MSCL-Attention: A Multi-Scale Convolutional Long Short-Term Memory (LSTM) Attention Network for Predicting CO 2 Emissions from Vehicles

The transportation industry is one of the major sources of energy consumption and CO 2 emissions, and these emissions have been increasing year by year. Vehicle exhaust emissions have had serious impacts on air quality and global climate change, with CO 2 emissions being one of the primary causes of global warming. In order to accurately predict the CO 2 emission level of automobiles, an MSCL-Attention model based on a multi-scale convolutional neural network, long short-term memory network and multi-head self-attention mechanism is proposed in this study. By combining multi-scale feature extraction, temporal sequence dependency processing, and the self-attention mechanism, the model enhances the prediction accuracy and robustness. In our experiments, the MSCL-Attention model is benchmarked against the latest state-of-the-art models in the field. The results indicate that the MSCL-Attention model demonstrates superior performance in the task of CO 2 emission prediction, surpassing the leading models currently available. This study provides a new method for predicting vehicle exhaust emissions, with significant application prospects, and is expected to contribute to reducing global vehicle emissions, improving air quality, and addressing climate change.