| 基于可解释性机器学习的城市道路交通碳排放驱动机制识别 |
| 摘要点击 250 全文点击 17 投稿时间：2025-05-26 修订日期：2025-08-05 |
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| 中文关键词 道路交通碳排放 机动车 机器学习 SHAP值 驱动因素分析 |
| 英文关键词 road transportation carbon emissions vehicles machine learning SHAP value analysis of driving factors |
| DOI 10.13227/j.hjkx.202505277 |
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| 中文摘要 |
| 在城市化加速发展的背景下，机动车保有量持续增长，导致道路交通碳排放迅速上升，成为制约城市绿色转型的重要因素. 为系统识别道路交通碳排放的关键驱动机制，采用“自下而上”法构建2003~2022年福建省城市群的道路交通碳排放清单，并将机动车细分为13种车型进行核算. 其次，结合相关性分析与Lasso回归进行变量筛选，采用多种机器学习算法构建碳排放预测模型. 最后，通过SHAP值提升模型解释性并量化驱动因素贡献. 结果表明：①交通运输业产值、城市绿地面积和发明专利数量是影响碳排放的主要因素；②在各模型中，XGBoost表现最优，测试集R2为0.992，MAE和RMSE分别为7.393×104 t和8.803×104 t；③SHAP分析揭示了关键因素对碳排放的正负向作用路径. 研究从可解释性视角出发，揭示机动车主导下的城市道路交通碳排放驱动机制，可为低碳交通政策制定提供科学支撑. |
| 英文摘要 |
| Under the background of accelerated urbanization， the continuous increase in the number of motor vehicles has led to a rapid rise in road traffic carbon emissions， becoming an important factor restricting the green transformation of cities. In order to systematically identify the key driving factors of road traffic carbon emissions， a “bottom-up” approach was used to construct a road traffic carbon emission inventory for the urban agglomerations in Fujian Province from 2003 to 2022， with motor vehicles being subdivided into 13 types for accounting purposes. Secondly， correlation analysis and Lasso regression were combined for variable selection， and multiple machine learning algorithms were used to build carbon emission prediction models. Finally， SHAP values were employed to enhance model interpretability and quantify the contributions of driving factors. The results show that： ① The output value of the transportation industry， urban green space area， and the number of invention patents were the main factors affecting carbon emissions. ② Among all models， XGBoost performed the best， with the test set R2 of 0.992 and MAE and RMSE of 7.393×104 t and 8.803×104 t， respectively. ③ SHAP analysis revealed the positive and negative impact pathways of key factors on carbon emissions. This study， starting from the perspective of interpretability， reveals the driving mechanisms of urban road traffic carbon emissions dominated by motor vehicles， providing scientific support for the formulation of low-carbon transportation policies. |
