## 2 Materials and Methods
### Data  
The border location data regarding the geographical limits of the research region were downloaded from the Chinese Academy of Sciences Resource and Environmental Science Data Center ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 30 October 2021). Figure 1: Location of the study area. * Land use data. The global LULC (land use/land cover) product (GlobeLand30, with aspatial resolution of 30 m, [[http://global7.org/home_en.html](http://global7.org/home_en.html)]([http://global7.org/home_en.html](http://global7.org/home_en.html)), accessed on 30 October 2021), was developed by China to the United Nations with extensive use. The images for land cover classification of development and update of GlobeLand30 [37] include TM5 ETM+, OLI multispectral images of Landsat 8 (USGS) and HJ-1 (China Environment and Disaster Reduction Satellite). What is more, the 16 m resolution GF-1(China High Resolution Satellite) multispectral image was also used for GlobeLand30 2020. The total accuracy of GlobeLand30 2010 and 2020 is 83.50% and 85.72%, respectively, and the kappa coefficient is 0.78 and 0.82 [38; 39; 40]. The results were both from the validation of over 150,000 points. The existing data of 2000, 2010 and 2020 effectively reflect the overall changes of global land use and landscape pattern during the past 20 years. The images include 6 first-level categories and 26 s-level categories. After merging and reclassifying the data from 2010 and 2020, the land use was divided into six categories within Beijing: cropland, forest, grassland, wetland, water body, impervious surface and bare area. Planning constraints data used in this study was Open Street Map (OSM, [[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)), accessed on 30 October 2021) data in 2020 and based on this data, the natural reserves, scenic spots, historical sites, religious sites, reservoirs, dams, major urban parks and other areas in the region were screened out by ArcMap. The development zone was derived from the centralized construction area in \"The Master Plan of Beijing (2016-2035)\". Physiographic factors, transportation factors and socioeconomic factors were determined to be development potential as spatial driving factors of the future land use changes in Beijing. The driving factors between 2000 to 2010 were used to simulate the land use in 2020 for accuracy verification. Correspondingly, the driving factors between 2010 to 2020 were used to simulate the land use in 2030. Referring to the previous studies [31; 41], we selected several forces (Table 1) as driving factors to predict the impacts of natural and socioeconomic conditions on land use changes. Among them, DEM was used as the main data source for more driving forces (slope, aspect). The data set was provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 30 October 2021), processed by using the data of the first version (V1) of Aster GDEM in 2009. It is a digital elevation data product with a global spatial resolution of 30 m.
* Vegetation net primary productivity data. The MOD17A3 HGF NPP products [42] came from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) which is a satellite-based sensor used for earth and climate measurements. MODIS data and products acquired through the LP DAAC. The temporal resolution is 1 year, and the spatial resolution is 500 m. The MOD17A3 HGF is derived from the sum of all 8-day net photosynthesis (PSN) products (MOD17A2H). The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). We cut and mosaicked the band of NPP from 2000 to 2020 and the pixel value is multiplied by the scale factor of 0.0001 to obtain the annual real value of NPP.