| 摘 要： |
Urban construction land has relatively high human activity and high carbon emissions. Research on urban construction land prediction under carbon peak and neutrality goals (hereafter dual carbon goals) is important for territorial spatial planning. This study analyzed quantitative relationships between carbon emissions and urban construction land, and then modified the construction land demand prediction model. Thereafter, an integrated model for urban construction land demand prediction and spatial pattern simulation under dual carbon goals was developed, where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types. Using Guangzhou as a case study, the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030-2060 under baseline development and dual carbon goals scenarios. The simulation results showed that Guangzhou's urban construction land expanded rapidly until 2030, with the spatial pattern not showing an intensive development trend. Guangzhou's urban construction land expansion slowed during 2030-2060, with an average annual growth rate of 0.2%, and a centralized spatial pattern trend. Under the dual carbon goal scenario, Guangzhou's urban construction land evolved into a polycentric development pattern in 2030. Compared with the baseline development scenario, urban construction land expansion in Guangzhou during 2030-2060 is slower, with an average annual growth rate of only 0.1%, and the polycentric development pattern of urban construction land was more prominent. Furthermore, land maintenance and growth, that is, a carbon sink, is more obvious under the dual carbon goals scenario, with the forest land area nearly 10.6% higher than that under the baseline development scenario. The study of urban construction land demand prediction and spatial pattern simulation under dual carbon goals provides a scientific decision-making support tool for territorial spatial planning, aiding in quantifying territorial spatial planning. |
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