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以淮南市市辖区城市公园绿地系统尺度和城市公园植物群落尺度为研究对象,利用景观格局指数法和i-Tree模型分别对城市公园绿地景观格局和内部植物群落的碳汇能力及效益进行分析,提出促进公园绿地碳汇效益提升的关键途径。研究结果表明:(1)淮南市公园绿地中专类公园数量少、面积较大,游园面积小、数量多;综合公园和社区公园数量较少,空间分布不均匀,聚集度指数(AI)、景观形状指数(LSI)、平均形状指数(MSI)均较小,碳汇效益较少。(2)各树种以中、小径级为主且为近期栽植,其碳汇能力未充分发挥。(3)调研区1 010株树木每年碳储量为203.42 t,总效益为187 146.4元;每年碳封存量为15.62 t,总效益为14 370.4元;单株效益较高的为槐(Styphnolobium japonicum)、无患子(Sapindus saponaria)、鹅掌楸(Liriodendron chinense),单株效益较低的为银杏(Ginkgo biloba)、鸡爪槭(Acer palmatum)。
Abstract:Based on the scale of urban park green space system and the scale of urban park plant community in Huainan City, the carbon sequestration capacity and benefits of urban park green space landscape pattern and internal plant community were analyzed using landscape pattern index method and i-Tree model, and the key ways to promote the carbon sink benefits of park green space were put forward. The results show that:(1) There are few specialized parks in the park green spaces of Huainan City but they have a relatively large area, while there are many garden parks with a small area. The number of comprehensive parks and community parks is small, and the spatial distribution is uneven. The aggregation index(AI), landscape shape index(LSI) and average shape index(MSI) are small, resulting in less carbon sink benefit.(2) Most of the tree species are of medium and small diameter classes, which are planted recently, so their carbon sink capacity is not fully exerted.(3) The annual carbon storage of 1 010 trees in the research area is 203.42 t, with a total benefit of 187 146.4 yuan; the annual carbon sequestration is 15.62 t, with a total benefit of 14 370.4 yuan. The tree species with the higher per-plant benefits are Styphnolobium japonicum, Sapindus saponaria, Liriodendron chinense, while those with lower per-plant benefits are Ginkgo biloba and Acer palmatum.
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基本信息:
DOI:10.13367/j.cnki.sdgc.2025.05.005
中图分类号:TU986.5;X173
引用信息:
[1]姚晓洁,李昊飞,杜存刚.“碳中和”目标下多尺度协同的城市公园绿地碳汇效益研究——以淮南市为例[J].山东理工大学学报(自然科学版),2025,39(05):7-14.DOI:10.13367/j.cnki.sdgc.2025.05.005.
基金信息:
安徽省自然科学基金面上项目(2308085ME183); 安徽省社会科学创新发展攻关研究课题(2023CX155)