## 5 Discussion  
Considering that the multi-attribute weighted intelligent gray target decision-making evaluation model is being applied for the first time to optimize the site selection for emergency supply reserve warehouses and that a new evaluation indicator system has been constructed, the reliability of the model's evaluation results is verified using both EEM-TOPSIS and Gray Correlation Degree-TOPSIS methods. The study results indicate that evaluation results obtained through the multi-attribute weighted intelligent gray target decision-making evaluation model are largely consistent with those derived from the two commonly used evaluation methods (Table 7). The multi-attribute weighted intelligent gray target decision-making evaluation model identifies the High-tech Zone as the optimal decision, which is consistent with the results obtained using the EEM-TOPSIS method. Among the 96 district- and county-level administrative divisions, the four that missed the target are Shaya County, Yuli County, Aksu City, and Hotan County. Their respective EEM-TOPSIS comprehensive evaluation rankings are 95 th (Shaya County), 94 th (Yuli County), 88 th (Aksu City), and 96 th (Hotan County). Similarly, their Gray Correlation Degree-TOPSIS comprehensive evaluation rankings are 93 rd (Shaya County), 94 th (Yuli County), 89 th (Aksu City), and 96 th (Hotan County).  
Through comparison, it is found that the multi-attribute weighted intelligent gray target decision-making evaluation model not only ranks the target schemes but also effectively identifies the superiority or inferiority of schemes by distinguishing between  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Critical Value for** & **Density of Road** & **Population** & **Economic** & **Post-Disaster** & **Optimal Site Based on** & **Missing-the-Target and \\
**Objective** & **Networks**\({}^{\text{rd}}_{\text{F}}\) & **Density**\({}^{\text{rd}}_{\text{F}}\) & **Density**\({}^{\text{rd}}_{\text{F}}\) & **Response Capability**\({}^{\text{rd}}_{\text{F}}\) & **Evaluation Result** & **Ranking Situation** \\ \hline \multirow{2}{*}{\begin{tabular}{c} After a 5\% \\ reduction \\ Unchanged \\ \end{tabular} } & \multirow{2}{*}{0.0138} & \multirow{2}{*}{3.0410} & \multirow{2}{*}{0.0015} & \multirow{2}{*}{3.8961} & \multirow{2}{*}{\begin{tabular}{c} High-tech Zone \\ (Cinshi District) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Aksu City = Hotan County \\ (Cinshi District) \\ \end{tabular} } \\  & & & & & & \\ \multirow{2}{*}{\
\begin{tabular}{c} High-tech Zone \\ (Cinshi District) \\ \end{tabular} } & \multirow{2}{*}{Shaya County \textgreater{} Julian County \textgreater{} Julian County \textgreater{} \\  & & & & & & \\ \multirow{2}{*}{
\begin{tabular}{c} Black Kazakh Autonomous \\ County \textgreater{} Long County \\ \textgreater{} Asian County \\ \textgreater{} Asian County \\ \textgreater{} Asian City \\ \end{tabular} } \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effects of changes in critical values for objectives on optimal evaluation and missing-the-target situations.  
hitting-the-target and missing-the-target scenarios. The computation process is simple, making it superior compared to other methods.  
The analysis of the study results reveals an intriguing phenomenon. In Shaya County, despite all other indicators being above the critical value, the density of road networks falls below the critical value for the objective, resulting in the target being missed. Similarly, in Aksu City, although other indicators surpass the critical values, the post-disaster response capability is below the critical value for the objective, leading to the target being missed (Table 8). In this study, the density of road networks and post-disaster response capability significantly influence the comprehensive evaluation results. However, whether they can be considered key indicators affecting the evaluation of emergency supply reserve warehouses remains to be validated by further studies from other scholars.