Exploring future habitat quality changes in the Mid-Section of the Tianshan Mountains (MSTM) is crucial for regional biodiversity conservation. This study utilizes climate projection data from CMIP6 and integrates the SD-PLUS-InVEST analytical framework to simulate future LULC and habitat quality under three distinct future scenarios. Additionally, the XGBoost-SHAP model is applied to identify and interpret the key regulatory factors within the modeling framework that influence habitat quality spatial heterogeneity. The results show the following: (1) the projections under the three 2035 scenarios generally follow the development trend of 2020, with continued spread of dry land and construction land, but general reduction in the ecological land, reflecting an intensifying conflict between land development and ecological preservation. (2) Habitat quality varies significantly across scenarios, generally exhibiting a “U-shaped” distribution pattern characterized by larger areas of high and low quality and smaller areas of moderate quality. Within the SSP5–8.5 scenario, habitat quality is relatively poor, accompanied by pronounced spatial heterogeneity and imbalance. (3) NDVI is identified as the dominant factor influencing habitat quality spatial heterogeneity, followed by GDP, TEM, and DEM. Although the influence of these factors varies slightly across scenarios, their relative importance remains generally consistent, reflecting the structural stability and response coherence of the ecosystem.

Achieving synergistic governance between carbon emissions and ecosystem service value (ESV) in the Yellow River Basin (YRB) remains a significant challenge. Existing studies often rely on a single administrative scale and rarely extend spatial clustering results toward governance-oriented zoning frameworks that support differentiated management. To bridge these gaps, we developed a multi-scale remote sensing framework by integrating the China Land Cover Dataset (CLCD), NPP/VIIRS nighttime light (NTL) imagery, and energy consumption statistics to examine carbon emissions and ESV across the YRB at both county and grid scales. We estimated land-use carbon emissions by integrating emission coefficients with NTL modeling, while ESV was quantified via the equivalent factor method. By coupling dynamic carbon–ESV trends with their spatial association patterns (derived from bivariate LISA), we established a dual-dimensional Trend–Pattern framework for synergistic governance. Our findings reveal a pronounced asymmetry in regional development: while carbon emissions surged from 235 to 1033 million tons, ESV grew by a marginal 0.13% annually. Although carbon emissions and ESV are significantly negatively correlated (p < 0.001), this relationship diverges across scales—weakening at the county level but intensifying at the grid level. Notably, grid-scale analysis identified a contraction of over 56% in “Low carbon–High ESV” synergistic clusters in the upper reaches. This critical signal of declining carbon–ecological synergy was completely obscured by the averaging effect inherent in county-level assessments. Capitalizing on these scale-dependent insights, we delineated seven distinct governance zones, including Trend Control and Ecological Conservation Zones, thereby transitioning from mere spatial description to decision support for zoning-based governance. This study highlights the potential limitations of relying solely on single-tier administrative evaluations and offers a robust, multi-scale decision-support tool for carbon–ecological governance in the YRB.