## 1 Introduction
Recognized for its relatively high retrieval accuracy, MODIS NPP has been widely adopted for CS studies at global [19], national [20], and regional [21] scales. Despite the growing urban population leading to increasing demand for urban green spaces among residents, urban vegetation cannot feasibly meet this demand through area expansion due to constraints from urban land scarcity [22]. This makes improving vegetation CS efficiency a potential strategy to partially address the demand gap. While several prior works of research had comprehensively explored factors influencing vegetation CS [23; 24; 25; 26], these studies primarily focus on large-scale contiguous natural ecosystems. Compared to natural ecosystems, urban vegetation CS is simultaneously influenced by both natural factors and diverse anthropogenic pressures [27; 28], potentially yielding distinct outcomes from those observed in natural ecosystems. Under intensifying pressures from climate change and urbanization, the impacts on urban CS may also exhibit significant spatial and temporal heterogeneity. There is therefore an urgent need to develop robust models to deeply understand urban vegetation CS contributions and their complex driven mechanisms. Existing related studies often make no distinction between municipal districts and their outside districts, and they lack comprehensive consideration of both driving capacity [14; 29] and underlying mechanisms [13; 30]. These severely constrain the attribution analysis of vegetation CS drivers within complex urban systems. This study conducted a quantitative investigation of vegetation carbon sequestration (CS) driving forces and mechanisms at the county-level scale within the Yangtze River Delta Urban Agglomeration (YRDUA) from 2001 to 2020, based on multi-source remote sensing data. The analysis specifically focused on the synergistic interactions between anthropogenic activities, climatic change, atmospheric CO\({}_{2}\) concentration, nitrogen deposition, soil nutrient cycles (particularly nitrogen and phosphorus), and vegetation photosynthetic processes. Given the complexity and nonlinearity of factors influencing CS, we introduce an integrated attribution and influence analysis framework [31; 32; 33], which integrates the nonlinear machine learning approach of Geographically Weighted Regression-Random Forest (GWR-RF) with Structural Equation Modeling (SEM) within a linear attribution framework [33]. This data-driven local approach comprehensively considers the impacts of driving factors on urban vegetation CS [34; 35; 36] and further distinguishes between direct and indirect driving mechanisms and their pathways [37; 38; 39] under a unified attribution framework. Our research focuses on the quantitative analysis of the driving forces, mechanisms, and pathways of multiple influencing factors on urban vegetation CS at the county scale within YRDUA, separately discussing the differences between municipal districts and their outside districts. Addressing these issues is critical for understanding the CS mechanisms of urban vegetation, ultimately providing essential support for the enhancement of natural-based solutions depending on ecosystem services under urbanization and climate change.