Managing overtourism through economic taxation: policy lessons from five countries
Bibliographic record
Abstract
Overtourism is a rapidly evolving concept relevant to sustainable tourism. While various definitions of overtourism exist, we conceive it as a form of post-mass tourism phenomenon that has transitioned from a state of ‘mass’ to a state of ‘over’, implying irreversible impacts on local populations and landscapes. As such, the focus of overtourism, as discussed in this paper, is on a destination’s supply-side attributes. Using a case study approach, we explore overtourism concerns in five countries, namely France, USA, China, Spain and Italy. The main objective of the paper is to explore the kinds of economic taxations used in managing overtourism. Study findings indicate tourist taxes and entrance fees as popular approaches employed in overtourism concerns; however, their effectiveness in solving environmental problems remains debatable. We propose a combination of destination specific economic and non-economic policies to combat overtourism including the imposing of correctives taxes and fees; sharing benefits among the locals and tourist authorities; maximising the social and economic benefits from tourism for local residents directly impacted by development; smoothing and extending visitors spread and flow; curbing fossils fuels energy consumption and regulating accommodation supplies. The long-term solution is the formulation and implementation of tourism policies that are integrated with the energy, environment and socio-economic policies at the national level. In the absence of integrative policy frameworks, many popular destinations around the world will eventually have to confront issues arising from overtourism.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".