Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models
Jianxin Tang, Rizhao Gong, Huilin Wang, Yuxi Liu
Abstract
Abstract Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network, and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of ‘rapid increase—fluctuating decrease—continuous increase’ from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of renewable energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.