Information Management of Low-Carbon Travel Behavior

Abstract

Low-carbon travel is widely recognized as an important strategy for reducing energy consumption, mitigating pollution emissions, and alleviating traffic congestion. This study utilizes a sample of 2167 residents from four Chinese cities and employs the Theory of Planned Behavior (TPB) in conjunction with Structural Equation Modeling (SEM) to obtain more information about the determinants of Low-carbon travel behavior (LTB). Key findings include: (1) The extended TPB proved to be highly applicable to the analysis of LTB, with perceived behavioral control (PBC) exhibiting the most influential factor, and the relationship between PBC and LTB is partially mediated. (2) Gender, education, and commuting distance positively affect LTB, while income, private car ownership, and possessing a driver's license demonstrate significant negative effects. (3) Concern for environmental quality significantly enhances LTB. In contrast, perceived traffic congestion significantly reduces LTB. Based on the empirical results, targeted and implementable policy recommendations are proposed.