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Slope-dependent CO2 emissions in tunnels: insights from field monitoring and machine learning
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DOI：10.1016/j.apr.2026.103105.png)
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Abstract
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• Uphill driving produces about 50% CO2 in the tunnel from just 28.3% of its length • Uphill section increases VSP by 301% and CO2 emissions by 138.8% over flat section • XGBoost outperforms MLR and Random Forest in predicting instantaneous CO2 emissions • Engine operating and driving conditions dominate CO2 emission prediction • Slope-based hotspot mapping supports prioritized CO2 mitigation assessment
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