Utilizing Green Tourism Marketing as an Initial Step Towards Achieving Sustainable Tourism Development.
Bibliographic record
Abstract
The objective of the research is to integrate the environmental aspect into the strategies of tourism institutions, recognizing the necessity for governments to implement a set of measures aimed at fostering tourism activity while fostering environmental consciousness. This involves employing the concept of green tourism marketing and prioritizing the environmental dimension within production and marketing strategies. The significance of this research lies in introducing green tourism marketing and delineating its dimensions, alongside elucidating sustainable tourism development goals and the primary challenges it encounters. Furthermore, the research evaluates the role of green tourism marketing in supporting and realizing the objectives of sustainable tourism development. To address these issues, the study is divided into several sections, including the conceptual framework of green tourism marketing, sustainable tourism development goals and challenges. The research employs inferential methods to investigate the relationship between green tourism marketing and sustainable tourism development. Green tourism marketing is spurred by environmental imbalances, prompting a shift in human behavior towards more ecoconscious practices. Utilizing production methods that minimize environmental impact through the use of sustainable or recycled materials and energy-efficient processes not only reduces costs but also enhances profitability. Moreover, green tourism marketing plays a vital role in safeguarding tourists and the environments they inhabit. Its significance lies in its influence on the reputation and competitiveness of tourism organizations, as well as its tangible benefits in terms of protecting the health and environment of tourists.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".