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Record W4403584943 · doi:10.1080/14616688.2024.2412542

Tourism-generated energy use characteristics and sustainability transitions

2024· article· en· W4403584943 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTourism Geographies · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSustainabilityTourismEnergy (signal processing)GeographyEcotourismNature tourismEconomic geographyNatural resource economicsBusinessEnvironmental resource managementEnvironmental economicsEconomicsEcologyMathematics

Abstract

fetched live from OpenAlex

Mountain tourism destinations are characterized as significantly impacted by their remoteness, seasonal climatic variations, and fragile ecosystems. These factors greatly influence the development, distribution, and consumption of energy sources in the tourism sector. With the increasing popularity of mountain parks and protected areas as tourism destinations, it is critical to understand the interplay between tourism development (e.g. expansion of tourism facilities), patterns of energy sources (diversity and consumption levels) in the tourism sector, and overall sustainability of resources use at the destination level. Current literature reveals a dearth of research on energy issues in mountain protected areas, which is somewhat surprising as energy and resource consumption issues are becoming more important from a climate change perspective. This paper examines contemporary energy use patterns in the Sagarmatha (Mount Everest) National Park of Nepal. A mixed methods is applied to analyze development trends and transitions in energy use. Data were collected based on questionnaire surveys of tourism facilities, complemented by semi-structured interviews. The findings indicate that energy sources used in the facilities have gone through a significant change, from a firewood-dependent tourism center to increasing use of alternative sources including liquified petroleum gas (LPG) and hydroelectricity. About one-third of the facilities did not use firewood. The heterogeneous geographic distribution of the facilities affects the spatial use of energy sources. Furthermore, we argue that the coordination efforts between the national park administration and local communities, the growth of tourism, the construction of hydropower plants, and the advancement of transportation are the leading causes of the changes in energy use patterns. Knowledge of the energy use characteristics and drivers influencing the energy transitions can inform future policies that promote a low-carbon economy in mountain destinations.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.199
Teacher spread0.192 · 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