Transitioning from onsite to virtual tourism using metaverse: An EcoTech framework for sustainable tourism development
Bibliographic record
Abstract
This study investigates how Virtual Reality (VR) tourism can help combat environmental degradation in Pakistan, including its potential to promote socio-economic growth in the country. Given the ever-increasing concerns governing the ecological degradation posed by physical tourism in a global landscape, VR tourism offers a sustainable alternative with much lesser impact on the environment and natural resources than on-site tourism. This study thus offers a conceptually-driven yet theoretically-supported and literature-backed EcoTech framework that integrates the external & internal factors affecting the adoption and effectiveness of virtual tourism activities & initiatives. To this end, the influence of external factors such as environmental concerns, environmental responsibility, pro-environmental behavior, eco-guilt and ecological impact on travel was theoretically evaluated to understand the individuals’ attitudes toward embracing virtual tourism. Moreover, the influence of age, gender, income, educational background and household size of the individuals were also considered as key control factors that could potentially affect their visit intentions from socio-economic & demographic viewpoints. Accordingly, this study advocates ‘Willingness-to-Forgo-Pleasure-of-In-situ-Tourism’ as a key enabler for promoting eco-friendly tourism and environmental sustainability as a whole. By and large, the suggested EcoTech framework offers some policy insights and guiding mechanisms for tourism industry stakeholders such as tour operators, government tourism departments including the eco-conscious travellers & tourists looking for cost-effective, environment-friendly and resource-efficient alternatives to on-site tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".