Urban spatial compactness is crucial for reducing carbon emissions and promoting sustainable urban development. However, the potential of and variations in spatial compactness in county-level carbon reduction have been largely overlooked. Taking the county-level areas in the Beijing–Tianjin–Hebei (BTH) region as an example, after reviewing the dimensions, scales, and pathways of the existing research, we quantifies the spatial compactness from 2005 to 2015 in terms of density, morphology, and function. The results reveal that, firstly, BTH county-level areas’ carbon emissions, both per land area (CO₂/L) and per capita (CO₂/P), have generally risen. Heterogeneity varied across spatial compactness indicators: urban density and functional compactness increased, while morphological compactness showed a minor fluctuation. Second, the correlations with CO₂/L were stronger than those with CO₂/P, ranking from urban density, functional compactness > morphological compactness. Additionally, the geographically and temporally weighted regression (GTWR) model revealed the spatial and temporal heterogeneity in the impact of spatial compactness on carbon emission intensity. The heterogeneity results provide insights into the mechanisms and general patterns of the differences in the factors influencing carbon emissions in four different types of county-level areas, namely, central, eastern, southern, and northwestern, offering policy guidance to promote the reductions in both CO₂/L and CO₂/P. This study is helpful for proposing urban spatial planning strategies for carbon reduction and formulating differentiated carbon reduction policies.

Urban functional zones (UFZ) serve as fundamental units of human activity, and their spatial configurations significantly influence urban carbon emissions. However, current research often overlooks the heterogeneity of UFZ forms and the complex, multi-scale relationships underlying their impact on emissions. To address this gap, this study investigates the Yangtze River Delta (YRD) urban agglomeration by delineating UFZ from the dual perspectives of block function and form. By integrating multi-scale spatial analysis with explainable machine learning, this study establishes a morphological indicator system covering three dimensions: density, morphology, and structure. The results reveal a pronounced scale dependency in the effects of UFZ form on carbon emission intensity (CEI), with optimal analytical scales identified at 14 km for density, 5 km for morphology, and 7 km for structure. Notably, variations within these dimensions lead to distinct patterns of impact intensity, even within the same functional category. Furthermore, most indicators exhibit nonlinear, threshold-dependent effects on CEI. These findings provide actionable guidance for fine-grained urban planning and carbon mitigation strategies.