Exploring Nonlinear Thresholds and Ecological Compensation Mechanisms of Annual Carbon Fixation Rates in High-Density Urban Parks
高密度都市公園における年間炭素固定率の非線形閾値と生態補償メカニズムの探求 (AI 翻訳)
Nan Wang, Hao Wang, Weixuan Wei
🤖 gxceed AI 要約
日本語
本研究は、南京の149の都市公園を対象に、Sentinel-2高解像度データとランダムフォレスト・SHAPフレームワークを組み合わせ、年間炭素固定率（ACFR）を高い予測精度（R²=0.969）でモデル化した。不浸透面率30%超で炭素吸収が構造的に低下し、最適葉面積指数は2.5-3.5であることを定量的に特定。高人為撹乱下での植生最適化により、低撹乱環境と比較して限界ACFR応答が23.9%高いという生態補償効果を示した。これらの知見は、都市再生における具体的な制御指標を提供する。
English
This study modeled Annual Carbon Fixation Rate (ACFR) in 149 urban parks in Nanjing using Sentinel-2 data, Random Forest, and SHAP, achieving high predictive reliability (R²=0.969). Key findings: impervious surface >30% causes structural decline in carbon sinks; optimal Leaf Area Index is 2.5-3.5; under high anthropogenic disturbance, optimizing vegetation can yield 23.9% higher marginal ACFR response compared to low disturbance. The work provides quantifiable thresholds for urban micro-renewal and regeneration.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の高密度都市（東京、大阪など）でも、都市緑地の炭素固定能の評価と最適化は重要課題。SSBJなどの情報開示とは直接関係しないが、都市計画におけるGX実践（グリーンインフラ、カーボンニュートラル都市）に示唆を与える。
In the global GX context
This paper offers a replicable framework for assessing urban carbon sinks using freely available satellite data and machine learning, relevant to global cities pursuing climate neutrality. The nonlinear thresholds (e.g., 30% impervious surface) can inform urban planning guidelines and green infrastructure investments, aligning with nature-based solutions in urban climate strategies.
👥 読者別の含意
🔬研究者:Demonstrates a robust explainable ML framework for urban carbon modeling, providing transferable methodology for other cities.
🏢実務担当者:Urban planners and landscape architects can use the identified thresholds (e.g., impervious surface ≤30%, LAI 2.5–3.5) to design carbon-optimized green spaces.
🏛政策担当者:Provides evidence for setting urban green space standards and micro-renewal policies to enhance carbon sequestration in dense cities.
📄 Abstract（原文）
As high-density cities face severe conflicts between urban expansion and limited ecological space, exploring the potential drivers of carbon sequestration within urban green spaces is crucial. The overarching aim of this study is to establish an interpretable, model-based analytical framework for assessing the Annual Carbon Fixation Rate (ACFR). To address the limitations of linear models and “black-box” machine learning algorithms, this study analyzed 149 urban parks in Nanjing by coupling Sentinel-2 high-resolution data with a Random Forest algorithm and the SHAP framework to model Annual Carbon Fixation Rate. The model achieved high predictive reliability (R2 = 0.9690). This study quantitatively identified critical nonlinear thresholds: an impervious surface proportion exceeding 30% is associated with a structural decline in carbon sinks, while the optimal Leaf Area Index interval for maximum efficiency is 2.5–3.5. Moreover, the present research identified a potential model-derived ecological compensation effect; optimizing vegetation community structures under high anthropogenic disturbance showed a 23.9% higher modeled marginal ACFR response compared to low-disturbance habitats. This explainable framework attempts to translate complex model-derived biophysical associations into referenced quantitative thresholds. Importantly, the ACFR and derived thresholds represent remote sensing-based model estimates rather than field-measured ecological causality, serving as exploratory planning references. These findings challenge the assumption that greening is inefficient in degraded environments and provide concrete control indicators for targeted micro-renewal and sustainable urban regeneration.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/f17070849first seen 2026-07-22 05:22:47
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