Explainable Machine Learning Prediction of Vehicle CO2 Emissions for Sustainable Energy and Transport

Dong Yuan, Long Tang, Xueyuan Yang, Fanqin Xu, Kailong Liu

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 CO2 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 CO2 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.