & \(-0.01\) & \(7.98\) & \(0.65\) & \(8.27\) \\ forest & \(-1.94\) & \(-0.12\) & \(-0.39\) & \(-0.02\) & \(-11.90\) & \(-0.75\) & \(-14.24\) \\ water & \(-0.45\) & \(-0.03\) & \(0.08\) & \(0.01\) & \(-1.89\) & \(-0.12\) & \(-2.26\) \\ construction land & \(3.27\) & \(-0.13\) & \(2.29\) & \(-0.14\) & \(28.29\) & \(-1.71\) & \(33.84\) \\ unused land & \(0.00\) & \(0.06\) & \(0.00\) & \(-0.01\) & \(0.00\) & \(0.65\) & \(0.00\) \\ Total & \(-0.68\) & \(-0.29\) & \(-0.63\) & \(-0.31\) & \(-3.95\) & \(-2.99\) & \(-5.26\) \\ \hline \hline \end{tabular}  
Storage Area: Carbon storage by land use type. Pct: Percentage of carbon storage by land use type.  
\end{table} 
Table 4: Changes in carbon storage of various land use types in the Taihang Mountains (\(\times 10^{6}\) t). Figure 5: Changes in the spatial distribution of carbon storage in the Taihang Mountains in different periods: (**a**) 2005–2010; (**b**) 2010–2014; (**c**) 2015–2019. environment and the dense degree of vegetation dominated the change in carbon storage in the region to a certain extent. The influence of other driving factors on the spatial variation of carbon storage in the Taihang Mountains was relatively stable. Except for the driving factor of sand content, the q values of all factors were greater than 0.05, and the explanatory power was more than 5%, indicating that these factors also played a certain role in the spatial differentiation of carbon storage. The factor of sand content had little explanatory power for the spatial variation of carbon storage in the region. The q value was less than 0.05, and the explanatory power was less than 5%, indicating that it had little influence on the spatial distribution of carbon storage in the Taihang Mountains. Table 5 showed the detection results of the pairwise interactions of seven factors for the spatial differentiation of ecosystem carbon storage. The pairwise interaction relationship of these factors was nonlinear enhanced or bivariate enhanced, indicating that any factor combined with other factors can enhance the influence on the spatial differentiation of carbon storage. In each period, the explanatory power of slope combined with NDVI type ranged from 0.2610 to 0.2950. This type had the most explanatory power among the synergistic types of the spatial differentiation of carbon storage in each ecosystem, indicating that the regions with good slope and NDVI values may have more carbon storage. Most of the synergistic types between NDVI and other factors were nonlinear enhancement types, which further proves that NDVI was the main driving factor affecting the spatial differentiation of carbon storage. There was a nonlinear enhancement type between sand content factor and other factors in all years, indicating that the effect of sand content factor combined with other factors was far better than that of the effect alone.