Explainable Machine Learning Prediction of Vehicle CO 2 Emissions for Sustainable Energy and Transport
Dong Yuan,
Long Tang,
Xueyuan Yang,
Fanqin Xu () and
Kailong Liu ()
Additional contact information
Dong Yuan: School of Artificial Intelligence, Wenshan University, Wenshan 663099, China
Long Tang: School of Artificial Intelligence, Wenshan University, Wenshan 663099, China
Xueyuan Yang: School of Artificial Intelligence, Wenshan University, Wenshan 663099, China
Fanqin Xu: Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK
Kailong Liu: Shenzhen Research Institute of Shandong University, Shenzhen 518000, China
Energies, 2025, vol. 18, issue 20, 1-21
Abstract:
Transport is a major contributor to anthropogenic greenhouse gases, making accurate assessment of vehicle emissions essential for climate change mitigation. This study develops a comparative machine learning framework to predict CO 2 emissions from internal combustion engines (ICEs) and hybrid electric vehicles (HEVs), using data from the UK Vehicle Certification Agency. In addition to standard technical variables, the study considers noise level, a factor seldom integrated into emission modeling, reflecting potential interactions between acoustic conditions and vehicular emission patterns. Explainable machine learning techniques, including accumulated local effects, are employed to clarify how engine capacity, fuel consumption and pollutant indicators influence CO 2 outputs under different driving conditions. Results show that medium- and high-speed driving dominate ICE emissions, whereas HEVs maintain lower emissions except under high power demand. By combining predictive modeling with interpretability, the study advances environmental informatics and provides actionable insights for low-carbon vehicle design, emission standards and sustainable transportation policies aligned with global climate goals.
Keywords: vehicle emissions; hybrid electric vehicles (HEVs); explainable AI; climate change mitigation; sustainable transport (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
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1996-1073/18/20/5408/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/20/5408/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:20:p:5408-:d:1770922
Access Statistics for this article
Energies is currently edited by Ms. Cassie Shen
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().