Water quality and property values: A meta-analysis and Canadian national benefit assessment
Bibliographic record
Abstract
This article synthesizes the water quality hedonic property literature and develops a Canadian freshwater amenity valuation model to derive spatially explicit national benefit estimates. We use over 600 meta-observations from 29 hedonic price studies to estimate price elasticities for changes in water quality measured by Secchi disk depth. We find that a 10% increase in water quality is associated with a 1.60% price increase for residential properties within 500 meters of the lakeshore. A distance-decay effect is observed, with a smaller price premium of 0.08% for properties located 500 to 1,000 meters from the lakeshore. These elasticities are integrated with spatial data to estimate the amenity values of a potential 10% water quality improvement across Canada’s freshwater lakes. The estimated local amenity benefits total $8.2 billion, with Ontario receiving 55% of the benefits and British Columbia experiencing the highest per-household gain among the provinces.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".