How can the tourism industry respond to the global challenges arising from climate change and environmental degradation?
Bibliographic record
Abstract
Purpose The aim of this paper is to critically review the contribution made by this theme issue in responding to the strategic question: “ How can the tourism industry respond to the global challenges arising from climate change and environmental degradation? ” Design/methodology/approach A critical content analysis of the papers selected for the theme issue was undertaken to learn from the best practices globally. This enabled the theme editors to reflect on the rationale for the theme issue question, the starting-point and the editorial process. Findings This summary paper highlights the most significant outcomes from the theme issue in terms of the contributions to knowledge and/or professional practice. It also summarizes the implications for management action and applied research arising from the outcomes and best practices based on case studies in Malaysia, Canada and New Zealand. As the main objective of the theme issue was to obtain a general overview of the relationship between tourism and climate change, five general review papers were included to strengthen the research framework. Research limitations/implications This paper outlines the challenges and new approaches in dealing with the issue of climate change. Given the economic, social and environmental significance of tourism, coverage of the climate change issue as it relates to tourism is, yet, limited. Given this scenario, the theme issue has contributed to the body of knowledge in this important field. Originality/value This paper explores the extent to which the cases presented and the review of various climate change concepts can provide guidance. The approaches and issues discussed in this theme issue could be replicated and applied in countries that are beginning to focus on climate change issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".