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Record W2002724717 · doi:10.1108/17554210910980594

Climate change implications for water resource management in Caribbean tourism

2009· article· en· W2002724717 on OpenAlexaff
Kwame Emmanuel, Balfour Spence

Bibliographic record

VenueWorldwide Hospitality and Tourism Themes · 2009
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsBrandon University
Fundersnot available
KeywordsTourismWater scarcityClimate changeLivelihoodPer capitaResource (disambiguation)Water useWater resourcesNatural resource economicsSmall Island Developing StatesWater supplyGeographyWater resource managementBusinessEnvironmental scienceEconomicsAgricultureEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the climate change implications for both rainfall and saline intrusion in ground water, which could directly threaten both the tourism industry and other local livelihoods in the Caribbean. Water shortages will be particularly critical in the locations that are already water‐stressed; at or near the limits of their available supplies. Design/methodology/approach The paper focuses on Barbados as the island exhibits four critical factors that make it particularly sensitive and potentially vulnerable to water shortages. Barbados is relatively small and flat, and has limited water flow. Second, it is the most densely populated country in the Caribbean. Third, the economy is primarily driven by tourism, and has prospered as a result; Fourth, Barbados is characterized as “absolute water scarce” on the Falkenmark scale because of a per capita availability of freshwater per year of less than 500 cubic meters. Findings The paper observes that Barbados has a water availability of just 306 cubic metres per capita per year, which makes Barbados the 15th most water‐scarce nation in the world. Thus, Barbados is critically dependent on a water‐intensive industry, has limited options to expand the supply of the key resource, and now finds that the availability of this key resource might decline in future as a result of climate change. Originality/value The paper provides data, case studies and analysis to demonstrate the significant threat to tourism from water shortages relating to climate change.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 designOther design
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

Citations23
Published2009
Admission routes1
Has abstractyes

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