Dual-Stage Analysis Combining Structural Equation Modeling and Machine Learning for Low-Carbon Travel Intention of Urban Residents

Guiding urban residents to travel with low carbon is an important measure to reduce carbon emissions and promote sustainable urban development. To explore the important factors affecting urban residents' low-carbon travel intention, this study proposes a composite model of urban residents' low-carbon travel intention based on the composite framework of the theory of planned behavior and value belief norm theory. A total of 398 valid pieces of data were collected in Qingdao, China. Structural equation modeling (SEM) was applied to empirically analyze the data to identify the predictors that have a significant effect on low-carbon travel intention. Then, two machine learning methods, artificial neural networks (ANN) and extreme gradient boosting (XGBoost), were used to conduct sensitivity analysis on the SEM results to identify the determinants that affect residents' low-carbon travel intention. The results showed that personal norm, attitude, subjective norm, and perceived behavioral control have a significant direct effect on residents' low-carbon travel intention. Policy factors can indirectly affect low-carbon travel intention, through mediating variables. Environmental awareness and travel time perception have a direct effect on both residents' attitude and subjective norms. In addition, the explained variance (R2) of low-carbon travel intention by ANN and XGBoost is 0.75 and 0.77, respectively. The Root Square Mean Error (RMSE) in both models are small, which verifies the effectiveness of the two machine learning methods. The results can provide a reference basis for policymakers to prompt urban sustainable development.