The Economic Value of the Greater Montreal Blue Network (Quebec, Canada): A Contingent Choice Study Using Real Projects to Estimate Non-Market Aquatic Ecosystem Services Benefits
Bibliographic record
Abstract
This study used a contingent choice method to determine the economic value of improving various ecosystem services (ESs) of the Blue Network of Greater Montreal (Quebec, Canada). Three real projects were used and the evaluation focused on six ESs that are related to freshwater aquatic ecosystems: biodiversity, water quality, carbon sequestration, recreational activities, landscape aesthetics and education services. We also estimated the value associated with the superficies of restored sites. We calculated the monetary value that a household would be willing to pay for each additional qualitative or quantitative unit of different ESs, and these marginal values range from $0.11 to $15.39 per household per unit. Thus, under certain assumptions, we determined the monetary values that all Quebec households would allocate to improve each ES in Greater Montreal by one unit. The most valued ES was water quality ($13.5 million), followed by education services ($10.7 million), recreational activities ($8.9 million), landscape aesthetics ($4.1 million), biodiversity ($1.2 million), and carbon sequestration ($0.1 million). Our results ascribe monetary values to improved (or degraded) aquatic ecosystems in the Blue Network of Greater Montreal, but can also enhance economic analyses of various aquatic ecosystem restoration and management projects.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".