## 5 Discussion  
The impact of human activities on vegetation growth is a perennial concern among scholars. An objective evaluation of this impact remains elusive, but the residual analysis method has emerged as a popular approach. This method involves fitting and removing natural factors to isolate the impact of human activities [17; 18]. For instance, [PERSON] et al. [16] used this method to quantitatively evaluate the impact of human activities on NDVI in the Qinling Mountains and found that human activities mainly caused vegetation degradation. However, this approach has limitations since it does not account for all natural factors, leading to less accurate assessments of anthropogenic impacts. Another commonly used approach involves representing human activities through single data sets related to human activities, such as nighttime light data and land use data [38; 39]. Using nighttime lights, [PERSON] et al. [38] characterized the intensity of human activities and concluded that human activities had a predominantly negative impact on NDVI in Anhui province. However, relying on single data sets may not provide an integrated and comprehensive representation of human activities. On the other hand, mapping human footprint data based on eight human activity variables, including population density, environmental buildings, and land use data, can lead to a more comprehensive quantification of human activities [21]. Human footprint data have been used to evaluate ecological vulnerability on the Tibetan Plateau [40], and explore arable land expansion [41]. In this study, we use human footprint data to directly quantify human activities and accurately assess their impact on vegetation ecology EQI. When assessing the impact of human activities on vegetation ecology, it is important to consider both the positive and negative facets. Figures 11 and 12b in our paper depict the spatial aggregation of human activities and EQI. The \"high-high\" type is primarily distributed within urban green spaces and nature conservation areas, where anthropogenic aforestation and ecological protection policies have led to a positive impact of human activities on EQI [42; 43]. In contrast, the \"low-high\" type, mainly concentrated in ecologically fragile areas and urban centers, has a negative impact of human activities on EQI. We recommend further strengthening ecological protection and balancing human and ecological needs [44]. Between 2000 and 2018, the \"low-high\" type proliferated in cities, revealing the ecological challenges arising from social development. It is therefore imperative to accelerate the construction of ecological civilization and maintain a balance between human and ecology. When studying the drivers of vegetation ecological quality, scholars typically use correlation analysis [45] and principal component analysis [46] to establish mathematical models between vegetation and factors. However, the spatial role of factors' influence on vegetation cannot be ignored, and Geographically Weighted Regression (GWR) models were developed to address this issue. GWR can explain the degree of influence of independent factors on dependent variables at different spatial locations, but this is based on the same scale (same bandwidth). To overcome this limitation, MGWR was developed to allow individual factors to reflect their spatial influence on the target variable at different scales (different bandwidths) [37]. In [PERSON] et al. 's study of FVC in Northeast China, temperature affects NDVI at large scales (bandwidth 193) and human activities at small regional scales (bandwidth 27) [47]. Similarly, in Anhui Province, human activities have varying patterns of influence on EQI at each small regional scale, while rainfall impacts on EQI display a clear pattern at the provincial scale, following a decreasing trend from south to north (Figure 12). These findings suggest that the influence factors of vegetation growth differs with spatial location, and there are scale differences in the influence of factors on vegetation growth.