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Record W2904100113

Climate Change, Seasonality and Visitation to Canada’s National Parks

2006· article· en· W2904100113 on OpenAlexaboutno aff
Brenda Jones, Daniel Scott

Bibliographic record

VenueJournal of Park and Recreation Administration · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationTourismNational parkClimate changeGeographyEnvironmental resource managementEnvironmental protectionEcologyEnvironmental scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

For over a decade, the scientific community and park professionals have recognized that climate change will have critical implications for park conservation policy and management (International Union for Conservation of Nature, 1993; Welch, 2005). The implications of global climate change for nature-based park tourism has only recently begun to be assessed. Nature-based tourism and related outdoor recreation are strongly influenced by climate. Climate can affect the physical resources that define the foundation of many tourism and recreation activities as well as the length and quality of tourism and recreation seasons. Any changes in the length and quality of tourism and recreation operating seasons brought about by changes in the climate would have considerable implications for visitation and related aspects of park management Canada’s national parks are a major resource for nature-based tourism, with approximately 16 million person visits in 2003. Visitation to Canada’s national parks is highly seasonal and greatly affected by the country’s regional climates. This paper examined the potential impact of climate change on the annual number of visitors and the seasonal pattern of visitation in Canada’s national parks. Multivariate regression analysis using four climate variables and monthly visitation data for 1996 to 2003 was used to develop a monthly climate-visitation model for 15 highvisitation parks. Each park-specific model was then run with two climate change scenarios to assess potential changes in park visitation under a range of climatic conditions projected for the 2020s, 2050s and 2080s. Results indicate that Canada’s national parks could experience an increase in visitors under climate change due to a lengthened and improved warm-weather tourism season. In the 2020s, overall visitation levels were projected to increase 6% to 8%, with a number of parks projected to experience larger increases (+12% to 30%). The largest increases in visitation occur during the spring and fall months. Visitation was projected to increase between 9% and 29% system-wide in the 2050s and between 10% and 41% in the 2080s. Perhaps more importantly, the affects of climate change will be combined with other factors influencing park visitation in the future. When the potential affects of climate change were combined with those of demographic change (population, ageing and ethnic diversity) total person visits for the mid-2020s were projected to be at least three times greater (+20% to 23%) than that projected under climate change alone. If these findings are indicative of the impacts of climate change on future visitation, the implications for tourism and park management are substantive. Management implications of the findings include a probable need for more intensive visitor management strategies, especially in parks where visitor increases could significantly stress natural resources or lead to the escalation of conflicts among user groups. Although the primary focus of climate change adaptation within Parks Canada has thus far been the maintenance of ecological integrity, this research suggests that changes in visitor management strategies will be a required component in the development of Parks Canada’s climate change adaptation framework.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.350
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations104
Published2006
Admission routes1
Has abstractyes

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