Sustainable tourism modeling: Pricing decisions and evolutionarily stable strategies for competitive tour operators
Bibliographic record
Abstract
In this article, we investigate two competitive tour operators (TOs) who choose between traditional tourism strategy (strategy T) and green tourism innovation strategy (strategy G). Our article attempts to address the following important issues using evolutionary game models: when would TOs facing environment-friendly tourists adopt the strategy G? How do TOs set product prices under different strategy combinations? How can the government effectively motivate TOs to pursue green tourism? Our research results show that a green tourism innovation pioneer could monopolize the market under certain conditions. Furthermore, when the environmental preference of tourists is sufficiently low, no TOs would adopt the strategy G; when it is moderate, only the TO with cost advantage (stronger TO) would adopt the strategy G; when it is sufficiently high, both TOs would select the strategy G. Our research also demonstrates that the stronger TO implements the strategy G mostly independent of the rival’s decisions, but the opposite is true for the TO with cost disadvantage (weaker TO). We further investigate potential government subsidies that can motivate TOs to carry out green tourism simultaneously. Our results suggest that to be more effective, the government first offer the green subsidy to highly competitive tourism locations and/or more innovative TOs.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".