# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 4 Discussion  
In contrast to prior investigations, this study conducted long-term EEQ (Ecological and Environmental Quality) monitoring in the Chinese region using remote sensing data. This approach effectively addresses the limitations inherent in assessments based solely on statistical data [57]. For instance, [PERSON] et al. [58] and [PERSON] et al. [59] employed panel data to evaluate the ecological and environmental quality of China. However, the subjectivity of the statistics and the utilization of data from multiple sources led to controversial findings. By adopting this approach, the inherent constraints associated with assessments relying solely on statistical data are effectively addressed. Furthermore, by utilizing scenario data generated through Earth system models and employing the grid-scale approach proposed in this study, it becomes feasible to forecast future ecosystem quality in China and investigate its evolving attributes. We integrated the Shared Socioeconomic Pathway (SSP) [60] and Representative Concentration Pathway (RCP) [61] frameworks to establish three widely employed scenarios. These scenarios were employed to examine the spatial and temporal variations of Ecosystem Environmental Quality (EEQ) in China throughout the 21 st century. The analysis was based on comprehensive remote sensing data from multiple sources and data from the Coupled Model Intercomparison Project 6 (CMIP6) model [62].  
Figure 15 illustrates the consistent spatial pattern between the simulated future EEQ for China and historical EEQ, indicating the robust generalizability of the EEQ simulations conducted in this study. In Figure 15e,f, it can be observed that the average future EEQ for China exhibits a slight upward trend across the three scenarios, but the change is not pronounced. It is noteworthy that under the SSP5-RCP85 scenario, China's EEQ demonstrates a stable increasing trend (Figure 15f), implying that the country's EEQ would remain relatively unaffected by traditional fossil fuel-based pathways. This finding suggests that future human activities would have minimal impact on national EEQ.  
However, it is important to note certain considerations within this study. For instance, due to limited ground validation data, it is not possible to provide an accuracy assessment for the entire study area. Furthermore, discrepancies among multiple sources of spatial data introduce some level of uncertainty into the EEQ assessment results, an aspect that was not extensively explored in this study. Nevertheless, this study offers a fresh perspective on global grid-based EEQ monitoring. Moving forward, our research endeavors will focus on investigating EEQ dynamics in rapidly urbanizing countries or regions worldwide, with particular attention to nations such as India and the USA.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**City** & **Year** & **M-RSEQI** & **ISA (km2)** & **Speed\_ISA (km2/a)** \\ \hline \multirow{4}{*}{Beijing} & 2000 & 0.183 & 1961.68 & \multirow{4}{*}{149.10} \\  & 2008 & 0.193 & 3188.98 & \\  & 2017 & 0.193 & 4645.54 & \\  & 2000 & 0.193 & 1324.74 & \\ \multirow{4}{*}{Shanghai} & 2008 & 0.181 & 2241.25 & 132.55 \\  & 2017 & 0.187 & 3710.68 & \\ \multirow{4}{*}{Guangzhou} & 2000 & 0.227 & 841.00 & \multirow{4}{*}{61.15} \\  & 2008 & 0.208 & 1330.20 & \\ \multirow{4}{*}{Nanjing} & 2017 & 0.223 & 1941.66 & \\  & 2000 & 0.184 & 750.25 & \\ \multirow{4}{*}{Nanjing} & 2008 & 0.183 & 1094.85 & 61.09 \\  & 2017 & 0.190 & 1849.78 & \\ \hline \end{tabular}
\end{table}
Table 7: Statistics of M-RSEQI, ISA and Speed_ISA in Beijing, Shanghai, Guangzhou, and Nanjing in 2000, 2008, and 2017.