In the pursuit of China’s carbon peak and carbon neutrality objectives, county-level areas assume a pivotal role in orchestrating diverse initiatives for low-carbon development. However, empirical evidence is limited. This paper aims to fill this gap by exploring the driving factors of carbon peak and carbon peak path at the county level, using Shandong Province as a case study. Employing data related to economic development, industrial structure, land utilization, energy consumption, and emission characteristics, a principal component analysis (PCA) was utilized to extract the following five driving factors of carbon peak: green transformation, urbanization, industrial construction, energy consumption, and environmental constraints. Subsequently, K-means clustering identified five cluster areas: (1) agricultural transformation pending area, (2) low-carbon lagging area, (3) industrial transformation area, (4) low-carbon potential areas, and (5) low-carbon demonstration area. Based on these areas, this study further elucidates spatial combination models of carbon peak within the urban system, spanning central cities, coastal cities, resource-based cities, and agricultural cities. The paper enhances comprehension of the integral role county-level areas play in achieving China’s carbon reduction objectives. By providing nuanced insights into diverse developmental trajectories and spatial interactions, the study contributes to effective low-carbon strategy formulation. The findings underscore the importance of considering specific county attributes in urban areas to devise precise optimization strategies and trajectories, ultimately facilitating the realization of carbon peak goals.

Long commuting times may constrain opportunities for exercise, but evidence remains limited on whether built-environment associations vary across area-level commuting-time burden. This cross-sectional spatial study analyzed 229,563 June 2024 Keep walking, running and cycling trajectories aggregated to 5802 500 m × 500 m grid cells in central Jinan, China. A within-mode, sample-relative activity index based on GPS-reconstructed distance and mean speed classified trajectories into low-, medium- and high-activity groups. Four theory-defined built-environment composites were examined using PPML models with distance to the urban-core reference, district fixed effects and 391 commuting-location clusters; primary models included 5012 grids. Green/recreation, street/public-transport accessibility and development intensity were positively associated with total and activity-specific trajectory counts in the primary models, whereas daily service provision was not independently associated. Continuous commuting time was not statistically associated with any outcome, and no built-environment dimension showed FDR-adjusted overall moderation. Alternative distance–speed weights did not alter the activity-specific conclusions. However, the green/recreation association was substantially attenuated after adjustment for the origin-based observed platform user base, while the street/transit association was sensitive to trajectory assignment and exposure specification. These findings support cautious interpretation of area-level platform-observed activity associations.