| Abstrakt: |
Ecosystem service value is crucial for balancing economic growth and ecological preservation in ecologically vulnerable watershed areas. Although Gross Ecosystem Product (GEP) has received significant attention, most existing studies have focused on how to measure it. Few studies have explored spatiotemporal variations in GEP and how land-use changes affect these variations regarding ecological restoration at the river basin level. Additionally, while many studies have examined the relationship between ecosystem service value and economic growth, there is little research on how components of GEP influence economic growth. Analyzing the spatiotemporal structure of GEP and its components could offer new insights into optimizing ecological restoration strategies and promoting sustainable development in vulnerable watershed regions. In this study, we used ArcGIS, InVEST, SPSS, and Python to analyze spatiotemporal variations in GEP in the Yongding River Basin within the Beijing–Tianjin–Hebei Economic Region from 1995 to 2020. Moran's Index and variance decomposition were applied to analyze the spatiotemporal structure. The grey prediction model forecasted GEP trends from 2025 to 2035. The random forest model was used to assess land-use changes' impacts on GEP. Paired T-tests were used to compare GEP and GDP, and a dynamic panel model was used to examine how ecosystem service value factors influenced economic growth. The results show the following: (1) Regarding values, GEP accounting and variance decomposition results indicated that ecosystem cultural service value (ECV) and ecosystem regulating service value (ERV) each contributed about half of the total GEP. Ecosystem provisioning service value (EPV) showed an upward trend with fluctuations. Regarding the spatial distribution, Moran's I analysis showed significant positive spatial correlations for EPV and ERV. The grey prediction model results indicated significant growth in GEP from 2025 to 2035 under current ecological restoration policies, especially for ERV and ECV. (2) In terms of the influence of land-use changes, random forest analysis showed that the forest land area was consistently the most influential factor across GEP, EPV, and ERV. Unused land area was identified as the most significant factor for ECV. (3) Before 2010, GEP was larger than GDP, with significant differences between 1995 and 2000. From 2010 onwards, GDP surpassed GEP, but the differences were not statistically significant. Dynamic panel regression further showed that the water conservation value significantly boosted GDP, whereas the water purification value significantly reduced it. This study highlights the importance of integrating GEP into ecological restoration and economic development to ensure the sustainability of ecologically vulnerable watershed areas. [ABSTRACT FROM AUTHOR] |
