Exploring the matching patterns and driving mechanisms of ecosystem service supply–demand: a case study of the Zhengzhou metropolitan Area, China

• Quantitatively evaluated supply, demand, and mismatches of key ecosystem services in the ZZMA. • Integrated CatBoost and PLS-SEM to identify key socio-ecological drivers and disentangle nonlinear direct and indirect effects on ESSD. • Classified ecological functional zones based on ESDR bundles using SOM, guiding spatial planning and ecological management while considering both supply and demand. Enhancing spatial resilience through coordinated regional resource allocation requires an understanding of the mechanisms behind ecosystem service supply and demand (ESSD), which is essential to ecological security. This study provides a comprehensive evaluation on five key ecosystem services (ESs):crop production (CP), carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ) in the Zhengzhou Metropolitan Area (ZZMA), with great emphasis on their supply, demand, and supply–demand ratios (ESDRs). Machine learning models were employed to measure the relative contributions of individual driving factors behind each ESDR, while the strength and pathways of influence among multidimensional drivers were revealed using PLS-SEM. Based on identified ESDR patterns, functional ecological zones were defined, and governance strategies were proposed. The results showed that: (1) From 2000 to 2020, the supply of most ESs exhibited an overall upward trend, with the exception for WY and HQ, while demand for all services increased. ESDRs for CS, WY, and HQ declined, with CS and WY shifting from surplus to deficit, whereas SC and CP showed improvements. (2) the comprehensive ESDR (CESDR) was mainly driven by urbanization level, slope, Shannon’s evenness index, and vegetation emerged, with notable spatial and temporal variation in their effects. Vegetation and landscape configuration were predominantly mediating variables in shaping ESSD dynamics. (3) Four functional ecological zones were identified based on ESDR patterns, each with distinctive governance strategies and development pathways. These findings lay a robust foundation for integrated ecological governance and regional development, based on national ecological civilization goals and informing sustainable spatial planning and risk management.