Introducing the concept of environmental policy integration into the discourse on sustainable tourism: a way to improve policy-making and implementation?
Bibliographic record
Abstract
Many studies have explored how the tourism sector and tourism policies understand and relate to the concept of sustainable development. A common conclusion is that tourism concentrates on economic and social viability at the expense of environmental sustainable development. This paper considers if and how the concept of environmental policy integration (EPI) could improve sustainable tourism policy implementation. It defines EPI, and explores both the three-level (co-ordination, harmonization and prioritization) and four-level (inclusion, consistency, priorities and reporting) EPI approaches. It notes that there is both strong and weak EPI, and both political systems and policy analysis approaches. The paper then describes Norway's post-2007 adoption of sustainable tourism as a central part of its national tourism development strategy, with 10 defined principles, and suggested defined development standards, and assesses the implementation of the national strategy through the lens of the EPI concept. While there are now 18 pilot sustainable tourism destinations, with 44 criteria and 108 indicators, there remain many difficult issues to address. A series of suggestions are made, the chief of which is the need for a politically strong central authority that has been entrusted with having environmental concerns within the tourism sector as its key mandate.
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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 | 0.060 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.023 | 0.059 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".