An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 10⁹kg and 4.28 × 10⁶kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.

Evaluating the effects of built environment factors (BEF) on residential land carbon emissions (RLCE) is an effective way to reduce RLCE and promote low-carbon development from the perspective of urban planning. In this study, the Grey correlation analysis method and Universal global optimization method were proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data in Zibo, a representative resource-based city in China. The results indicated that RLCE can be significantly affected by BEF such as intensity, density, morphology, and land. The morphology is the most critical BEF in reducing RLCE. Among them, the building height (BH) and building shape coefficient (BSC) had positive effects on RLCE, while the high-rise buildings ratio (HRBR) and RLCE decreased first and then increased. The R² of BH, BSC, and HRBR are 0.684, 0.754, and 0.699. The land had limited effects in reducing RLCE, and the R² of the land construction time (LCT) is only 0.075, which has the least effect on RLCE. The results suggest that urban design based on BEF optimization would be effective in reducing the RLCE.