Village committees, as grassroots spontaneously formed by rural collective members in China’s hierarchy system, play an irreplaceable role in the management of rural public affairs. Based on the filed survey dataset taken from three pilot counties/districts in Sichuan province, we explored the significant role that village committees played in farmers’ withdrawal from rural homesteads (WRH). Our empirical results, according to binary logistic regression (BLR) modelling, indicated that the WRH was significantly affected by the triple roles of village committees, among which information intermediary was the most effective followed by the trust builder and then the coordinated manager. Firstly, village committees’ involvement facilitated the WRH by improving policy transparency and decreasing information cost. Secondly, the depth of village committees’ participation (i.e., being involved in multiple phases) positively affected the WRH given its signification of the participation of farmers. Whereas the breadth of participation (i.e., considering various demands of different participants) negatively affected the process of WRH by reducing the decision-making efficiency. Thirdly, farmers’ trust in institutions played a positive role in the WRH, but their confidence in village cadres had limited impact. We therefore argue that promising village committees should act as “all-round stewards” in the decision-making of rural households, which not only includes the transmit of information between those above and those below, but also needs to actively strive for farmers’ trust by letting their voice heard. Based on our empirical findings, this paper finally proposed some policy suggestions, such as strengthening mutual communication, empowerment of rural grassroots, encouraging farmers’ participation and improving formal institutions.

Aligning urban morphology with carbon emission intensity categories is essential for advancing sustainable urban development and achieving dual carbon objectives. This study utilizes data from 336 Chinese cities across 2010, 2015, and 2020 to construct multi-dimensional morphological indicators. Spectral clustering categorizes cities into four distinct classes: high-emission intensity, medium-emission ecological, medium-emission developmental, and low-emission. An integrated gradient boosting framework, combined with SHAP and PDP interpretability tools, identifies key morphological drivers and their nonlinear contributions to class assignments. Results demonstrate that morphological features exert nonlinear and threshold-dependent effects on carbon emission intensity category assignments, exhibiting substantial spatial heterogeneity across urban clusters. Core drivers, such as economic density and the landscape shape index, follow distinctly different decision pathways in each category. Furthermore, morphological factors produce non-additive interactive effects that generate region-specific shifts in classification probability. Through this classification-oriented approach, the study provides policymakers with a systematic and readily interpretable reference to inform the formulation of context-specific low-carbon spatial planning strategies.