重慶市中心市街地における都市ブルー・グリーン景観パターンが炭素隔離に与える影響：XGBoost–SHAP分析
Effects of Urban Blue–Green Landscape Patterns on Carbon Sequestration in Chongqing’s Central Urban Area: An XGBoost–SHAP Analysis (原題)
Xiaoyu Gao, Zhongsheng Chen, Ling Zhang, Kai Xiong, Li Wang
🤖 gxceed AI 要約
日本語
重慶市中心部を対象に、2000・2010・2020年のリモートセンシングと土地利用データからCASAモデルでNPPを推定し、景観パターン指標とXGBoost・SHAPを組み合わせて炭素隔離の空間的要因を分析した。景観指標を加えたモデルはR²が約0.57〜0.62から0.73〜0.80へ大幅に改善し、標高に次いでPLANDが重要な予測因子であることを示した。地形とブルー・グリーン空間の面積・分断・連結性が山岳都市のNPP変動と相関することを明らかにした。
English
Using remote sensing and land-use data for 2000, 2010 and 2020, the study estimates NPP via the CASA model in central Chongqing and applies landscape metrics with XGBoost–SHAP to explain spatial carbon sequestration. Adding landscape indices raised cross-validated R² from ~0.57–0.62 to 0.73–0.80, with elevation and PLAND the strongest predictors. Topography plus blue-green space area, fragmentation and connectivity correlate with NPP variation in mountainous cities.
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
都市の炭素吸収源を定量化する手法は、自治体の脱炭素計画やグリーンインフラ整備の根拠として参考になるが、企業開示やSSBJ対応との直接の接点は乏しい。
In the global GX context
Adds a machine-learning approach to quantifying urban carbon sinks, relevant to city-level climate planning and nature-based solutions, though it does not engage TCFD/ISSB disclosure frameworks directly.
👥 読者別の含意
🔬研究者:都市炭素吸収の空間的要因をMLで説明する手法として、景観指標とSHAPの組み合わせが参考になる。
🏛政策担当者:都市計画・緑地政策において炭素吸収ポテンシャルを高める空間設計の根拠として活用できる。
📄 Abstract（原文）
Against the backdrop of the "Dual Carbon" goals, this study focused on the main urban area in Chongqing. With remote sensing, land use, meteorological data from 2000, 2010, and 2020 -- we used the CASA model to estimate vegetation net primary productivity, or NPP, which we compared to MODIS NPP products. By combining optimal scale analysis, landscape pattern indices, the XGBoost model and SHAP (the Shapley Additive exPlanations) interpretation methods, our study showed that the space relationship between the blue and green landscapes and how they help to reduce the amount of vegetation carbon sinks are associated with one another, not just by the space that the areas are in, but by these other ways. The results indicate that from 2000 to 2020, NPP in the study area showed an overall upward trend. High-value areas were primarily distributed in the peripheral mountains and continuous ecological spaces, while blue-green spaces in the central urban area and densely built river valleys exhibited greater fragmentation. Compared to the baseline model (M0) containing only elevation and slope, the extended model (M1) incorporating landscape pattern indices significantly improved cross-validation R² values: from 0.5954, 0.6248, and 0.5735 to 0.7996, 0.8031, and 0.7311 for the years 2000, 2010, and 2020, respectively.SHAP analysis revealed that among the selected predictors, elevation contributed the most. PLAND (Percentage of Landscape) demonstrated a significant predictive contribution across all three years, whereas the roles of ED (Edge Density), SPLIT (Splitting Index), and AI (Aggregation Index) varied interannually. Nonlinear responses and reference thresholds were identified for elevation, slope, PLAND, LPI (Largest Patch Index), and their contributions to NPP prediction; however, SHAP contributions do not imply direct causal effects. The study concludes that topographic backgrounds, combined with the area, fragmentation, and connectivity of blue-green spaces, collectively correlate with spatial variations in NPP within mountainous cities. These findings provide a reference for optimizing ecological spaces and enhancing vegetation carbon sink potential in the main urban area of Chongqing.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.22158/se.v11n4p47first seen 2026-10-25 04:50:16 · last seen 2026-11-06 04:55:27
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