## 4 Discussion
### Spatial Autocorrelation Analysis of Ecosystem Quality  
To guarantee the robustness and comprehensiveness of the analytical outcomes, this study integrated the unique attributes of the study area and resampled the imagery to a spatial resolution of 300 m \(\times\) 300 m. This study collected 129,075 sample points, which supported the spatial autocorrelation analysis of Jincheng City's RCEI utilizing Moran's I and the local indicators of spatial association. Figure 14 delineates the overall spatial autocorrelation of the RCEI within Jincheng City, with a preponderance of data points clustering in the first and third quadrants. This distribution indicated a positive spatial autocorrelation, predominantly characterized by high-high (H-H) and low-low (L-L) clustering patterns. The trajectory of [PERSON]'s I generally followed a pattern of initial fluctuation, subsequent consolidation, and eventual fluctuation, with notable perturbations in 2002 and 2008. In 2006, [PERSON]'s I peaked at 0.95796, reflecting a pronounced spatial agglomeration of the RCEI. Conversely, by 2022, the index had declined to its lowest point at 0.90062, suggesting a decline in the RCEI's spatial clustering and an emergent  
Figure 13: Correlation analysis between the RCEI and six ecological indicators from 1990 to 2022.  
fragmentation of ecological patches. Over the period from 1990 to 2022, [PERSON]'s I values (0.9349, 0.9459, 0.9574, 0.9579, 0.9202, 0.9249, 0.9168, and 0.9006) reinforced the inference that the spatial distribution of the ecological environmental quality in Jincheng City was clustered, not random. The evolution of [PERSON]'s I aligns with the trajectory of ecological environmental quality levels. In the present study, local indicators of spatial association clustering diagrams were utilized to delineate the spatial patterns of Jincheng City's RCEI from 1990 to 2022 (Figure 15). In 1990, low-low (L-L) clusters primarily occupied the central region of Jincheng City, including the adjacent coal mining areas of Fenghuangshan, Wangtai, and Gushuyuan; the urbanized sectors of Yangcheng and Qingshui Counties, along with the mining zones of Sihe and Duanshi; and the developed areas and mining clusters of Gaoping City. By 2002, the expanse of the L-L clusters had significantly diminished, primarily retreating to  
Figure 14: Scatterplot of the RCEI Moran’s I in Jincheng from 1990 to 2022.  
the eastern precincts of Jincheng City. Concurrently, the L-L clusters in Qingshui County expanded in an east-west trajectory toward Gaoping County, with the L-L clusters near the Sihe coal mine in southern Yangcheng County transitioning into high-high (H-H) clusters. Figure 15 illustrates that by 2006, the L-L clusters had intensified in concentration and spatial extent, surpassing those observed in 1990, 1996, and 2002. However, by 2008, the domain of the L-L clusters commenced a discernible contraction, centralizing around the urban core of Jincheng City and the Gaoping District, while the H-H clusters witnessed a progressive annual expansion. The enactment of pivotal governmental policies and regulations, such as the revised Environmental Protection Law (2014) and the Overall Program for Reform of the Ecological Civilization System (2015), catalyzed the momentum toward sustainable development, reinforcing environmental stewardship and ecological conservation. A comparative analysis of the LISA clustering diagrams and the RCEI change detection maps from 1990 to 2022 revealed a general alignment between the H-H clusters and ecological amelioration, and conversely, the L-L clusters with environmental degradation. These findings corroborate prior research, affirming the nonrandom, clustered nature of the RCEI's spatial distribution.