Exploring Suitable Urban Plant Structures for Carbon-Sink Capacities

Hyeseon Eom, Yeeun Shin, Sang-Woo Lee and Kyungjin An ()
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Hyeseon Eom: Department of Forestry and Landscape Architecture, Graduate School, Konkuk University, Seoul 05029, Republic of Korea
Yeeun Shin: Laboratory of Spatial Design Research, Konkuk University, Seoul 05029, Republic of Korea
Sang-Woo Lee: Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Republic of Korea
Kyungjin An: Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Republic of Korea

Land, 2025, vol. 14, issue 4, 1-16

Abstract: Urban parks, a type of urban green space, help mitigate environmental pollution and climate change by absorbing and storing atmospheric carbon. Optimizing their carbon-sink capacity requires thoughtful plant community design considering multiple factors. This study analyzed South Korean urban parks using QGIS and i-Tree Eco, integrating satellite imagery with field surveys at both spatial and tree scales. Park spaces were classified into six types based on the biotope criteria established in this study. Random forest regression was applied to each type to identify key variables influencing annual carbon sequestration and storage. The relationship between maturity and sequestration was examined for ten dominant tree species, offering insights for plant selection. Higher tree coverage and more deciduous species were linked to efficiency in carbon sequestration and storage. While variable importance varied slightly across biotope types, tree density was most influential for sequestration, and diameter at breast height and age were key for storage. These findings provide integrated insights into short-term sequestration and long-term storage, as well as strategic directions for structuring plant communities in urban ecosystems. The study offers empirical evidence for designing carbon-efficient urban parks, contributing to sustainable landscape strategies.

Keywords: carbon storage; carbon sequestration; plant community design strategy; i-Tree Eco; random forest regression (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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