Evaluating whether zoning-based management is associated with measurable ecosystem function benefits is crucial for China’s national park system reform, yet most existing assessments emphasize greening or productivity alone. Here, we evaluate zoning-associated patterns in the Three-River Headwaters Region by combining MODIS-derived carbon use efficiency (CUE = NPP/GPP; 2001–2024), a boundary–buffer comparison with environmental matching, and an explainable machine learning attribution framework. NPP increased across all zones, whereas CUE remained stable to slightly declining, indicating a productivity–efficiency decoupling in the remote sensing record. Core and Buffer zones maintained higher long-term median CUE than the Outside zone, but matched boundary contrasts were heterogeneous, and the Experimental–Outside CUE contrast, although robust in sign, was small in magnitude. Zone–year attribution (2002–2020) suggests that interannual CUE variability is dominated by climate and land surface structure/change, while human pressure shows a smaller negative association; these grouped SHAP contributions should be interpreted as indicative rather than precise estimates. Post-2020 climate baseline residuals show persistent negative CUE anomalies in Buffer and Experimental zones, suggesting additional non-climatic influences but not demonstrating causality. Given the temperature-sensitive structure of MOD17 and the representativeness limits of QC-filtered 500 m observations, we interpret these results as management-consistent patterns rather than stand-alone causal proof. The findings support incorporating carbon use efficiency into zonal monitoring and may inform differentiated, efficiency-oriented management review.
Xiao et al. (Thu,) studied this question.