## 4 Discussion
### Associations between CCD and Driving Covariates  
In our study, we employed a geo-detector model (GDM) to capture the spatially response characteristics of the dependent variable in relation to the independent variable. This approach allowed us to analyze how different factors contribute to the observed spatial patterns of the coordination between ESS and ESD across the study area (Figures 6 and 7). We found that socio-economic factors had a greater impact on the coordination relationship between ESS and ESD than natural and ecological factors, emphasizing the significance of socio-economic factors in shaping the spatial pattern of this relationship. This finding aligns with previous research that highlighted the role of socio-economic variables as determinants of ES supply and demand distribution [43; 83]. The dominant factors influencing the spatial pattern of CCD were identified as construction land, followed by population and GDP. The expansion of construction land, driven by extensive human activity, has significantly influenced land use patterns in terms of magnitude, type, and distribution [84]. The complex nature of human activity further complicates these relationships. However, these findings contrast with a study by [PERSON] et al. [82] in China's Loess Plateau, which found that vegetation cover had the greatest positive effect on the relationship between ES supply and demand. This discrepancy could be attributed to the dominant influence of vegetation coverage on both ESS and ESD in the Loess Plateau. Interestingly, the effect of grassland on CCD was second only to socio-economic factors, ranking below elevation and cropland. Grassland emerged as the vegetation cover factor with the strongest impact on CCD, indicating its significant role in shaping the spatial pattern of the coordination relationship between ESS and ESD in Shanxi Province. This finding is supported by previous studies that emphasized the influence of land use changes caused by socio-economic factors on ESs [85]. Notably, the effect of forestland and NDVI on CCD was much smaller compared to grassland, suggesting that the increase in grassland area through GFGP mainly influenced the relationship between ES supply and demand in Shanxi Province. According to data from the 2020 \"Shanxi Province Third Land Survey Main Data Bulletin\", grassland covers an area of 3.11 million hectares in Shanxi Province, and is primarily distributed in Datong, Xinzhou, Luliang, Jinzhong, and Linfen cities, accounting for 73% of the province's grassland [86]. However, the grassland ecosystem in the study area remains fragile, with 70% of the grassland experiencing varying degrees of degradation and facing challenges such as insufficient protection and restoration, low utilization and management efficiency, and a lack of effective technological support. Therefore, the coordination degree between ESS and ESD could be improved by implementing quantitative and spatial adjustments to grassland planting policies. In this study, the slope and precipitation factors were found to have no significant direct effect on CCD. However, after interacting with construction land, population, and GDP, slope and precipitation played an important role in shaping the spatial pattern of CCD. This indicates that socio-economic factors enhance the influence of natural factors on CCD. Generally, slope and precipitation influence the supply capacity and demand preferences for ESs by controlling the spatial distribution of human activity and landscapes [76], and previous studies demonstrated that precipitation facilitates coordination between ESS and ESD in arid and semi-arid areas [79]. The changes in terrain and precipitation affect both social and economic processes, leading to changes in coordination relationships. Previous research provided guidance on identifying factors that contribute to ES supply and demand mismatch [87], ESS, and ESD, including terrain ruggedness for supply and population density for demand [88].