Heterogeneity and Influencing Factors of Carbon Sequestration Efficiency of Green Space Patterns in Urban Riverfront Residential Blocks

  • Yunfang Jiang*
  • , Di Xu
  • , Lixian Peng
  • , Xianghua Li
  • , Tao Song
  • , Fangzhi Zhan
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Green spaces in waterfront residential blocks, where the water landscape and green space intersect, have a special carbon sequestration effect due to the distinct ecological interaction between water bodies and green spaces. Studying the carbon sequestration efficiency of green space patterns is crucial for enhancing urban ecological quality. Herein, 100 residential blocks adjacent to water bodies in Shanghai were selected as case areas, and green space pattern classification, random forest algorithm and spatial configuration quantitative analysis were used to analyse the impact of spatial morphology factors, surrounding building environment and water–green coupling environment on the CS efficiency of the green space in residential blocks. The results showed that the importance of the green space morphology index influencing CS is significantly greater than that of the building environment index. Among the indices, the fraction vegetation coverage, coverage ratio of evergreen broadleaved trees and canopy coverage of the green space have a more significant effect. Moreover, the different types and compositions of tree species in residential green spaces have different impacts on CS. Residential blocks with higher levels of water surface ratio (Wr) have a slightly higher CS of the internal green space. In residential blocks 500 m from water bodies, Wr has a significant impact on the CS capacity of the green space. The blocks with an external greenway pattern and external greenway–green grid pattern provide an advantageous environment for CS. This study provides a reasonable basis for the development of riverfront green spaces to increase carbon sequestrations.

Original languageEnglish
Article number681
JournalForests
Volume16
Issue number4
DOIs
StatePublished - Apr 2025

Keywords

  • carbon sequestration effect
  • green space pattern type
  • interpretable machine learning
  • riverfront residential blocks

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