## 5 Conclusions  
In this paper, the EC-LUE model and CUE were used to simulate and analyze the ANPP in subtropical regions of China from 2001 to 2018 and its heterogeneity distribution at different altitudes. The Thornthwaite memorial model was used to calculate the potential net primary productivity of vegetation, and based on this scenario an analysis was set up to quantitatively assess the impacts of climate change and human activities on vegetation productivity in the study area. Finally, the main driving effects of climate factors on ANPP were studied. The conclusions are as follows:  
1. From 2001 to 2018, the ANPP in this region showed an overall growth trend, especially in mountainous areas, protected areas, and cultivated areas in the northeast plain, but showed an obvious downward trend around the southeast coastal urban agglomerations. 2. Human activities and climate change are the main factors driving the ANPP changes in the study area, and they jointly dominated the ANPP changes in 42.79% of the areas, in which the single impact of human activities exceeded the single impact of climate change on ANPP, dominating 35.99% and 20.44% of the areas, respectively. 3. ANPP presented a pattern of significant rise, slight fluctuation, and significant decline along the altitudinal gradient, and the climate-driven effects on ANPP were different at different altitude gradients. 4. Temperature and precipitation significantly affected the growth and distribution of vegetation in this region, showing an overall positive correlation with ANPP, but this relationship showed significant heterogeneity among different regions. In future studies, we will use more climatic and human factors related to ANPP to participate in the analysis of ANPP, and combine these factors to make a more accurate and comprehensive analysis of the driving effect of ANPP and the heterogeneity of changes along the elevation gradient in subtropical regions, so as to have a more comprehensive understanding of the carbon balance of subtropical forest ecosystems. Conceptualization, Z.W., Y.S. and B.X. ; methodology, Z.F., Y.S. and B.X. ; validation, Z.F., Y.S., Z.W., B.X. and Y.C. ; formal analysis, Z.F., Y.S., Y.Z. and B.X. ; investigation, Z.F., Y.S. and B.X. ; resources, Z.F., Y.S. and B.X. ; data curation, Z.F., Y.S. and B.X. ; writing--original draft preparation, B.X. ; writing--review and editing, Z.F., Y.S., Z.W., B.X. and Y.C. ; supervision, Z.F., Y.S. and Z.W. ; project administration, Z.F., Y.S. and B.X. ; funding acquisition, Z.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the 55 Engineering Research & Innovation Team Project of Beijing Forestry University (No: BLRC2023A03) and Beijing Municipal Natural Science Foundation (grant number 8232038). **Data Availability Statement:** The NDVI dataset came from the Moderate Resolution Imaging Spectroradiometer, which can be reached at [[http://eartheengine.google.com](http://eartheengine.google.com)]([http://eartheengine.google.com](http://eartheengine.google.com)) (accessed on 25 July 2023). The DEM dataset came from the International Center for Tropical Agriculture, which can be reached at [[https://data.tpdc.ac.cn/zh-hans/data/acb45](https://data.tpdc.ac.cn/zh-hans/data/acb45) ce8-2 bfe-4 abd-97 ff-ee6e727110703]([https://data.tpdc.ac.cn/zh-hans/data/acb45](https://data.tpdc.ac.cn/zh-hans/data/acb45) ce8-2 bfe-4 abd-97 ff-ee6e727110703) (accessed on 26 May 2023). The average temperature and dew point temperature data were both sourced from ERA-LAND. The precipitation data came from the Climatic Research Unit (CRU). The temperature and precipitation data can be reached at [[https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)]([https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)) (accessed on 12 June 2023).