## 5 Discussion
### Driving Mechanisms of CCD Between HF and EQ in China's Lake-Ring Urban Agglomerations  
Scholars have extensively researched human-driven mechanisms affecting ecosystems in recent years using models such as geographically weighted regression (GWR), Barrier,and Tobit models [52; 53]. Although these methods can quantify human-environment relationships, they do not account for the influence of HF and EQ on CCD. Furthermore, these models overlook the reinforcing or weakening effects of interactions between influencing factors on CCD. Unlike previous studies, this research introduced the GeoDetector model to address these shortcomings. The GeoDetector's factor detection results indicate that in China's lake-ring urban agglomerations, HF-related indicators exhibit stronger explanatory power for CCD, especially GDP, LUI, and NL. These indicators represent the dominant influence of economic growth, land expansion, and human activity intensity on the level of coordination in human-environment systems. This result aligns with the study by [PERSON] [54], confirming the decisive role of socio-economic factors in the coastal eco-environmental complex system. At the same time, although EQ-related indicators such as NDVI, LST, and NDBSI show relatively weaker explanatory power, they have exhibited a significant upward trend over the study period. This pattern reflects ecological conditions' increasingly prominent regulatory role in shaping human-environment interactions. The underlying mechanism can be understood in two ways. First, as land development pressure increases and ecological degradation risks intensify, LST and NDBSI have become more sensitive in reflecting ecological stress, thereby contributing more strongly to CCD. Second, implementing ecological restoration projects across regions, including wetland recovery and green space construction, has enhanced NDVI values and strengthened its effectiveness in indicating coordination levels. These trends suggest that ecological regulation is shifting from a passive response toward proactive intervention, and the role of ecosystem conditions in the evolution of human-environment systems are becoming increasingly significant. This trend is also supported by the study of Zhang in the middle reaches of the Yangtze River urban agglomeration, validating the positive feedback of ecological restoration [29]. Interaction detection results reveal synergistic mechanisms are increasingly driving coupling coordination in lake-ring urban agglomerations among multiple factors rather than a single driver. Within the HF dimension, combinations such as LUI\(\cap\)GDP, NL\(\cap\)GDP, and LUI\(\cap\)NL consistently showed strong enhancement effects from 2000 to 2020. NL intensity, as a proxy for population activity and infrastructure density, reinforces the \"concentration-expansion\" pattern of urban growth when combined with GDP and LUI. This interaction leads to a more pronounced nonlinear response in coupling human and environmental systems. Meanwhile, increasing interactions among EQ-related factors are also noteworthy. Combinations such as NDVI\(\cap\)NDBSI and WET\(\cap\)LST showed increased explanatory power, suggesting that ecosystems are responding to external pressures through complex feedback mechanisms. NDVI\(\cap\)NDBSI reflects a dynamic trade-off between green space degradation and land exposure. Urban expansion may fragment or replace green space, but vegetation recovery offers a restorative effect on CCD. WET\(\cap\)LST reflects a coupled response between moisture and thermal conditions. As surface water declines and thermal stress rises, ecosystem regulation is pushed to its limits, resulting in asymmetric feedback in the human-environment coupling. As these ecological interactions intensify, the system shows increasing regulatory capacity under development pressure, indicating emerging self-buffering behavior.