ESTIMATING THE ECONOMIC VALUE OF IMPROVING THE ECOLOGICAL CONDITION OF THE SASKATCHEWAN RIVER DELTA ECOSYSTEM
Bibliographic record
Abstract
This research aims to quantify how much Canadians are willing to pay to improve the ecological condition of the Saskatchewan River Delta (SRD). The research develops and administers a stated preference survey that focuses on non-use values for changes in important ecological endpoints (lake sturgeon population levels, muskrat abundance, habitat in healthy ecological condition, and waterfowl population levels). A second objective of this research is to understand if values differ across provinces, age range, income levels and other socio-economic characteristics. Results suggest that Canadians are willing to pay for the improvement of the delta ecological condition. Estimated marginal willingness to pay values range from $1.55 - $2.53 for a 1% improvement in the level of the ecological attributes. Overall habitat in healthy ecological condition is the most preferred SRD ecological attribute. Taken together, the annual economic benefits to Canadian households for various SRD restoration scenarios is estimated to be $104 to $223 for 20 years. From a policy perspective, the study provides credible economic values for the benefits associated to the restoration of SRD and suggests that there can be a level of confidence that valid non-use values for river deltas in Canada do, in fact, exist and can be quantified. The results also indicate that Canadians have diverse values for SRD restoration. Some of this preference heterogeneity can be attributed to people’s income level, age category, education level, employment status, gender, and province of residence. Explained preference heterogeneity with respect to a few of socio-demographic characteristics provides insight into the social demand for the Delta restoration. Decision-makers and public managers can then use this knowledge and information on the sources of heterogeneity to improve SRD restoration.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".