Full
Outline
This study focuses on East China, establishing a 15-indicator system to investigate the driving mechanisms of traffic-related CO2 emissions and their contributions to ozone (O3) concentrations. First, the Lasso regression model is employed to screen key variables. Then, feature importance is assessed using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, with the SHAP method used to visualize the marginal contributions of each factor. A systematic framework for identifying and ranking driving factors is thus constructed. Results indicate that traffic-related CO2 emissions in East China have shown a continuous upward trend, with Shanghai, Jiangsu, and Shandong together accounting for 62.18% of total emissions by 2022. High O3 concentration zones are primarily located in the northeastern subregion, with Anhui Province experiencing a remarkable increase of 92.25%. In terms of model performance, the RF model outperforms XGBoost in both explanatory power and stability, achieving R2 values above 0.8 for both CO2 and O3, with MSE values of 0.001 and MAE values below 0.1. SHAP analysis reveals that the main drivers of CO2 emissions include population density, freight turnover, tertiary industry output, and urbanization level; while O3 concentrations are primarily influenced by total population, transport sector output, the number of freight vehicles, and urban road length.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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