## 3 Results  
### The Variations in Eeq  
Table 3 lists the contribution rates, eigenvalues, and percentages of each principal component (_PC1_-_PC4_) from 1995 to 2019. As depicted in Table 3, the contribution rate of the first principal component (_PC1_) was notably high, at 75.5732%, 76.4586%, 70.7658%, and 68.6252% for the periods of 1995, 2000, 2010, and 2019, respectively. This indicates that _PC1_, with the majority of the characteristics of the evaluation indicators, could be suitable for calculating the _RSEI_ value of the study area. Within _PC1_, the coefficients of _NDVI_ and _WET_ were positive, while those of _LST_ and _NDBSI_ were negative, indicating that _NDVI_ and _WET_ had a positive correlation with the EEG, whereas _LST_ and _NDBSI_ exerted a negative influence on the EEG. Based on the absolute values of the index coefficients in _PC1_, _NDVI_ had the greatest effect on the EEG in 1995, 2010, and 2019, which was consistent with the high vegetation coverage of the study area. In contrast, in 2000, _LST_ was the most prominent factor affecting the EEG. This is mainly because the study area experienced rapid industrialization and urbanization after 1995, which had a significant effect on the eco-environment by intensifying the urban heat-island effect. Among the four indicators, _WET_ had the least influence on the EEG. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Formula** & **Meaning** \\ \hline Global Moran’s I & \(I_{C}=\frac{\frac{\mu_{0}^{2}-\sum_{i=0}^{n}\mu_{i}}{\sum_{i=1}^{n}\mu_{i}}(x -\overline{x})(y_{i}-\overline{x})}{\sum_{i=1}^{n}\sum_{i=1}^{n}\mu_{i}\mu_{i}^{ 2}=(x_{i}-\overline{x})}\) & \(n\) is the total number of elements; \(x_{i}\) and \(x_{j}\) are the property values of elements \(i\) and \(j\); \(\overline{X}\) is the average value; \(\omega_{i,j}\) is the spatial weight between elements \(i\) and \(j\); \(n\) is the total number of elements; \(x_{i}\) and \(y_{i}\) are the coordinates of element \(i\); \(\overline{x}\) and \(\overline{y}\) are the mean center point of all the elements. \(n\) is the total number of elements; \(k\) is the kernel function; \(f(x,y)=\frac{1}{n!}\sum_{i=0}^{n}k\left(\frac{d_{i}}{\overline{n}}\right)\) & \(n\) is the total number of elements; \(k\) is the kernel function; \(d_{i}\) is the distance between element \(i\) and point \((x,y)\); \(k\) is the smoothing bandwidth. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis methodology. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Indictors** & **PC1** & **PC2** & **PC3** & **PC4** \\ \hline \multirow{6}{*}{2019} & _NDVI_ & 0.64257 & 0.42440 & 0.61273 & 0.17764 \\  & _WET_ & 0.37449 & 0.04581 & \(-\)0.62275 & 0.68549 \\  & _LST_ & \(-\)0.43786 & 0.88361 & \(-\)0.16250 & 0.03334 \\  & _NDBSI_ & \(-\)0.50512 & \(-\)0.19257 & 0.45862 & 0.70529 \\  & Eigenvalue & 0.00706 & 0.00223 & 0.00091 & 0.00009 \\  & Percent eigenvalue & 68.6252 & 21.6641 & 8.8444 & 0.8663 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of PCA. Table 4 illustrates the \(RSEI\) average values and the area and percentage of each EEQ grade for the study area from 1995 to 2019. The data revealed that the \(RSEI\) average value during the study period was 0.628, indicating that the EEQ of the study area was generally in good condition. Initially, in 1995, the average value of \(RSEI\) was the highest (0.683); the value declined to 0.544 by 2000 and then rebounded to 0.672 by 2019. The data also showed a fluctuating trend in the EEQ, with an initial decline followed by an improvement from 1995 to 2000. The EEQ in most of the study area was categorized into the moderate and good grades, accounting for 98.4%, 97.45%, 84.26%, and 96.5% of the total area, respectively, in 2019, 2010, 2000, and 1995. These areas were predominantly distributed in mountainous regions, woodlands, and grasslands surrounding the city, exhibiting a certain degree of spatial continuity.