碳汇
环境科学
环境工程
碳纤维
水槽(地理)
城市规划
中国
固碳
土地利用
环境保护
土木工程
生态学
地理
二氧化碳
工程类
气候变化
数学
地图学
复合数
考古
生物
算法
作者
Lingxiang Wei,Dongjun Guo,Jun Zha,Nikolai Bobylev,Zhilong Chen,Shasha Huang
标识
DOI:10.1016/j.tust.2023.105533
摘要
Urban carbon sinks from plant photosynthesis in urban blue-green spaces (BGSs) effectively sequester CO2 and achieve carbon neutrality. Building urban facilities underground saves land for the development of urban BGSs. Development and utilization of urban underground space (UUS) is increasing rapidly in most advanced cities globally, many of which are located in China. This study combined the planting design strategy of urban BGS and the vegetation carbon sequestration efficiency estimation method to quantitatively assess the ecological carbon sink potential of the use of UUS. Three types of BGSs: partly open green space (GPO), closed green space (GC), and partly open blue space (BPO), were first constructed based on their practicability in the city, and a planting strategy was designed for urban BGSs accordingly. Secondly, a model for the conversion of UUS development intensity into surface space to achieve ecological carbon sinks under three scenarios of UUS development intensity (low, medium, and high intensity) was constructed. Thirdly, methods for the calculation of carbon sequestration by GPO, GC, and BPO, were established based on the land-use carbon emission model. The developed method was finally validated through a case study in the Tianfu New District Directly Administered Area of Chengdu in Sichuan Province, China. The results of the study in Chengdu show that the higher the intensity of the use of UUS, the greater the proportion of carbon sink contribution to the CO2 emissions of the building industry for the planned population, and a maximum of 7% of carbon emissions absorbed from the building industry for the planned population was achieved. These findings serve as a reference for assessing the use of UUS to achieve urban carbon neutrality in other cities.
科研通智能强力驱动
Strongly Powered by AbleSci AI