Unleashing eco-conscious travel: exploring the factors influencing green travel behavior in urban communities
Bibliographic record
Abstract
In this study we investigate the psychological mechanisms through which eco-guilt influences individuals’ green travel behavior (GTB), focusing on the mediating role of environmental self-identity and the moderating effect of perceived social visibility. Drawing on self-concept theory, the research was conducted across major urban centers in Pakistan, using a structured survey administered to a sample of 317 respondents. Structural equation modeling (SEM) and PROCESS macro analysis were employed to test the hypothesized relationships. Results indicate that eco-guilt significantly enhances environmental self-identity, which in turn positively predicts GTB. Furthermore, perceived social visibility strengthens the link between environmental self-identity and GTB, confirming a moderated mediation model. These findings extend existing pro-environmental behavior literature by highlighting the interplay between internal emotional cues and social observation in shaping sustainable urban travel choices. The research holds particular relevance for developing economies and informs policy strategies aligned with several Sustainable Development Goals (SDGs), including SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). By integrating emotional, identity-driven, and social-contextual factors, this study offers a nuanced framework for promoting sustainable mobility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".