The low-carbon utilization (LCU) of territorial space represents a critical strategy for addressing climate change and promoting sustainable development, yet current assessments in this domain remain insufficient. This study develops an integrated assessment framework utilizing kernel density estimation, an optimal parameter-based geographical detector, and the Tobit regression model to analyze the spatiotemporal evolution, typology, and driving factors of the LCU of territorial space in the Yangtze River Economic Belt. The findings reveal that the LCU index in this region increased from 0.548 in 2005 to 0.569 in 2020, despite significant regional disparities. Cities are classified into eight distinct types of LCU, with over 80% demonstrating poor performance in at least one functional space, particularly in urban space where the number of cities below the average is highest. The analysis demonstrates that the LCU of territorial spaces is influenced by an integration of natural conditions, socio-economic factors, and landscape patterns. In light of these findings, this study systematically proposes policy recommendations to enhance the LCU of territorial space. This research contributes to the establishment of a scientific evaluation framework for the LCU of territorial space, providing empirical evidence to improve spatial governance policies and support sustainable development.

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.