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Record W4382238699 · doi:10.37741/t.71.2.2

Barriers to Sustainable Waste Management in Mountain Tourism

2023· article· en· W4382238699 on OpenAlexfundno aff
T. S. Krishnan, Kishore Kumar Gangwani, Annapureddy Rama Papi Reddy, K Kiran

Bibliographic record

VenueTourism · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersUniversity of WaterlooAuckland University of Technology, New Zealand
KeywordsTourismBusinessSustainabilityCertificationEnvironmental planningEnforcementGovernment (linguistics)Environmental resource managementSustainable tourismEnvironmental economicsNatural resource economicsGeographyEnvironmental scienceEconomicsPolitical science

Abstract

fetched live from OpenAlex

Goal 15 of the Sustainable Development Goals calls for efforts to protect fragile mountain ecosystems. Waste generated due to mountain tourism leads to environmental degradation, biodiversity loss, and poses a significant challenge to achieving this goal. Mountains which are characterized by uninhabitable terrain and remoteness, coupled with current tourism practices compound this challenge. The paper resolves this challenge by understanding barriers to sustainably manage waste using th Classical DEMATEL method. Based on data from 36 experts in India’s mountain tourism industry, barriers to sustainable management of non-biodegradable waste are analyzed. Results suggest that enforcement of regulations , waste collection infrastructure , and lack of transportation for waste transfer are the most prominent barriers that can be mitigated by collectively leveraging four tangible barriers: tourists’ motivation or achievement mentality , local government’s initiative , economic value of waste , and tourists’ lack of environmental awareness . Based on this, a policy intervention mandating certification standards for tourists is suggested before they embark on mountain tourism.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.018
GPT teacher head0.324
Teacher spread0.306 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations11
Published2023
Admission routes1
Has abstractyes

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