Adoption of Circular Economy and Environmental Certifications: Perceptions of Tourism SMEs
Bibliographic record
Abstract
Tourism is one of the most important industries in the world. On the one hand, tourism activities provide a significant boost to many national economies but on the other hand, they severely impact the environment. Tourism SMEs are therefore needed to transform their activities from a linear economy to a circular economy (CE). However, the tourism industry has not yet shown a clear and decisive transition towards CE. There is no or very little academic discussion on why the tourism industry has not yet adopted CE and how tourism SMEs can adopt CE. In this context, we analyzed a sample of 256 tourism SMEs (hotels and accommodations, travel agencies, tour operators, and reservation service activities) based in Cyprus, France, Italy, and Spain. Our survey reveals a ruthless situation regarding the adoption of environmental certifications. There is a very low demand to adopt an environmental certification in the tourism industry. Moreover, the adoption of CE among tourism SMEs is not so high. The main factors that hinder the adoption of green or CE practices are lack of funds, lack of information about potential partners, and lack of skilled personnel. Nonetheless, many tourism SMEs perceive that CE adoption leads to various positive outcomes. Our study provides some suggestions to facilitate the transition towards CE in the tourism industry.
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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 | 0.001 | 0.001 |
| 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".