Understanding the determinants of biodiversity non-use values in the context of climate change: Stated preferences for the Hawaiian coral reefs
Bibliographic record
Abstract
Global climate change is leading to rapid deteriorations of the health and productivity of coral reefs. There is limited research on the associated human welfare implications, particularly in terms of the non-use values that people hold for coral reefs. We examine climate related changes in non-use values of coral health, coral cover, water clarity, fish numbers, fish species diversity and presence of turtles. Using a discrete choice experiment conducted among 1,369 Hawaiian and US mainland residents, we find that climate change induced declines in coral cover and fish numbers result in large welfare losses; whereas, declines in coral health and fish species diversity lead to moderate welfare losses. Deterioration in water clarity results in large welfare losses for US mainland residents but relatively smaller losses for Hawaiian residents. On aggregate, differences in welfare estimates for the US mainland and Hawaii sample are minor. However, we find significant differences in the underlying determinants of willingness-to-pay for partial climate change mitigation including income and beliefs in the need to mitigate climate change. The paper concludes with some recommendations for policy on the basis of these findings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".