## 4 Discussion
### Drivers of the Changes of Human Activity Index in Eastern Inner Mongolia  
As calculated in Section 3.4, the HAI consists of two parts, i.e., \(\rm{NPPLuc}\) and HNPP. The average total \(\rm{NPPLuc}\) and HNPP of the grasslands was 64.55 Tg C yr\({}^{-1}\) and 8.85 Tg C yr\({}^{-1}\). Thus, the changes of the HAI was dominated mainly by \(\rm{NPPLuc}\) in eastern Inner Mongolia.  
The \(\rm{NPPLuc}\) is the difference between the PNPP and ANPP, and denotes the impact of human-induced land conversions on terrestrial NPP [21; 22]. Land cover and land use changes induced by human activities, such as deforestation [52], and desertification due to overgrazing on grasslands [9; 16], were considered to be the main drivers of changes in the \(\rm{NPPLuc}\)[21; 53]. Moreover, human managements, such as ecosystem protection and irrigation, can effectively improve terrestrial ANPP. For example, irrigation in cultivated land in arid areas can reduce the water stress on crops, and significantly increase the crop yield. In this case, the irrigation land can have a higher ANPP than PNPP [21]. \(\rm{NPPLuc}\) changes driven by these factors can be regarded as a direct impact of human activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Period** & **Objects** & **HAI (\%)** & **References** \\ \hline Global & 1910–2005 & All land covers & 13–25 (\(\uparrow\)) & [23] \\ Hungary & 1961–2005 & All land covers & 67–49 (\(\downarrow\)) & [43] \\ Spain & 1955–2003 & All land covers & 67–61 (\(\downarrow\)) & [45] \\ China & 2001–2010 & All land covers & 49–58 (\(\uparrow\)) & [46] \\ The Yangtze River Delta & 2005–2015 & All land covers & 59–72 (\(\uparrow\)) & [47] \\ Coastal Areas of Jiangsu & 2000–2010 & Cropland dominance & 50–71 (\(\uparrow\)) & [44] \\ Guangzhou \({}^{\star}\) & 2001–2013 & All land covers & 31–80 (\(\uparrow\)) & [48] \\ Northeast China \({}^{\star}\) & 2000–2011 & Marshes & 6–14 (\(\uparrow\)) & [49] \\ Eastern Inner Mongolia & 2000–2017 & Grasslands & 75–47 (\(\downarrow\)) & this study \\ \hline \hline \end{tabular} Note. \({}^{\star}\)HNPP was not considered in the study.  
\end{table}
Table 2: Comparison of the HAI estimated in this study with previous studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **Types** & **Period** & **GPP\({}_{\text{obs}}\)** & **ANPP/PNPP** & **RPE\%** \\ \hline NM & Grasslands & 2004–2008 & 231.66 \(\pm\) 111.13 & 136.39 & 17.76\% \\ XLD & Grasslands & 2006 & 294.44 & 159.20 & 8.13\% \\ HZ \({}^{\star}\) & Forests & 2010 & 962.75 & 504.29 & 4.76\% \\ \hline \hline \end{tabular} Note. RPE means Relative Predictive Error and is calculated as (\(\rm{2}^{\star}\)ANPP \(-\)GPP\({}_{\text{obs}}\))/GPP\({}_{\text{obs}}\)\(\times\) 100%; \({}^{\star}\) value in HZ is PNPP and values in NM, XLD is ANPP.  
\end{table}
Table 3: Comparison of ANPP/PNPP with observations.  
Some factors indirectly influenced by human activities can also drive the changes of \(\text{NPP}_{\text{Luc}}\). For example, the continuously increasing \(\text{CO}_{2}\) concentration and global warming, resulting from human carbon emission, were found to be of significant impact on terrestrial ANPP [54]. However, these changes, induced by indirect factors, sometimes combined the effects of climate change and human activities and are hard to distinguish, although some studies have tried to separate them by the slope trends of the ANPP and PNPP [16; 17].