Amid the Fourth Industrial Revolution and the 2030 Sustainable Development Goals (SDGs), China’s digital village initiative has emerged as a localized implementation for achieving multidimensional sustainability. However, the progress of digital villages in China remains uneven, posing challenges to achieving sustainable rural transformation. This study develops a multidimensional index system at four levels: rural digital infrastructure, the digital development environment in rural areas, the digital industry in rural areas, and agricultural production digitalization. Entropy weighting was used to evaluate digital village progress across 30 Chinese provinces (2013–2022). Kernel density estimation, the Dagum Gini coefficient, and the obstacle degree model were used to study China’s spatiotemporal dynamics, regional disparities, and digital village development barriers. The results show that between 2013 and 2022, digital villages in China advanced (the average annual growth rate: 9.43%), with a spatial distribution pattern of “east superior, west inferior, south prosperous, and north declining”. National and regional digital villages have advanced yearly, with absolute and relative disparities increasing, extensibility increasing, and multi-polarizing rising. Digital village development is becoming increasingly imbalanced, with inter-regional differences driving “east, central, and west” disparity and intra-regional disparities driving North–South disparity. Ranking the average hurdle levels: the digital industry in rural areas (45.94%) > the digital development environment in rural areas (24.83%) > rural digital infrastructure (21.85%) > agricultural production digitalization (7.38%). Taobao villages are a major restraint on China’s digital village development.

Taking a reinforced-concrete frame teaching building at a university as a case study, this study established a carbon emission accounting framework based on BIM-derived quantities, identified carbon emission hotspots at the stage, material, and component levels, and comprehensively evaluated the accounting results and emission reduction potential through sensitivity analysis, Monte Carlo uncertainty analysis, and low-carbon scenario assessment. The results showed that the total A1–A5 carbon emissions of the case-study building were 16,812.69 tCO₂e, corresponding to a carbon emission intensity of 442.51 kgCO₂e/m², of which modules A1–A3 accounted for 94.40%. C35 concrete, reinforcing steel, and autoclaved aerated concrete blocks collectively contributed 88.1% of A1–A3 emissions, while reinforcing steel, infill walls, and floor slabs constituted the major component-level hotspots. Monte Carlo simulation showed that the baseline accounting result fell within the main probability distribution range, while the integrated optimization scenario reduced total A1–A5 carbon emissions by 7.72%. This study quantified carbon emissions from the building material production, transportation, and construction stages (A1–A5) of a teaching building and identified priority targets for emission reduction. The results provide a quantitative basis for low-carbon material selection, key component control, and construction-stage optimization of teaching buildings in China.