中国の都市圏における複数交通モードの炭素排出評価と解釈可能な機械学習フレームワークによる予測：長江デルタからのエビデンス
Assessing carbon emissions from multiple transport modes and prediction via an interpretable machine learning framework in China's urban agglomerations: Evidence from the Yangtze River Delta (原題)
Xiaobin Ye, Fangxi Chen, Zhenyu Wang, Xin Ning
🤖 gxceed AI 要約
日本語
長江デルタ都市圏を対象に、複数交通モードの炭素排出を評価し、空間コンパクト性と貨物重力を新変数として組み込んだ解釈可能な機械学習フレームワークを構築した。CatBoostが最高精度を示し、SHAP分析によりGDP・人口・都市化が排出を支配し、空間コンパクト性が高いほど排出が低いことを明らかにした。水路・自家用車の排出増加が懸念され、鉄道ハブ拡大やコンパクト都市開発を政策提言する。
English
Using China's Yangtze River Delta, this study assesses multi-mode transport carbon emissions and builds an interpretable ML framework adding spatial compactness and freight gravity as novel predictors. CatBoost performed best (R2=98.42%), and SHAP analysis shows socioeconomic factors dominate while higher urban compactness links to lower emissions. Findings support cleaner waterway transport, scaled rail hubs, and compact urban development.
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
中国都市圏の交通脱炭素は日本企業のサプライチェーン排出（Scope 3）や中国拠点の移行計画に直結する。解釈可能MLによる都市単位の排出予測手法は、日本の自治体・企業の地域別排出推計やSSBJ対応のシナリオ分析にも応用可能な示唆を持つ。
In the global GX context
This adds city-level, multi-mode transport emission inventories and an interpretable ML prediction approach to the global decarbonization literature, complementing TCFD/ISSB scenario analysis where transport emissions are a major Scope 3 category. The spatial compactness and freight gravity variables offer transferable methodology for urban agglomeration-scale climate planning beyond China.
👥 読者別の含意
🔬研究者:解釈可能MLとSHAPを都市圏交通排出予測に適用した手法は、空間構造・貨物重力変数を組み込む際の参照モデルとなる。
🏢実務担当者:中国拠点を持つ企業は、都市別交通排出動向をScope 3推計や物流ネットワーク最適化に活用できる。
🏛政策担当者:コンパクト都市開発・鉄道ハブ拡大・水路クリーン化を排出削減策として優先する根拠を提供する。
📄 Abstract（原文）
Accurately assessing and predicting transportation carbon emissions (TCE) is essential for targeted mitigation. However, the lack of city-level historical emission inventories covering multiple transport modes over consecutive years has constrained TCE assessment and prediction at the urban agglomeration scale. Existing prediction models offer limited interpretability, focus mainly on socioeconomic factors, and rarely include spatial compactness or freight gravity. In response, we assess the carbon emissions from multiple transport modes and propose an interpretable machine learning framework for emission prediction, using the Yangtze River Delta urban agglomeration in China as a case study. The framework incorporates spatial compactness and freight gravity as innovative explanatory variables to capture intercity freight interactions and urban spatial structure. Our findings indicate that TCE increased by 40.98% from 2010 to 2021, while Shanghai's share fell from 37.46% to 29.36%; emissions from private transport and waterways also increased markedly. The rising emissions in private transport and waterways are particularly concerning. The CatBoost model (R 2 = 98.42%, RMSE = 1.3406) outperformed Linear Regression, Random Forest, XGBoost, and LightGBM, providing accurate insights into TCE dynamics. Based on SHAP interpretability, socioeconomic factors (GDP, population, and urbanization) dominate. Besides, higher urban spatial compactness exhibits a strong predictive association with lower emissions. Cities with strong highway and railway freight gravitational attractions witness a notable rise in emissions, reflecting the carbon pressure from intensive multimodal freight activities. These findings inform decarbonization planning through interpretable machine learning and point to cleaner waterway transport, scaled railway hubs, compact urban development, and less private car use.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.1016/j.rtbm.2026.101880first seen 2026-09-14 00:28:44
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