Rapid urbanization and global climate change are likely to exacerbate urban flooding intensity, frequency, and uncertainty. Thus, it is fundamental and crucial to investigate the dominant influencing factors for the mitigation of urban flooding. However, the influence of building patterns on urban flooding remains limited. Taking Beijing, a typical megacity, as a case study area, we quantified the importance of building patterns and their interaction effect at the subwatershed scale using the boosted regression tree (BRT) and geographical detector model (GeoD). The results indicated that (1) the landscape shape index, slope, green space ratio and waterbody ratio were the most important influencing factors determining urban flooding, with a total relative contribution of 67.23%, (2) building metrics had a certain impact on urban flooding, and the sum of the relative contribution can reach 21.03%, (3) with urban flooding density, the landscape shape index, slope, and green space ratio exhibited a combination of negative and positive correlation, and (4) an enhancement effect existed between building metrics, especially the building congestion degree and building density. These findings provide quantitative insights that rational urban morphology planning can improve stormwater management and promote urban sustainability in megacities.

Urbanization significantly affects the global carbon balance. Therefore, clarifying the effect of urbanization-induced land use change on carbon emissions and proposing the zonal-based policy implementation became crucial. In this study, we estimated the future land use carbon emissions in three scenarios by adopting the partitioned Patch-generating Land Use Simulation Model (PLUS) in the Xiamen–Zhangzhou–Quanzhou (XZQ) metropolitan circle, China. The results showed that (1) the region could be divided into two sub-areas (carbon sink function zone and high-carbon optimization zone); the partitioned PLUS model had a higher accuracy in the developed areas, whereas it had a lower accuracy in the undeveloped areas; (2) the main characteristic of land use change was the construction on the land that was previously farmland, spanning 260.2 km²; the land used for construction would continue to expand, with 215.5 km² (8.2%), 261.0 km² (9.9%), and 129.5 km² (4.9%) in the natural development scenario (ND), economic development scenario (ED), and carbon neutrality scenario (CN), respectively; (3) the amount of carbon emissions would increase by 196.2 × 10⁴ tons and 235.4 × 10⁴ tons in the ND and ED, respectively, whereas, it would decrease by 49.0 × 10⁴ tons in the CN. This study made a beneficial attempt at partitioning based on carbon neutrality by improving the PLUS model simulation results and carbon emission assessment, which could provide methods and references for future similar studies. The findings suggest a method for the coordinated development between the carbon sink function zone and the high-carbon optimization zone. In addition, comprehensively understanding the land use change and its effects on carbon emissions, from this study, could provide important insights into carbon neutrality and regional sustainable development for urban planners and policymakers. In future research, the zoning research could be conducted at a finer scale, since the partition results were relatively coarse in this study.