A Multi-Stage Feature Selection and Explainable Machine Learning Framework for Forecasting Transportation CO 2 Emissions

Mohammad Ali Sahraei (), Keren Li and Qingyao Qiao ()
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Mohammad Ali Sahraei: Department of Civil Engineering, College of Engineering, University of Buraimi, Al Buraimi 512, Oman
Keren Li: School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100811, China
Qingyao Qiao: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China

Energies, 2025, vol. 18, issue 15, 1-26

Abstract: The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO 2 emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO 2 emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO 2 emissions. This framework aids policymakers in making informed decisions and setting precise investments.

Keywords: CO 2 emissions; transportation sector; machine learning; feature selection; SHAP analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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