Quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance: A case study in Beijing, China

• Published in: Ecological indicators 2023-04, Vol.148, p.110034, Article 110034
• Main Authors: Liu, Yang, Xia, Chuyu, Ou, Xiaoyang, Lv, Yingshuo, Ai, Xin, Pan, Ruiqi, Zhang, Yaru, Shi, Mengyu, Zheng, Xi
• Format: Article
• Language: English
• Subjects: Carbon balance; Optimization; Quantitative structure; Spatial pattern; Urban green space
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2023.110034

•The carbon emission coefficients method and net ecosystem productivity (NEP) were used to evaluate urban carbon emissions and green space carbon sinks, respectively.•A multi-objective programming model (MOP) was constructed to derive the optimal solution of the amount of urban green space based on the carbon balance theory.•Combined with ventilation resistance and species migration resistance assessment, priority distribution areas of green space in 2035 were identified.•The PLUS model was introduced to optimize the spatial pattern of urban green space.•Optimized green space network stability and carbon sinks will be enhanced in 2035. Cities experience the most intensive level of human activity. As a result, more than 60 % of global CO2 emissions come from urban areas. Urban green space has the dual ecological benefits of increasing carbon sinks and reducing carbon emissions. Creating green space is essential to promoting the development of a low-carbon cycle in a city. Therefore, exploring the quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance can effectively improve the total carbon sink of a city. Based on the carbon balance theory, this paper first evaluates the carbon offsetting capability (COC) of urban green space in Beijing in 2020. Then, CO2 emissions is predicted, COC improvement targets are established, and the quantity of standard green space is calculated under these targets in 2035. A multi-objective programming model (MOP) is constructed to derive the optimal solution to determine the amount of standard green space needed to meet the constraints of urban development planning and maximize the carbon sink. A circuit model is used to identify the priority distribution area of green space in 2035, and the Patch-generating Land Use Simulation (PLUS) model is used to simulate the spatial pattern optimization results. The results show that: (1) CO2 emissions in Beijing caused by human activities in 2020 totaled about 240.12 million tons, the net absorption of CO2 of green space was about 8.99 million tons, and the COC was about 3.74 %; (2) in 2035, Beijing’s CO2 emissions will be about 265.40 million tons. Under 5 %, 10 %, 15 %, 20 %, and 25 % COC improvement gradients, the demand for standard green space will be 12,204.80 km2, 12,763.80 km2, 13,353.85 km2, 13,943.90 km2, and 14,533.96 km2, respectively; (3) the results of the Multi-Objective Programming (MOP) model show that the opt