Multi-Scale Spatial Structure Impacts on Carbon Emission in Cold Region: Case Study in Changchun, China

Cities in cold regions face significant challenges, including high carbon emissions, intense energy use, and outdated energy structures, making them critical areas for achieving carbon neutrality and sustainable development. While studies have explored the impact of spatial structures on urban carbon emissions, the effects of multi-scale spatial structures remain insufficiently understood, limiting effective spatial planning strategies. This research examines Changchun, a city in a severe cold region, using data from 2012 to 2021, including road networks, land use, nighttime light, and energy statistics. Employing spatial syntax, landscape pattern indices, random forests, and segmented linear regression, this research establishes a carbon emission translation pathway to analyze the nonlinear effects of multi-scale spatial structures. Findings reveal a 26.70% annual decrease in carbon emissions, with winter emissions 1.84 times higher than summer ones. High-emission zones have shifted from industrial areas to transportation, commercial, and residential zones, reflecting growing seasonal variability and structural changes. Spatial complexity increased while connectivity declined. Multi-scale analysis identified a “decrease–increase–decrease” pattern, with macro-scale centrality declining and micro-scale hierarchy rising. These results provide both theoretical and practical guidance for urban planning in cold regions, supporting early carbon neutrality and long-term sustainable development goals.