## 4 Discussion  
### Impact of Forest Cover Change on Carbon Dynamics  
The bookkeeping model effectively estimates carbon stocks and fluxes resulting from LUCC [53; 9]. Although it is extensively utilized for national and global carbon estimations, it faces challenges when applied to regional scales with spatially detailed carbon accounting. To address this limitation, we combined the bookkeeping model with the CASA model, utilizing the linear relationship between forest NPP and forest biomass to estimate forest vegetation carbon density. Furthermore, we integrated literature-based carbon density for diverse vegetation and soil categories into the carbon accounting process, generating spatially detailed carbon flux data. This approach enables the monitoring of forest carbon dynamics under forest cover changes at fine scales in Shaoguan and Nanjing. Using the bookkeeping model in Nanjing and Shaoguan, we estimated the carbon fluxes resulting from forest cover changes from 2000 to 2022. The spatial distribution map of carbon flux (Figure 7) and statistical map (Figure 8) show that the carbon budget caused by forest cover change is similar and different in both regions. The similarity is that the forest area of Nanjing and Shaoguan decreased significantly during 2000-2022, which is consistent with the conclusion of [PERSON] et al. [32]. The decreased forest area indicated that the afforestation area is lower than deforestation's. Therefore, carbon emissions caused by forest cover change exceeded carbon sequestration in Nanjing and Shaoguan during the study period, resulting in net carbon loss. This is consistent with previous research results [62; 63; 64]. However, there is a significant difference in the resulting net carbon loss between the Shaoguan and Nanjing regions between 2000 and 2022. The net carbon loss in Shaoguan was 1.293 TgC. In contrast, the net carbon loss from forests in Nanjing was only 0.222 TgC. The main reason for this difference is that the forest area of Shaoguan has been reduced more than Nanjing's. The carbon flux caused by afforestation in Nanjing is mainly concentrated in the central and southern areas of Nanjing. The carbon fluxes caused by afforestation in Shaoguan were focused on the central, northeastern, northwestern, and southern regions. This is supported by the data on afforestation distribution in the Chinese Forestry Statistical Yearbook [65] ([[http://202.99.63.178/c/www/tjrij.html](http://202.99.63.178/c/www/tjrij.html)]([http://202.99.63.178/c/www/tjrij.html](http://202.99.63.178/c/www/tjrij.html)), [[https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2006050899?z=Z010](https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2006050899?z=Z010)]([https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2006050899?z=Z010](https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2006050899?z=Z010)), accessed on 14 March 2024). At the same time, we found that the carbon loss was mainly concentrated in the central part of Shaoguan and the central part of Nanjing. These regions are near farmland, indicating that expansion mostly led to deforestation. This suggests that human activities mainly dominate the change in forest cover in the two regions. However, according to Figure 3, the farmland area in Nanjing did not increase but declined, while the farmland area in Shaoguan showed an increasing trend. This showed that the two regions' factors driving forest conversion to farmland differ. This phenomenon may be due to policy differences in the different areas. Since the early 21 st century, Shaoguan has upgraded the scale and quality of its cultivated land to promote agricultural development. At the same time, Nanjing has strengthened its Yangtze River protection policy during the urbanization process, focusing on afforestation and forest protection [66]. In the meantime, Nanjing's urban expansion took up a lot of farmland. Nanjing needs to reduce the area of forests converted into farmland to maintain food security [67]. It also inhibited the afforestation area to a certain extent.