This study examines the spatial imbalance and driving mechanisms of human–environment coupling in the Xiuhe River Basin, an important agricultural–ecological watershed in the middle and lower reaches of the Yangtze River. We integrated the coupling-coordination degree (C–D) model, Coupling Elasticity Index (CEI), spatial autocorrelation analyses (global and local Moran’s I, LISA, and Getis–Ord Gi*), and GeoDetector to assess spatial heterogeneity, classify coupling types, and identify key human–ecosystem. The results reveal marked spatial variation in coordination levels, which are higher in the central–western mountains and lower in the southeastern plains (global Moran’s I = 0.7344, p < 0.01), indicating significant spatial clustering. Using the CEI, the basin was classified into ecology-dominated zones (ecological advancement with human retreat), human-dominated zones (human advancement with ecological retreat), and relatively balanced zones. Upstream areas (CEI < 0.6) exhibit ecological advantages, whereas downstream plains (CEI > 1.5) experience dominant human pressures. GeoDetector analysis identified population density, proportion of built-up land, and water quality indicators (COD and TP) as the primary drivers, with interactions substantially enhancing explanatory power (e.g., PD × COD q = 0.86). These findings underscore nonlinear feedbacks and cross-scale interactions that influence coordinated ecosystem services in agricultural landscapes. We recommend differentiated management strategies: conserving upstream ecological functions, promoting balanced development in midstream areas, and regulating development intensity and pollution downstream to sustain human–environment coordination. This study provides quantitative evidence and methodological insights to improve understanding of ecological complexity and optimize governance of the agricultural landscape.

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls.