Qiutan Ren

Dr. Qiutan Ren


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 March 2025 in Forests
Verified
Journal Article
Open access
PDF file
QRQiutan Ren
Qiutan Ren
LZLingling Zhang
Lingling Zhang
ZYZhilan Yang
Zhilan Yang
MZMengting Zhang
Mengting Zhang
MWMengqi Wei
Mengqi Wei
Shidong GeShidong Ge
Shidong Ge
As global climate change and urban issues worsen, increasing carbon offsets is crucial, with urban plants playing a key role. However, research on assessing plant carbon sequestration (CSE) capacity at the regional scale, selecting urban plants, and optimizing CSE capacity-based scenarios is still limited. A total of 272 plant species were surveyed in the nine cities of the Zhengzhou Metropolitan Area (ZMA). The i-Tree and biomass models estimated the average carbon storage (CS) density at 9.32 kg C m−2 and the CSE density at 0.55 kg C y−2 m−2 in the ZMA. The highest CS density (13.58 kg C m−2) was observed in Pingdingshan, while the lowest CSE density (0.36 kg C y−1 m−2) was observed in Xuchang. Hierarchical and cluster analyses identified plant species with balanced CSE capacity, adaptability, and ornamental value, such as Populus tomentosa Carr. and Salix babylonica L., as well as shrubs like Abelia biflora Turcz and Kerria japonica (L.) DC. Vegetation regeneration modeling indicated that CS could increase by 37%–41% along roads, 28%–43% in amenity areas, and 17%–30% near waterfronts over the next 50 years. These findings serve as a reference for urban regeneration and planning aimed at enhancing the carbon reduction potential of urban green spaces (UGS).

27 April 2026 in Forests
Verified
Journal Article
Open access
PDF file
SWShilong Wang
Shilong Wang
Shidong GeShidong Ge
Shidong Ge
HCHui Cao
Hui Cao
RWRan Wen
Ran Wen
XWXueqian Wang
Xueqian Wang
MZMan Zhang
Man Zhang
This study developed a comprehensive framework integrating ecosystem services (ESs), ecological adaptability, and ornamental value to guide tree species selection in historic cities constrained by soil salinization and subsurface heritage conservation. Taking Kaifeng, Henan Province, as a case study, we employed field surveys, i-Tree Eco, the Analytic Hierarchy Process, and K-means clustering to evaluate trees across protective, park, attached, and square green spaces. Results showed that carbon-related services dominated Kaifeng’s urban ES profile, with carbon storage (CS) and sequestration (CSE) value densities of 9.09 ¥·m−2 and 0.84 ¥·m−2·y−1, respectively. Air pollutant removal (AR) (0.21 ¥·m−2·y−1) and P (0.009 ¥·m−2·y−1) values remained comparatively low. Camphora officinarum Nees ex Wall delivered the highest annual ES value per tree (33.24 ¥·y−1). Plaza greenery outperformed other space types in overall service provision, and deciduous broadleaf species generated greater ES value than evergreen conifers. Cluster analysis identified four functional groups: stress-tolerant pioneers, balanced adapters, high-efficiency carbon sinks, and ornamental specialists—each suited to specific green space contexts. This integrated framework offers a transferable approach for evidence-based tree selection in saline historic cities, supporting nature-based solutions in urban green space (UGS) planning.