The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km² and 673 km², respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km². (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors.

Given the crucial role of land use in shaping coupled water–carbon processes in ecosystems, it is essential to assess carbon use efficiency (CUE) and water use efficiency (WUE) across different land use types. This study established an analytical framework incorporating trend analysis, partial correlation, and relative contribution methods to evaluate how WUE and CUE in the Yellow River Basin (YRB) responded to key climatic variables between 2001 and 2023. It also identified the dominant climatic drivers across different land use types during 2001–2022. The principal findings were as follows: (1) from 2001 to 2023, the mean WUE and CUE were 0.73 g C m⁻² mm⁻¹ and 0.60, respectively. (2) Wetlands and croplands had higher WUE, while grasslands and shrublands showed higher CUE. (3) MAT was negatively correlated with WUE and CUE across 89% and 74% of the YRB, respectively, while MAP and SR showed spatially variable effects. (4) MAT was the dominant factor driving WUE variation across all land use types (accounting for approximately 40%). Except for shrublands, CUE was mainly influenced by MAP. These results highlight differentiated climate impacts across land use types, providing important insights for ecosystem restoration and spatial governance.

Megacities in developing countries are still undergoing rapid urbanization, with different cities exhibiting ecosystem services (ESs) heterogeneity. Evaluating ESs among various cities and analyzing the influencing factors from a resilience perspective can effectively enhance the ability of cities to deal with and react quickly to the risks of uncertainty. This approach is also crucial for optimizing ecological security patterns. This study focuses on Xi’an and Jinan, two important megacities along the Yellow River in China. First, we quantified four ecosystem services for both cities: carbon storage (CS), habitat quality (HQ), food production (FP), and soil conservation (SC). Second, we analyzed the synergies and trade-offs between these ESs using bivariate local spatial autocorrelation and Spearman’s rank correlation coefficient. Finally, we conducted a driver analysis using the Geographic Detector. Results: (1) The spatial and temporal distribution of the four ESs in Xi’an and Jinan is quite different, but both cities show lower ES levels in the urban core area. (2) ESs in Xi’an showed a strong synergistic effect. Among them, CS-HQ had the strongest synergy of 0.93. In terms of space, the north is dominated by low–low clustering, while the south is dominated by high–high clustering. The FP-SC in Jinan showed a trade-off effect of −0.35 in 2000, which gradually weakened over time and was mainly distributed in the northern area of the city where cropland and construction were concentrated. (3) Edge density, patch density, and NDVI have the greatest influence on CS in Xi’an and Jinan. DEM, slope, and patch density have the greatest influence on Xi’an HQ. Temperature, edge density, and patch density have the greatest impact on Jinan HQ. NDVI and temperature have the greatest influence on FP in the two cities. DEM, slope, and edge density have the greatest influence on SC. Landscape fragmentation has a great impact on CS, HQ, and SC in Xi’an and Jinan. Due to insufficient research data, this study focused on only four ESs in Xi’an and Jinan, the megacities in the middle and lower reaches of the Yellow River. However, the research results can provide a new perspective for solving the problem of regional sustainable development and new directions and ideas for follow-up research in this field.

Evaluating whether zoning-based management is associated with measurable ecosystem function benefits is crucial for China’s national park system reform, yet most existing assessments emphasize greening or productivity alone. Here, we evaluate zoning-associated patterns in the Three-River Headwaters Region by combining MODIS-derived carbon use efficiency (CUE = NPP/GPP; 2001–2024), a boundary–buffer comparison with environmental matching, and an explainable machine learning attribution framework. NPP increased across all zones, whereas CUE remained stable to slightly declining, indicating a productivity–efficiency decoupling in the remote sensing record. Core and Buffer zones maintained higher long-term median CUE than the Outside zone, but matched boundary contrasts were heterogeneous, and the Experimental–Outside CUE contrast, although robust in sign, was small in magnitude. Zone–year attribution (2002–2020) suggests that interannual CUE variability is dominated by climate and land surface structure/change, while human pressure shows a smaller negative association; these grouped SHAP contributions should be interpreted as indicative rather than precise estimates. Post-2020 climate baseline residuals show persistent negative CUE anomalies in Buffer and Experimental zones, suggesting additional non-climatic influences but not demonstrating causality. Given the temperature-sensitive structure of MOD17 and the representativeness limits of QC-filtered 500 m observations, we interpret these results as management-consistent patterns rather than stand-alone causal proof. The findings support incorporating carbon use efficiency into zonal monitoring and may inform differentiated, efficiency-oriented management review.