Are Tourists Willing to Pay for Mitigation of Abrasion? A Study on Coastal Areas in Yogyakarta, Indonesia
Bibliographic record
Abstract
"Objective � Climate change has an impact on not only environmental problems, but also on socio-economic communities. Indonesia as an archipelago country has the second longest coastline after Canada. Indonesia has a high vulnerability to climate change, especially rising sea levels which can cause abrasion. Public awareness is needed to preserve the coastal area, to prevent potential disasters that may occur. Consequently, it is important to analyze the determinant factors of tourist�s willingness to pay (�WTP�) for mitigation of abrasion and how much it would cost. This study also estimates how the relationship between a tourist�s WTP and abrasion on coasts in Yogyakarta. Methodology/Technique � A multiple linear regression method is used to estimate the determinant factors of a tourist�s WTP. The location of this study is on Kuwaru Beach and Pandansimo Beach in Bantul Regency, which have several indicators of the possibility of abrasions. Two hundred respondents were interviewed regarding the influence of socioeconomy and other factors to tourist�s WTP. Findings � That result is equivalent with the level of abrasion for each beach. Variables of education and income have significant effects on tourist� WTP at Kuwaru Beach. While in Pandansimo Beach, age and education have significant effect on WTP. The average tourist�s WTP for mitigation in Kuwaru beach and Pandansimo beach at Yogyakarta are Rp 81, 150.00 and Rp 62, 250.00. Novelty � Mitigation on abrasion calls for community awareness amongst local citizens, tourists, and people who conduct business along the beach. For the two beaches studied, the variables used � sex ratio, age, education and income � have a significant effect on a tourists� willingness to pay for abrasion mitigation."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".