Influence of Planned Behavior on Residents’ Low-Carbon Travel Intention in Chengdu City, China

Main Article Content

Zhicheng Wang
Wasin Phromphitakkul


The objectives of this research are to investigate the influence factors of the correlation between planned behavior and residents' low-carbon travel intention in Chengdu city, China. The research was designed as quantitative research. Based on the previous mature scales, this paper designs the questionnaire of low-carbon travel intention. Questionnaires were distributed to urban residents of Chengdu. After the reliability and validity test, a formal questionnaire was formed and 440 questionnaires were collected. Software SPSS 26.0 was used for reliability analysis. CFA analysis was used to analyze the data. SEM is used to verify the correlation and influence path among low-carbon travel attitude, low-carbon travel subjective norms, low-carbon travel perceived behavior control and low-carbon travel intention. The results show that attitude, subjective norms and perceived behavior control have a direct influence on low-carbon travel intention. Attitude plays a mediating variable between subjective norms and intention, which plays a mediating variable between perceived behavior control and intention too, the hypothesis 1-5 were verified which proposed in this paper, according to the research conclusion, this paper puts forward the countermeasures and suggestions to promote the implementation of low-carbon travel of urban residents.


Download data is not yet available.

Article Details



Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach.Psychological Bulletin,103(3),411–423. 1037/0033-2909.103.3.411

Bentler, P. M., & Chou, C. P. (1987). Practical Issues in Structural Modeling. Sociological Methods & Research, 16(1), 78–117.

Boomsma, A. (2013). Reporting Monte Carlo Studies in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 518–540. 797839

Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling, Fourth Edition (Methodology in the Social Sciences) (Fourth ed.). The Guilford Press.

National Bureau of Statistics. (2018). Chengdu statistical yearbook. China Statistics Press, 56-57.

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99(6), 323–338.

Zhang, H., Zhang, S., & Liu, Z. (2020). Evolution and influencing factors of China’s rural population distribution patterns since 1990. PLOS ONE, 15(5), e0233637. pone.0233637