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

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

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<sub>2</sub> emissions. This research aims to establish an...

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Bibliographic Details
Main Authors: Mohammad Ali Sahraei, Keren Li, Qingyao Qiao
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Energies
Subjects:
CO<sub>2</sub> emissions
transportation sector
machine learning
feature selection
SHAP analysis
Online Access:https://www.mdpi.com/1996-1073/18/15/4184
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Summary: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<sub>2</sub> emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO<sub>2</sub> 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<sub>2</sub> emissions. This framework aids policymakers in making informed decisions and setting precise investments.
ISSN:1996-1073

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