The economic value of improving the ecological condition of the Saskatchewan River Delta, Canada
Bibliographic record
Abstract
River deltas around the world have experienced ecological decline due to upstream development and human activities. However, assessing the monetized benefits of ecosystem restoration is challenging due to the less tangible nature of many values that people hold for these natural areas. This paper quantifies the non-use values for river delta ecological conservation using a stated preference survey. The empirical application is the Saskatchewan River Delta in Canada where we develop and administer a national survey to elicit people’s preferences for restoration scenarios with changing fish and wildlife population levels and changes in the extent of habitat in good ecological condition. We find that Canadians are willing to pay for river delta restoration improvements, and preferences for restoration options are heterogeneous across the population. Models with nonlinear attribute levels fit the choice data better than linear attribute specifications suggesting the presence of diminishing marginal willingness to pay as ecological improvements increase. The annual monetized benefits for Saskatchewan River Delta future scenarios are estimated to be $79 to $223 per household depending on the level of restoration efforts. This study contributes to the empirical evidence that the benefits of nature accrue to people that live far away from natural areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".