Estimating willingness to pay for the preservation of the Alfred bog wetland in Ontario : a multiple bounded discrete choice approach
Bibliographic record
Abstract
The Alfred Bog wetland is the largest high quality bog ecosystem and one of the most important natural areas in southern Ontario. The 4,200 hectare bog provides habitat to a large number of rare and endangered species and plays an integral role as a natural water filter. This study used the contingent valuation survey method to estimate respondents' willingness to pay for the preservation of the Alfred Bog wetland, which is threatened by the competing activities of drainage, burning, and the extraction of peat. A multiple bounded discrete choice model was used to analyze the survey results. Results indicated that respondents were willing to pay an average of $79.22, in the form of a one-time voluntary contribution to a hypothetical preservation fund, for the preservation of the Alfred Bog wetland. Conservation club membership, visits to the bog, donations to wetland preservation programs, attitudes, distance from the bog, household income, and education level were found to be important predictors of willingness to pay. Aggregate willingness to pay to preserve the bog was estimated to be between $2.2 million to $663,000 depending upon the inclusion or exclusion of protest bids. The survey results suggested that most of this value was nonuse value attributed to option, bequest, and altruistic values. Thus, the failure of policy makers and resource managers to consider nonuse values in decision making processes can understate the value of preserving the Alfred Bog.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".