2.2. Data sources
The data from 2020 was used in this study for GEP calculation, and various data sources, including statistical and geospatial data, were integrated into this study.
Statistical data were obtained from the statistical survey information of the Miyun County Ecological Environment Bureau, Water Bureau, Meteorological Bureau, Cultural and Tourism Bureau, other industry departments, and the Miyun Statistical Yearbook. Ecosystem classification, vegetation coverage, biomass, evapotranspiration and other geospatial data were obtained from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, with 10, 250, 250 and 500 m data resolutions. Rainfall data (Peng et al. 2019) were obtained from the National Earth System Science Data Center (http://www.geodata.cn) at a resolution of 1 km.
2.3. Methods
The GEP accounting of Miyun County includes three categories: material supply, regulation service, and cultural service. Material supply includes crop supply products and regulation services with seven types of products: products for water retention, soil retention, flood control, carbon sequestration, air purification, water purification, and climate regulation. Cultural services include nature-based tourism, recreation, and leisure. For these ten types of products, the potential (Ouyang et al. 2013) and actual GEPs (National Development and Reform Commission 2022) were calculated in this study, and the accounting indicators are listed in Table 1.
Bivariate correlation analysis was used to assess the degree of association between pairs of variables. According to the search results of the Web of Science (WOS), core set = ”ecosystem service*” and ”driving force*”, the factors with the highest use frequency and no repetition were selected. Because of minimal spatial variation in climate factors such as precipitation and temperature in Miyun, these factors were not included in the analysis. Finally, the natural ecosystem area, built-up area, vegetation coverage, normalised difference vegetation Index (NDVI), slope, digital elevation model (dem), total population, and GDP were selected for the driving force analysis.