China’s vast rural landscape exhibits pronounced regional disparities in both foundational resources and development potential. In the context of nationwide rural revitalization efforts, the emergent divergence in village development pathways underscores a pressing need for context-specific, classified interventions. To furnish a scientifically grounded typology of villages and inform differentiated development planning, this investigation focuses on Hubei Province as an illustrative case. Synthesizing survey data from 32,457 villages, we developed a multidimensional evaluation framework encompassing four pivotal domains: economic vitality, social service provision, ecological integrity, and cultural value. Leveraging the Self-Organizing Feature Map (SOFM) neural network—an unsupervised machine learning algorithm—we performed a cluster analysis on multi-source, heterogeneous datasets. This technique enabled the objective delineation of spatial typological patterns among Hubei’s villages, elucidated their underlying classification architecture shaped by multifaceted drivers, and demonstrated the methodological robustness and applicability of this approach for large-scale village categorization. Grounded in the derived typologies and informed by strategic directives from higher-tier planning instruments, we conducted a nuanced examination of the distinctive attributes characterizing each village type. The findings provide scientific evidence and decision-making support for village classification and rural revitalization planning in Hubei Province, with valuable implications for other regions with similar development foundations in China.

Urban low-carbon development involves multiple socioeconomic and ecological dimensions that may not improve synchronously. This study evaluates the multidimensional low-carbon development of Wuhan, China, from 2014 to 2023 by integrating ecosystem carbon storage with carbon, economic, transportation, living, and environmental indicators. Entropy weight-TOPSIS, InVEST, coupling coordination, obstacle degree, and spatial autocorrelation analyses were combined to assess temporal performance, subsystem coordination and relative constraints, and the spatial distribution of carbon storage. Within the study-period evaluation framework, the comprehensive low-carbon development index increased from 0.398 to 0.598, while ecosystem carbon storage decreased by approximately 0.74 million t C, indicating divergent trajectories between aggregate performance and ecological carbon stocks. The coupling coordination degree increased from 0.544 to 0.782, corresponding to a shift from reluctant to intermediate coordination. The dominant relative constraint shifted from Living during 2014–2018 to Transportation during 2019–2023, with civilian vehicle ownership becoming the largest transportation-related shortfall by 2023. Carbon storage exhibited strong positive spatial autocorrelation and a broadly stable clustering pattern, characterized by peripheral high–high and central low–low clusters. These findings demonstrate the value of jointly considering overall performance, subsystem constraints, and spatial ecological conditions when evaluating urban low-carbon development.