What question did this study set out to answer?

To evaluate carbon storage and sequestration patterns in high-altitude urban green spaces and identify how environmental, structural, and biodiversity factors influence these dynamics.

March 17, 2026Trees Forests and PeopleOpen Access

Urban forest carbon storage and sequestration on the Qinghai-Tibetan plateau: Machine learning analysis and management implications for Xining, China

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QRQiutan Ren GYGuoliang Yun ZYZhilan Yang

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Overview

Machine learning study reveals structural tree traits drive urban forest carbon dynamics in high-altitude environments, highlighting targeted community design for carbon sequestration.

Key Points

To evaluate carbon storage and sequestration patterns in high-altitude urban green spaces and identify how environmental, structural, and biodiversity factors influence these dynamics.
Quantified carbon storage and sequestration across 215 field sample plots in Xining, China, using the i-Tree Eco model.
Applied correlation analysis alongside an explainable machine learning approach (XGBoost-SHAP) to examine the relative importance of environmental, plant community, and biodiversity drivers.
Urban green spaces demonstrated mean carbon storage and carbon sequestration densities of 4.08 kg C m⁻² and 0.69 kg C m⁻² y⁻¹, respectively.
Tree structural diversity (variations in diameter at breast height and height) exhibited a greater influence on carbon metrics than traditional biodiversity indices like the Shannon-Weaver index.
Carbon accumulation decreased when tree crown width exceeded 3.5, while shrub performance was strongly mediated by elevation and rainfall.

Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/6a6fa4ae6c240de38cdb8a20 https://doi.org/10.1016/j.tfp.2026.101232

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March 17, 2026Trees Forests and PeopleOpen Access

Urban forest carbon storage and sequestration on the Qinghai-Tibetan plateau: Machine learning analysis and management implications for Xining, China

View Full Paper

Ask AI

Bookmark

Authors

QRQiutan Ren GYGuoliang Yun ZYZhilan Yang

Discussion

Member takes

Overview

Machine learning study reveals structural tree traits drive urban forest carbon dynamics in high-altitude environments, highlighting targeted community design for carbon sequestration.

Key Points

To evaluate carbon storage and sequestration patterns in high-altitude urban green spaces and identify how environmental, structural, and biodiversity factors influence these dynamics.
Quantified carbon storage and sequestration across 215 field sample plots in Xining, China, using the i-Tree Eco model.
Applied correlation analysis alongside an explainable machine learning approach (XGBoost-SHAP) to examine the relative importance of environmental, plant community, and biodiversity drivers.
Urban green spaces demonstrated mean carbon storage and carbon sequestration densities of 4.08 kg C m⁻² and 0.69 kg C m⁻² y⁻¹, respectively.
Tree structural diversity (variations in diameter at breast height and height) exhibited a greater influence on carbon metrics than traditional biodiversity indices like the Shannon-Weaver index.
Carbon accumulation decreased when tree crown width exceeded 3.5, while shrub performance was strongly mediated by elevation and rainfall.

Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/6a6fa4ae6c240de38cdb8a20 https://doi.org/10.1016/j.tfp.2026.101232

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