## 4 Results
### Spatiotemporal Interaction Effects of HF and EQ
#### 4.2.2 Interactive Stress Mechanism of HF and EQ on CCD  
Based on a comprehensive analysis of the CCD in China's lake-ring urban agglomerations, this section explores the driving mechanisms of HF and EQ indicators on CCD. Figure 6 presents the results of the factor detection model for China's lake-ring urban agglomerations and each individual agglomeration. The impact of each driving factor on CCD in the study area was statistically significant (\(p\) < 0.05), demonstrating robust and reliable results. For the entirety of China's lake-ring urban agglomerations (Figure 6a), the HF dimension factors demonstrated relatively high explanatory power for CCD. The most significant contributing factor to CCD was identified as GDP (q = 0.4218), followed by LUI (q = 0.4183) and NL (q = 0.4068). In contrast, the EQ dimension factors exhibited weaker explanatory power for CCD, with NDVI showing the highest contribution (q = 0.2979). During the study period, LST exhibited the highest increase in explanatory power for CCD, rising from 0.0336 in 2000 to 0.2253 in 2020. NDBSI followed, with its contribution increasing from 0.0985 to 0.1799. In summary, the long-term impact of the HF dimension on CCD exceeded that of other dimensions, highlighting its critical role in human-environment coordinated development. The explanatory power of the HQ dimension for CCD increased more significantly over time compared to the HF dimension, indicating the growing importance of the ecological environment in enhancing human-environment coordination.  
Figure 5: Spatiotemporal evolution characteristics of CCD. (**a**) Numerical development trend of different CCD levels; (**b**) Mean values and spatiotemporal distribution patterns of CCD.  
The factor detection results for the five lake-ring urban agglomerations (Figure 6b) showed that the ranking of driving factor contributions to CCD was THU > PYU > DTU > CHU > HZU. From 2000 to 2020, except for a slight decrease in THU (\(-\)2.87%), the average contribution of driving factors in other lake-ring urban agglomerations exhibited an increasing trend. For each urban agglomeration, (1) in DTU, LUI consistently remained the dominant factor. NDBSI and LST exhibited substantial growth in explanatory power for CCD, increasing by 248.26% and 194.46%, respectively. This highlights the growing role of bare soil and temperature changes in human-environment coordination. (2) In PYU, the dominant factors included LUI (0.2257), NDVI (0.2810), and NL (0.3281) in order of importance. The contribution of LST increased to 0.2615 by 2020, indicating the growing impact of the thermal environment on human-environment coordination. (3) In CHU, the contributions of NL, GDP, NDVI, and LST showed the most significant increases. NL increased from 0.1781 to 0.3495, emerging as the dominant factor in 2020. In contrast, LUI consistently maintained a high level with minimal fluctuation. (4) In HZU, the contributions of all factors remained relatively weak, showing either stability or mild growth. In 2020, NL and GDP led with values of 0.2875 and 0.2623, respectively, while the growth of EQ-related indicators remained limited. (5) In THU, HF dimension factors consistently remained high, significantly driving CCD development. Although WET and NDVI showed slight declines, they remained higher than those in other urban agglomerations, indicating that EQ regulation still played a crucial role. Overall, THU has developed a stable driving structure, while other urban agglomerations must strengthen EQ regulation capacity to achieve a higher level of human-environment coordination.