Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment

The transportation industry contributes significantly to climate change through carbon dioxide ( $$\hbox {CO}_{2}$$ ) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting $$\hbox {CO}_{2}$$ emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government’s official open data portal, we explored the impact of various vehicle attributes on $$\hbox {CO}_{2}$$ emissions. Our analysis reveals that not only do high-performance engines emit more pollutants, but fuel consumption under both city and highway conditions also contributes significantly to higher emissions. We identified skewed distributions in the…