The rapid pace of urbanization and industrialization has reshaped land use patterns globally, particularly within the interconnected domains of ‘production, living, and ecological spaces’ (PLES). Understanding the spatiotemporal evolution of these spaces is crucial for guiding sustainable development. Although a number of previous studies have explored aspects of their dynamics and driving factors, further investigation is needed to fully understand their long-term spatiotemporal evolution and the broader influences of socio-economic and environmental forces. This study aims to fill that important gap by leveraging advanced remote sensing techniques to analyze PLES transformations over a 30-year period. Using Henan Province, China, as a testbed, this study applies high-resolution Landsat data, land use transition matrices, dynamic degree analysis, Principal Component Analysis (PCA), and multiple linear regressions to uncover trends and underlying drivers. The results reveal a substantial reduction in production spaces by 3394.62 km² steady growth in living spaces by 4459.41 km² and complex, non-linear changes in ecological spaces, which decreased by 1067.43 km². Key driving forces, such as economic growth, urbanization, and fiscal policies are identified and discussed. These insights provide a robust framework for sustainable land use planning, with broader implications for rapidly urbanizing regions worldwide.

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.