## 4 Results Analysis
### Spatial Autocorrelation Analysis of ESV  
The global Moran's \(I\) index was employed to assess the spatial autocorrelation of ESV intensity at a grid scale by using ArcGIS 10.8. The results revealed that the global Moran's \(I\) index for Putian City in 2000, 2010, and 2020 was 0.646, 0.640, and 0.637, respectively. These values were accompanied by Z-scores of 58.21, 57.60, and 57.39, respectively, and \(p\)-values that were all less than 0.0001. This indicates that ESV intensity in Putian City exhibited a positive spatial autocorrelation, demonstrating a non-random distribution pattern at the  
Figure 8: Spatial differentiation of ESV in Putian from 2000 to 2020. Sources: Drawings from this study.  
\(1\) km \(\times\) 1 km grid scale. The observed declining trend in Moran's \(I\) indices suggests a weakening spatial autocorrelation of ESV intensity in Putian City over time. This implies that the spatial clustering of ESV values has become less pronounced, suggesting a more dispersed distribution of ESs across the city.  
To further investigate the spatial patterns of ESV intensity, local spatial autocorrelation analysis was conducted using GeoDa 1.20 software. The results (Figure 9) revealed that the overall spatial pattern of ESV intensity in Putian City remained relatively stable from 2010 to 2020, with high-high (HH) and low-low (LL) agglomerations dominating the landscape. High-low (HL) and low-high (LH) agglomerations were less significant. HH agglomerations were primarily concentrated in the central and coastal regions of Putian City, characterized by flat terrain and extensive areas of cultivated land, grassland, and water bodies. These areas benefited from a favorable ecological environment, contributing to their elevated ESV intensity. In contrast, LL agglomerations were the most widespread, predominantly concentrated in Xianyou County in the southwest, Xiuyu District along the coast, and Licheng District in the city center. These areas were densely populated and underwent intense urbanization, leading to a decline in ESV intensity due to land-use conversion and environmental degradation. HL and LH agglomerations were sparsely distributed and scattered around the HH and LL agglomerations. These areas were primarily located at the interface between cultivated land, forest land, and orchard land. Significantly, the range of LL agglomerations was larger than that of HH agglomerations, and both ranges exhibited a gradual reduction over time, albeit at a slow rate. The range of insignificant regions remained relatively stable, suggesting a subtle weakening of spatial aggregation in ESV intensity. These findings highlight the persistent spatial disparities in ESV intensity across Putian City. While some areas have maintained or even enhanced their ecological value, others have experienced degradation due to urbanization and land-use change. The insights from this analysis are crucial for informed decision-making in land-use planning and conservation efforts, aiming to mitigate the negative impacts of urbanization and promote sustainable development in Putian City.  
Figure 9: LISA aggregation of ESV intensity in Putian City at the grid scale from 2000 to 2020. Sources: Drawings from this study.