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.