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Identifying Influence Mechanisms of Low-Carbon Travel Intention Through the Integration of Built Environment and Policy Perceptions: A Case Study in Shanghai, China

Author

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  • Yingjie Sheng

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Anning Ni

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Lijie Liu

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Linjie Gao

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yi Zhang

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yutong Zhu

    (School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Promoting low-carbon travel modes is crucial for China’s transportation sector to achieve the dual carbon goals. When exploring the mechanisms behind individuals’ travel decisions, the relationships between factors such as the built environment and transportation policies are often derived from prior experience or subjective judgment, rather than being grounded in a solid theoretical foundation. In this paper, we build on and integrate the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) by introducing built environment perception (BEP), encouraging policy perception (EPP), and restrictive policy perception (RPP) as either perceived ease of use (PEOU) or perceived usefulness (PU). The integration aims to explain how the latent variables in TPB and TAM jointly affect low-carbon travel intention. We conduct a traveler survey in Shanghai, China to obtain the data and employ a structural equation modeling (SEM) approach to characterize the latent mechanisms. The SEM results show that traveler attitude is the most critical variable in shaping low-carbon travel intentions. Perceived ease of use has a significant positive effect on perceived usefulness, and both constructs directly or indirectly influence attitude. As for transportation policies, encouraging policies are more effective in fostering voluntary low-carbon travel intentions than restrictive ones. Considering the heterogeneity of the traveling population, differentiated policy recommendations are proposed based on machine learning modeling and SHapley Additive exPlanations (SHAP) analysis, offering theoretical support for promoting low-carbon travel strategies.

Suggested Citation

  • Yingjie Sheng & Anning Ni & Lijie Liu & Linjie Gao & Yi Zhang & Yutong Zhu, 2025. "Identifying Influence Mechanisms of Low-Carbon Travel Intention Through the Integration of Built Environment and Policy Perceptions: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 17(17), pages 1-30, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7647-:d:1731856
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