The Loess Plateau (LP) in China is an ecologically fragile region that has long faced challenges such as soil erosion, water shortages, and land degradation. The spatial and temporal variations in ecological environment quality on the LP from 2000 to 2020 were analyzed using the Remote Sensing Ecological Index (RSEI) on the Google Earth Engine (GEE) platform. The Sen, Mann–Kendall, and Hurst exponent analyses were used to examine the spatial variation trends over the past 20 years, while Geodetector identified key factors influencing RSEI changes and their interactions. The results indicate that (1) RSEI effectively represents the ecological and environmental quality of the LP, with 47% of the study area’s annual mean RSEI values over the 20-year period classified as moderate, ranging from 0.017 to 0.815. (2) Ecological quality trends showed improvement in 72% of the area, with a 90% overall increase, but 84% of these trends are not likely to continue. (3) Key factors influencing RSEI changes during abrupt change years included precipitation, land use/land cover, and soil sediment content, with precipitation and topography emerging as primary influences on ecological quality. Although natural factors largely drive ecological changes, human activities also exert both positive and negative effects. This study underscores the importance of sustainable ecological management and provides policy insights for advancing ecological civilization on the LP, contributing to the achievement of the Sustainable Development Goals (SDGs).

Cultural ecosystem services (CESs) reflect the interaction between ecosystems and human well-being. Owing to constraints in data availability and existing methodological limitations, deriving information from non-material ecosystem attributes was inadequate. We took Yulin City, located in the northern Shaanxi Loess Plateau, as a case study. Based on open-source geospatial data and user interaction data from social media, a coupled multi-source model was applied to elucidate the spatial distribution of CESs’ supply–demand flow. The Maxent and LDA model were utilized to quantify CES supply–demand, whereas the breakpoint and gravity model were applied to explain the direction and intensity of CES flow. The results indicated the following: (1) aesthetic was the most perceivable CES in Yulin, with 27% high supply areas and four demand topics. And the perception of the educational CES was the least pronounced, with only 2% of high supply areas and two demand topics. (2) Yulin exhibited a notable mismatching in CES supply–demand, with the supply–demand matching area constituting only approximately 10%. In the center of the city, CESs displayed a spatial pattern of a supply–demand deficit, while areas farther from the city center presented a spatial pattern of a supply–demand surplus. (3) The flow of CESs followed a pattern of movement from peripheral counties to central counties and from less developed counties to more developed counties. We proposed the following targeted recommendations: introducing low-perception CESs to promote the enhancement of ecosystem services (ESs); and alleviating CES supply–demand mismatches by enhancing transportation accessibility and protecting the ecological environment. Simultaneously, attention should be directed towards the developmental disparities between counties, providing differentiated guidance for CES spatial flow. Our study provided a theoretical foundation for understanding CES supply–demand flow and offered scientific insights for the spatial development of urban CES.

Introduction: The imbalance between ecosystem service supply and demand, combined with increasing ecological vulnerability, poses a significant challenge to the sustainable development of ecologically sensitive regions. Northern Shaanxi, a typical agro-pastoral zone in China with ecological fragility, is highly susceptible to severe soil erosion, desertification, and water scarcity. These stresses are intensified by intensive human activities and climate change. Therefore, it is necessary to systematically clarify the spatial differentiation of ecosystem service value (ESV) and vulnerability in this region, and to propose regionally tailored optimization and regulation strategies. Methods: This study introduces a “classification–simulation–strategy alignment” analytical framework to examine the interaction between ESV and ecosystem vulnerability (EV). Using Northern Shaanxi as a case study, the spatiotemporal dynamics of ESV and EV are quantitatively analyzed from 2000 to 2020. According to the spatial coupling relationship between ESV and EV, the region is divided into four functional zones: Low ESV–Low EV (general utilization areas), High ESV–Low EV (ecological stability areas), Low ESV–High EV (key restoration areas), and High ESV–High EV (core protection areas). The PLUS model is applied to simulate various future development scenarios, assess the ecological responses across these zones, and propose targeted adaptive management strategies for Northern Shaanxi. Results: The results indicate that ESV is generally higher in the southeast and lower in the northwest, whereas EV declines from northwest to southeast. Under future scenarios, key restoration areas and core protection areas show elevated ecological sensitivity and should be prioritized in governance efforts. Scenario simulations support the development of region-specific management strategies and contribute to a systematic governance pathway based on “classification–simulation–strategy alignment” tailored to the challenges of Northern Shaanxi. Discussion: This study advances understanding of the coordinated evolution of ESV and EV, providing both theoretical and practical foundations for precise zoning management and adaptive ecological governance in Northern Shaanxi. The findings also offer valuable insights for other ecologically sensitive regions with similar characteristics.