## 4 Discussion  
Scientific estimation and dynamic monitoring of the variation trend of LCEs in a long time series is the scientific basis and basic guarantee for formulating, implementing, and evaluating regional carbon emission reduction strategies [12]. Many previous studies have used different methods to estimate regional LCEs, such as using energy statistical yearbooks to calculate carbon emissions [12], but this method cannot achieve grid-scale carbon emissions [36]. Meanwhile, carbon emission research based on traditional statistical data is often limited to national or provincial levels due to data limitations, and it is difficult to refine to spatial scales which could provide more powerful support for  
Figure 10: Spatial distribution patterns of regression coefficients in MGWR. formulating regional and differentiated carbon emission reduction policies [37]. In addition, some studies selected the BK model to estimate LCEs. Although this model can quantify the impact of land-use change on carbon emissions, it is difficult to obtain parameters [38]. Compared to previous studies, this study used the carbon emission coefficient method to calculate direct LCEs and introduced the carbon emission grid data of fossil fuel data released by the ODIAC platform to calculate indirect LCEs [39]. Through the superposition of direct and indirect carbon emission results, we analyzed the temporal and spatial changes of LCEs in the GZA at a grid scale. The method used in this study mainly calculated indirect carbon emissions using 1 km high-resolution grid data; this simple process is highly accurate [40]. This study found a significant increase in LCEs in the GZA (Figure 4), which was consistent with most previous research [18; 31; 32; 35]. Especially for urban and cultivated areas, the LCEs show a significant increase (Figure 4). The land use transfer matrix shows (Figure 3) that the area of construction land in the GZA significantly increased since 2000, indicating that the urbanization process accelerated during this period. In the process of urbanization, the increase in population density and various anthropogenic energy consumptions has become the dominant factor that increases LCEs [41]. Spatially, regions with high LCEs were mainly distributed in the Xi'an, Xianyang, and Baoji urban agglomerations (Figure 6), and this distribution pattern was closely related to the development of heavy industry and the human energy consumption in these cities [42]. For cropland, this study found that LCE showed a significant increase in large areas of cropland. Although cropland had a certain carbon sequestration effect, it became a major carbon source due to the increased use of agricultural films, pesticides, and diesel fuel in various agricultural production activities [9]. For grassland and forest areas, this study found that the LCEs showed negative values, indicating that these areas showed obvious carbon sinks (Figure 5). However, due to the overall small changes in the areas of grassland and forest, the impact of grassland and forest changes on the total amount of carbon emissions was relatively weak (Figure 3). Considering the analysis of LCEs drivers on a grid scale, this study selected NLT, PD, and GPP as the driving factors to explore the impact of urbanization, population, and ecological restoration on LCEs. The NLT can reflect the intensity of regional economic development, and changes in PD will directly cause changes in carbon emissions [43]. The GPP can reflect the carbon sink level of vegetation [44]. This study found that NLT, PD, and GPP have significant spatial heterogeneity in their impact on LCE in the study area (Figure 9). The NLT, PD, and LCE showed a positive correlation, while GPP showed a negative correlation with LCE (Figure 10). The results of this study indicated that energy consumption driven by regional industrial agglomeration, urban scale expansion, and population aggregation to cities became the main reason for the increase in LCEs in the study area.