Assessing the carbon peak challenge in extremely cold regions: Scenario analysis of building emissions via an integrated Kaya-LMDI-SD framework

• An integrated Kaya–LMDI–SD framework is developed to dynamically assess building carbon emissions in Jilin Province, an extremely cold region. • Per-capita GDP and per-capita urban floor area are identified as the dominant drivers of building emission growth in Jilin (2006–2022). • The reduction in tertiary-sector energy intensity and per-capita energy consumption provided the primary inhibitory effects on emission growth. • Scenario analysis projects that building emissions in Jilin cannot achieve the 2030 national carbon peak target under baseline or low-carbon trajectories. The building sector is a major contributor to energy consumption and carbon emissions in China, playing a pivotal role in achieving the dual carbon goals. In extremely cold regions, building carbon emission intensity substantially surpasses that of other areas, yet the underlying mechanisms remain underexplored. This study develops an integrated framework combining the Kaya identity, the logarithmic mean Divisia index (LMDI), and a system dynamics (SD) model. Jilin Province, China, serves as a case study to identify the drivers of building carbon emissions and forecast future trends under multiple scenarios. Results indicate that between 2006 and 2022, per capita GDP and per capita urban building floor area were the primary drivers of emission growth, whereas the energy intensity of the tertiary sector and per capita energy consumption had significant inhibitory effects. Scenario analysis projects peak emissions of 73.35 MtCO 2 in 2031 under the low-carbon scenario, 86.90 MtCO 2 in 2034 under the baseline scenario, and 106.09 MtCO 2 in 2037 under the high-carbon scenario. Achieving the 2030 carbon peak target for Jilin’s building sector remains a formidable challenge. This study proposes targeted carbon reduction pathways, offering critical insights for evidence-based low-carbon policy development in extremely cold regions.