Future-proofing nature-based tourism in Canada: a horizon scan of emerging challenges
Bibliographic record
Abstract
Global climate change, biodiversity loss, health crises, and economic instability converge to form a polycrisis that challenges the sustainable planning, management, and operations of parks, protected, and conserved areas (PPCAs) for biodiversity conservation and nature-based tourism (NBT). Utilising a horizon scan methodology for the first time in the tourism field, this paper engaged experts across Canada to identify and critically examine the opportunities and risks associated with emerging challenges anticipated to affect sustainable NBT in PPCAs over the next three decades. Using a modified Delphi technique in three phases, beginning with challenge identification followed by two rounds of scoring to prioritise and rank challenges based on impact and likelihood, eight key challenge themes were identified: (1) demographic change, (2) climate change risk and adaptation, (3) low carbon transition, (4) workforce sustainability, (5) sustainable financing, (6) equitable and effective governance, (7) balancing conservation with visitation, and (8) truth and reconciliation. These insights are critical for practitioners, policymakers, and tourism stakeholders to adapt planning and management efforts, addressing interconnected challenges and stimulating research to enhance the sustainability and resilience of the NBT sector in Canada amid the polycrisis.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".