Valuing high‐seas ecosystem conservation
Bibliographic record
Abstract
The high seas provide a variety of ecosystem services that benefit society. There have, however, been few attempts to quantify the human welfare impacts of changes to the delivery of these benefits. We assessed the values of several key ecosystem service benefits derived from protecting ecosystems in the high seas of the Flemish Cap through choice experiments conducted in Canada, Norway, and Scotland. Rather than solely eliciting public willingness to pay, we also explored the determinants of variance in the estimates of willingness to pay. We aimed to determine how much respondents were willing to pay for high-seas ecosystems conservation, which factors influence individuals' willingness to pay, and whether individuals in Canada had a higher willingness to pay relative to those living in Norway and Scotland. This latter point captures distance-decay effects. On average, the public placed positive value on conserving high-seas ecosystems and on developing economic activities related to the exploitation and exploration of marine resources, despite a lack of awareness and familiarity with these environments. Distance-decay effects on willingness to pay were not clear. Scots had the highest willingness to pay and the Norwegians the lowest willingness to pay for all attributes, with the only exception being willingness to pay for a large increase in new jobs, in which case Canadians' willingness to pay was higher than Scots'. The public's willingness to pay was influenced by sociodemographic characteristics and their perceptions of high-seas ecosystems. Our results provide evidence of the impacts of high-seas governance on human welfare and that improved governance could increase the value people place on high-seas ecosystems and the services they produce.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".