## 4 Results and Analysis
### Discussion  
#### 4.3.1 Discussion of Correlation  
This paper analyzes the correlation between six ecological indicators, i.e., NDVI, LAI, GPP, LST, Wet, and CEI, and the LE in 16 cities along the part of the \"Silk Road Economic Belt\" from Xi'an to Urumqi in Mainland China. (1) Correlation coefficient  
Longevity is the consequence of complex contributions. Without considering spatial distribution, the Pearson coefficients between each index and LE were calculated. The results show that GPP, LAI, Wet, NDVI, and \(P\) are positively related to LE, but the Pearson coefficient between LST and LE and the Pearson coefficient between Wet and LE are negative, which indicates that the impact of temperature and humidity is different from that of other indicators on LE. With an increase in temperature and humidity, LE decreases. In recent years, the intensification of climate change has made the temperature and humidity change correspondingly, which affects the change in LE. For example, the intensification of urban heat-island effects may aggravate the response to heat stress through an increase in outdoor temperature and humidity, thus affecting the health and LE of the residents [42]. (2) Spatial crosscorrelation  
Spatial analysis is of great significance for the geographical phenomenon of public health inequality and the guidance of public health measures at the local scale [25]. The results of spatial crosscorrelation analysis on various indicators and LE shows that GPP, LAI, Wet, NDVI, and \(P\) are positively correlated with LE, but there are negative values for the Moran's I for LST and Wet. It can be seen that the impact of temperature and humidity in adjacent cities on local LE is negatively correlated. The result of the cumulative greenhouse effect (year-by-year) on global warming needs to be reflected on a longer time scale, so LST in the study area has a small trend change from 2010 to 2020, but global warming is indisputably happening. Some studies show that appropriate temperature reduction is conducive to the extension of life span [43; 44] and that the increase in temperature in adjacent cities may have a negative impact on the LE of local residents; the trend analysis results of Wet show that there are many areas with significant increases and decreases in the study area. The study area in this paper belongs to the northwest of China, with low humidity and drought. In recent decades, a \"warm and wet\" trend has been shown, and the humidity is gradually increasing. Besides, the improvement in humidity in adjacent cities may also affect the local LE. (3) Consistency between Pearson coefficient and Moran's I  
The research results show that the Moran's I value is basically consistent with the analysis results of the correlation coefficient, i.e., the Pearson coefficients and Moran's I for GPP, LAI, Wet, NDVI, \(P\), and LE are positive. The Moran's I and Pearson coefficient between GPP and LE are relatively high, indicating that the correlation between GPP and LE is the highest. This result shows that there is a certain positive correlation between the ecological environment and LE, i.e., with an improvement in the quality of the ecological environment, LE also increases. In some of our experiments, the Pearson coefficient and Moran's I between LST and LE are also consistent, i.e., they both have negative values, indicating that a rise in surface temperature has a negative impact on LE. Studies have shown that the occurrence of global warming may promote more frequent, more intense, and more lasting extreme temperature events, leading to an increase in heat-related mortality [15], which may affect LE. The Pearson coefficient and Moran's I between Wet and LE also have negative values, indicating that humidity also has a certain impact on LE. In the research related to LE, the Wet index is rarely used as an influencing factor to link with LE.