Impact of social-psychological factors on low-carbon travel intention: Merging theory of planned behavior and value-belief-norm theory
Bibliographic record
Abstract
Low-carbon travel assumes paramount significance in energy conservation and the establishment of an eco-friendly transportation ecosystem. This paper endeavors to explore the relationship between low-carbon travel intention, latent psychological variables, and sociodemographic attributes, drawing insights from responses of 602 residents in Hangzhou, China by structural equation model and multi-group model. In particular, we synthesize the theory of planned behavior, value-belief-norm theory, and view of incentive, a reflection of the public support for incentive policies. Results reveals that the primary determinants influencing the low-carbon travel intention encompass the view of incentive, attitude, and subjective norms. Individuals with diverse sociodemographic attributes manifest varying sensitivities, with males and elders exhibiting heightened responsiveness to incentive, while the presence of children decrease the attraction of incentive. These findings demonstrate that low-carbon travel intention can be increased by three ways, one is by the strong attraction of incentive especially tailor incentive policy, another is by making family-friendly policies to facilitate travel for groups with children, and the last is by improving the quality of low-carbon travel services thus increasing the attitude and other determinants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".