Yellow River Delta National Nature Reserve plays a critical role in ecological conservation, and assessing its ecosystem service value (ESV) is essential for guiding sustainable management strategies that harmonize development and preservation. This study was motivated by the need to generate actionable insights for adaptive conservation planning in this vulnerable coastal region. We evaluated the spatiotemporal dynamics of ESV from 2000 to 2020 using a combination of remote sensing, geographic information system analyses, and statistical modeling. Primary drivers influencing the spatial heterogeneity of ecosystem service value were identified through geographical detector analysis, and future trends were projected based on historical patterns. The results revealed that (1) ESV showed a clear spatial gradient, with higher values in coastal zones, moderate values along river channels, and lower values inland, and exhibited an overall significant increase over the two decades, primarily driven by improvements in regulating services; (2) wetland area and precipitation were the most influential factors, though socio-economic elements and environmental conditions also contributed to ESV distribution; and (3) future ESV is expected to follow current trends, reinforcing the importance of current management practices. Given that the continuous increase in ESV from 2000 to 2020 was predominantly attributed to water body expansion, future conservation strategies should prioritize the protection and restoration of these water resources.

The Yangtze River Delta (YRD) region, including Shanghai City and the Jiangsu and Zhejiang Provinces, is the largest metropolitan region in China. In the past three decades, the region has experienced an unprecedented process of rapid and massive urbanization, which has dramatically altered the landscape and detrimentally affected the ecological environments in the region. In this paper, we analyzed the spatiotemporal variations of ecological conditions (Eco_C) via a synthetic index with analytic hierarchy processes in the YRD during 1990–2010. The relative contributions of influencing factors, including two natural conditions (i.e., elevation (Elev) and land-sea gradient (Dis_coa)), three indicators of human activities (i.e., urbanization rate (Urb_rate), per capita GDP (Per_gdp), the percentage of secondary and tertiary industry employment (Per_ind)), to the total variance of regional Eco_C were also investigated. The results showed that: (1) The Eco_C over YRD region was “Moderately High”, which was better than the national average and demonstrated obvious spatial variations between south and north. There existed fluctuations and an overall increasing trend for Eco_C during the study period, with 20% of the area being deteriorated and 40% being improved. (2) The areas with elevation below 10 m was relatively poor in Eco_C, while the regions above 1000 m showed the best Eco_C and had the most obvious changes (9.33%) during the study period. (3) The selected five influencing factors could explain 91.0–94.4% of the Eco_C spatial variability. Elevation was the dominant factor for about 42.4–52.9%, while urbanization rate and per capita GDP were about 32.5% and 9.3%.

Understanding the spatial and temporal evolution of ecosystem service values (ESVs) and their underlying drivers is essential for advancing ecological conservation and sustainable management in protected areas. The study area comprises ten national park system pilot areas in China, covering approximately 220,000 km² and encompassing environments ranging from tropical forests to alpine ecosystems. Using 1 km gridded data from the Spatial Distribution Dataset of Terrestrial Ecosystem Service Value in China for five observation years (2000, 2005, 2010, 2015, and 2020), we examined ESV dynamics, inter-service relationships, and their associated drivers through Mann–Kendall trend analysis, Spearman correlation analysis, partial least squares structural equation modeling (PLS-SEM), and Geodetector. Spatial trend analysis showed that, from 2000 to 2020, 58% of the total study area exhibited a decreasing ESV trend, 38% showed an increasing trend, and the remaining 4% remained stable; statistically significant decreases and increases accounted for 0.9% and 0.2% of the area, respectively. At the park level, ESVs increased in Three-River Source and Qilian Mountain but declined most markedly in Hainan Tropical Rainforest National Park. Positive associations among ecosystem services were more widespread than negative associations, although their spatial patterns differed among parks. Climatic, topographic, and socioeconomic variables showed service-specific associations with the estimated ESVs. Because policy implementation was not directly modeled, the analysis does not isolate the effects of national park designation or specific conservation measures. These findings can provide a national comparative baseline for monitoring ESV dynamics and may inform park-specific ecosystem service-oriented management strategies in China’s national parks.

Under the Yellow River Basin’s strategic imperative of ecological protection and high-quality development, the critical question is whether rapid urbanization’s population agglomeration and land expansion are genuinely aligned with-or increasingly decoupled from-underlying economic vitality. To address this, we construct a unified analytical framework that synchronously captures coupling coordination, decoupling trajectories, and land-use efficiency, operationalized through multi-source county-level panel data for the Jinan Metropolitan Area in lower Yellow River Basin across 2000, 2010, and 2020. Unlike conventional two-dimensional assessments, the framework distinguishes system coordination, evolutionary direction, and input–output performance while using harmonized nighttime light data to capture the spatial intensity of economic vitality. The results show that: (1) the mean county-level coordination grade increased from 4.76 to 5.32, although strong core–periphery disparities persisted; (2) the decoupling structure shifted from relative homogeneity to pronounced differentiation, and strongly decoupled counties accounted for 48% of all units by 2020; and (3) average efficiency rose from 0.25 to 0.39, but most counties remained below the efficiency frontier, producing a pattern of central agglomeration and peripheral dispersion. Cross-classification further identifies coordinated but inefficient, efficient but dysfunctional, and low-coordination–low-efficiency trajectories that cannot be detected using a single model. The framework therefore provides a transferable diagnostic tool for differentiated territorial planning in metropolitan regions undergoing rapid structural transformation.