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To explore the mechanisms of spatiotemporal changes in carbon storage (CS) and identify optimal pathways for achieving carbon neutrality. Existing studies have revealed the intrinsic relationship between land use changes and carbon storage. However, research gaps remain regarding the spatiotemporal driving mechanisms of CS in urban agglomerations from an economic-ecological gradient perspective. This study integrates a hybrid machine learning framework (coupling Categorical boosting-Shapley Additive exPlanations and GeoDetector based on optimal parameters-Wavelet analysis) to construct a tripartite CS assessment system encompassing “gradient analysis-scenario simulation-mechanism identification.” Based on the Technique for Order Preference by Similarity to Ideal Solution method, multi-gradient spatial units in the Yangtze River Delta were delineated. The PLUS-InVEST model was employed to simulate the spatiotemporal evolution of CS under business as usual, cultivated-land protection, and ecological protection (ELC) scenarios from 2030 to 2060, thereby revealing the gradient response mechanisms in ecological governance of territorial space. The findings indicate that (1) from the gradient difference dimension, the expansion of built land from 2000 to 2020 led to a continuous decline in CS, with the decline intensity in high-economic-gradient zones exceeding that in high-ecological-gradient zones, primarily due to significant reductions in forest and arable land in high-economic-gradient areas. (2) Spatiotemporal coupling characteristics revealed an increasing coastal-inland gradient and a decreasing north-south gradient. The fluctuation coefficient of CS in high-economic-gradient zones was 50% higher than in ecological zones. (3) Mechanism analysis demonstrated that natural factors (digital elevation model, slope) exerted decisive explanatory power over CS changes within gradient zones, while the nighttime light index exhibited a secondary driving effect in high-economic-gradient zones. Multi-model validation indicated that ecological governance policies could effectively trigger “gradient transitions” in CS by enhancing the interaction effects between net primary productivity and topographic factors. The proposed multi-gradient machine learning analytical framework provides a decision-making pathway of “gradient diagnosis-process simulation-policy response” for coordinated CS management in urban agglomerations and ecological governance of territorial space.

Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning