Tourism and the Environment: Trends and Patterns in the Academic Literature
Bibliographic record
Abstract
Tourism constitutes a significant activity from an environmental perspective.Environmental resources provide essential inputs for tourists, while the natural environment itself serves as a primary attraction.The rapid growth of tourism increases pressure on land, water, and biodiversity.A total of 4,996 articles retrieved from the Scopus database using the keywords "tourism" and "environment" were analyzed with the R Bibliometrix package, employing descriptive statistics (annual growth rate, average citations per document), keyword cooccurrence mapping, trend topic analysis, and international collaboration mapping.Results indicate an annual publication growth rate of 12.14%, peaking in 2024 (n = 495).The most frequent keywords were "tourism development," "ecotourism," and "sustainability."China (n ≈ 850), the United States (n ≈ 610), and Australia (n ≈ 480) were the leading contributors.Temporal trends show an evolution from early focuses on ecotourism and environmental management to recent emphasis on sustainability and environmental behavior.Research from Africa and Russia remains limited.This study distinguishes itself by being one of the few to systematically examine the entire body of tourism-environment literature in Scopus without time or geographical restrictions.These findings clarify the study's methodology and highlight key statistical results, providing readers with a clear understanding of the research's scope and significance from the outset.The study concludes by offering forward-looking recommendations for the academic community, encouraging research that explores emerging themes, integrates interdisciplinary approaches, and addresses regional imbalances.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.086 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".