## 3 Results
### The Response of LCE Changes to Different Driving Factors
#### 3.4.2 Analysis of the Driving Factors Affecting LCE Changes at the Local Dimension  
For NTL variables, the estimated coefficients of most regions in the GZA were significantly positive (Figure 10a,d), implying that the LCE was increasing with the increase of the NTL index. Given that the NTL index can explain the level of urbanization and energy consumption to a certain extent, the acceleration of urbanization significantly promotes the increase of LCE. It showed that the high-value areas of the NTL index from 2000 to 2019 were mainly concentrated in the Guanzhong urban agglomeration area. It can be seen that the urban agglomeration area, with Xi'an as the center, was driving the development of the surrounding areas (Figure A3). From 2000 to 2019, the land-use type in the high-value area of the coefficient estimation was mainly constructed land and cropland, while the land-use type in the low-value area of the coefficient estimation was mainly forest. The NTL index and LCE are mainly positively correlated. Therefore, the higher NTL index indicated that the land-use type was to be a carbon source. On the other hand, compared with 2000, the estimated average of the minimum and maximum levels of coefficients increased in 2019.  
Figure 9: Correlation of LCE with factors (NTL, PD, and GPP) at global dimension.  
For the PD variables, the coefficient estimation indicated a significant spatial pattern of polarization across the whole study area (Figure 10b,e). Similar to the NLT, the high-value areas of PD were also distributed in urban agglomeration areas, such as Xi'an, Xianyang, and Baoji, and the high-value areas of PD showed an increasing trend from 2000 to 2019 (Figure A4). The significant negative correlation effect was mainly observed in grassland, cropland, and the forest area in the west of Xianyang, while the significant positive correlation effect was mainly found in construction land and cropland area. In 2000, the high estimated value was relatively small, mainly distributed in the north of Xianyang. Compared with 2000, there were concentrated areas with a high estimated value in all cities in 2019, which indicated that the development of the five cities had a center of gravity, and the low-value areas were distributed in the west and east of the study area.  
For GPP variables, the regression coefficients of most areas in the GZA showed significant negative values (Figure 10c,f), indicating that LCE tended to decrease with the increase of GPP. From 2000 to 2019, the total GPP in the study area indicated an increasing trend (Figure A5). The frequency of human activities in low-value areas was high, resulting in damage to the vegetation environment or growth difficulties. High-value areas were mainly distributed in the Qinling Mountains in the south of GZA, where the vegetation coverage was high and the ecological environment was well-preserved. Based on the spatial distribution results estimated by the correlation coefficient between GPP and LCE in 2000 and 2019, the high-value areas were mainly distributed in the southern part of Xianyang and the eastern part of Weinan. The low-value areas were mainly distributed in the junction of Xi'an, Xianyang, and Weinan, and in the middle and eastern parts of Baoji. From 2000 to 2019, the land-use types in the areas with high coefficient estimates were forest and grassland, while the land-use types in the areas with low coefficient estimates were construction land and cropland. In general, GPP and LCE showed a negative correlation, mainly because the land-use types in areas with high GPP value tend to be carbon sinks. On the other hand, compared with 2000, the estimated value of the coefficient generally declined in 2019.