Focusing on the core issue of the multi-scale characteristics and driving mechanisms of spatial differentiation of rural settlements (RS) in the middle-lower Yellow River Basin (ML-YRB), this study aims to provide a scientific basis for the development and protection of regional RS. Using nearest neighbor analysis, kernel density estimation and spatial autocorrelation, the study systematically reveals the spatial distribution patterns and scale-dependent differences of RS in ML-YRB at city, county, and town scales. Furthermore, geographic detectors are employed to quantitatively evaluate the explanatory power of natural geographical and socio-economic factors for RS spatial differentiation. The results show that: (1) In ML-YRB, RS shows clear spatial clustering, concentrating in areas with gentle slopes, low altitudes, favorable thermal humid conditions, and high agricultural potential. (2) RS clustering varies across scales. The Moran’s I values are 0.79, 0.75, and 0.81 at city, county, and town scales. (3) Geographical environment, location conditions, and socio-economic factors together shape the spatial pattern of RS in ML-YRB. Specifically, lower and flatter terrain, soil more suited to farming, and proximity to rivers and lakes are linked to denser settlements. Also, areas near cities and roads show a clear RS agglomeration effect. (4) Key factors affecting the spatial distribution of RS have been quantitatively identified: soil type, population density, water proportion, topographic undulation, road network density, and distance from the central town. These findings offer a direct basis for creating tailored spatial policies for RS development in ML-YRB.

Urban living space (ULS) is known to be a significant contributor to carbon emissions. However, there is a lack of studies that have considered the impact of spatial organization indexes (SOIs) of various scales on urban living space carbon emissions (ULSCE), and so far, no definitive conclusions have been reached. To address this gap, taking Tianjin as an example, the measurement methods of ULSCE and SOI at different scales were proposed, and a random forest model was constructed to explore the effects of SOI on ULSCE. The results indicated that on the district scale, Beichen had the highest carbon emissions and absorption in 2021, with carbon emissions reaching 1.43 × 10⁸ t and carbon absorption at 7.29 × 10⁵ kg. In terms of area scale, the comprehensive service area had the highest carbon emissions at 3.57 × 10⁸ t, accounting for 47.70%, while the green leisure area had the highest carbon absorption at 5.76 × 10⁵ kg, accounting for 32.33%. At the block scale, the industrial block had the highest carbon emissions at 1.82 × 10⁸ t, accounting for 54.02%, while the forest block had the highest carbon absorption at 1.25 × 10⁶ kg, accounting for 91.33%. Each SOI had varying impacts, with the industrial land ratio (ILR) having the highest order of importance at the area scale, followed by road network density (RND), residential land ratio (RLR), bus station density (BSD), public service facilities land ratio (PLR), land mixing degree (LMD), open space ratio (OSR), and commercial land ratio (CLR). ILR, RND, and RLR were particularly important, each exceeding 10%, with importance values of 50.66%, 17.79%, and 13.17%, respectively. At the block scale, building area (BA) had the highest importance, followed by building density (BD), building height (BH), land area (LA), and floor area ratio (FAR). BA and BD were particularly important, with values of 27.31% and 21.73%, respectively. This study could serve as both theoretical and practical guidance for urban planning to aid the government in developing differentiated carbon emissions reduction strategies that can mitigate the heat island effect and promote low-carbon healthy urban planning.

Against the backdrop of the intensifying global climate crisis, urban construction land (UCL), as a major source of carbon emissions, faces the severe challenge of balancing emissions reduction and development in its low-carbon transformation. This study is dedicated to filling the theoretical and methodological gap in the refined assessment of urban construction land carbon effects (UCLCE) spatial heterogeneity among regions, and proposes and validates an innovative block-scale prediction framework. To achieve this goal, this study takes the central urban area of Changxing, Zhejiang Province, as the study area and establishes a BP neural network model for predicting UCLCE based on multi-source data such as building energy consumption and built environment elements (BEF). The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, p < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. Optimization trials on representative blocks achieve up to 25.45% carbon reduction. This study provides theoretical foundations for understanding UCLCE spatial heterogeneity while delivering scientifically grounded tools for diagnosing built environment issues and advancing low-carbon optimization in urban renewal contexts. These contributions carry significant theoretical and practical implications.