Landowners' willingness to accept compensation for selling saltwater marshes to a conservation program : a multiple bounded discrete choice approach
Bibliographic record
Abstract
Saltwater marshes in the Bay of Fundy act as greenhouse gases sinks and reservoirs by fixing atmospheric carbon dioxide and storing it in carbon rich deposits. There are approximately 3,131.5 hectares of saltwater marsh in the Bay of Fundy, of which 958 ha are currently owned by the government or conservation organizations and 2,173.5 ha are owned by private landowners. A multiple bounded discrete choice contingent valuation survey was conducted to estimate landowners' willingness to accept compensation for selling their saltwater marshes to a conservation program. A multiple bounded model developed by Gregory Poe and Michael Welsh (1995) was used to analyze the responses. Mean willingness to accept compensation for one hectare of saltwater marsh was estimated to be $1,004.22, and aggregate willingness to accept compensation for the total of 2,173.5 ha of saltwater marsh owned by private landowners was approximately $2,180,000. Using these estimates and the rate of carbon dioxide-equivalent absorption of saltwater marshes, the opportunity cost of one tonne of carbon dioxide-equivalent sequestered by saltwater marshes in the Bay of Fundy was calculated to range between $16.70 and $19.95. Decision-makers can use this result for policy purposes concerning the achievement by Canada of its greenhouse gases emission reduction commitment under the Kyoto Protocol (1997).
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".