Ecological security driving mechanisms and optimization of zoning in Chinese urban agglomerations: A case study of the central plains urban agglomeration

• Develop an ecological security framework for EH, ER, and ESs. • Identify key drivers and their spatial heterogeneity influenced by landscape, human, and natural factors in three dimensions. • “Three-dimensional Rubik’s Cube” modeling to delineate ecological safety zones characteristic of the CPUA. • The “three zones, two belts, and one region” ecological security zoning and spatial distribution characteristics converge. The global ecological security framework is facing unprecedented challenges and transformations, with ecological security issues transcending national and regional boundaries and evolving into a global concern. The Central Plains Urban Agglomeration (CPUA) serves as a critical urban growth pole in China. In light of mounting ecological security challenges, including disparities in ecological efficiency and growing constraints from resource and environmental limitations, the CPUA urgently requires achieving a balance and mutually beneficial relationship between economic growth and ecological protection. This study examines 271 counties within the CPUA, utilizing both objective and subjective weighting methods to assess ecological security from a three-dimensional perspective, encompassing ecosystem health, landscape ecological risk, and ecosystem services over the period from 2000 to 2020. The analysis identifies dominant driving factors and spatial heterogeneity through the application of the Optimal Parameter Geodetic Detector (OPGD) and Multi-scale Geographically Weighted Regression (MGWR) models. Additionally, it combines the ’Three-dimensional Rubik’s Cube model with primary functional zoning to enhance the optimization of ecological security delineation. The results indicate that: (1) The ecological security situation in the CPUA remained stable from 2000 to 2020. The number of counties experiencing an upgrade in ecological security levels was greater than those experiencing a downgrade, with transitions primarily occurring between adjacent levels. Spatial disparities in ecological security were relatively small, and counties with lower ecological security levels tended to show greater clustering; (2) The explanatory power of the driving factors is ranked as follows: human factors > natural factors > landscape factors. Interaction detection factors exhibit varying degrees of dual-factor or nonlinear enhancement, with the combined strength of positive effects being greater than that of negative effects; (3) The spatial distribution characteristics of ecological security zones in the CPUA align with those of ecological security conditions. The CPUA is divided into “three zones, two belts, and one area,” with personalized ecological security model recommendations based on the primary functional zoning. This research furnishes a theoretical foundation for crafting scientifically informed ecological security policies for the CPUA and provides meaningful insights applicable to comparable urban agglomerations worldwide.