Willingness to pay for multiple dimensions of green open space: Applying a spatial hedonic approach
Bibliographic record
Abstract
Abstract To optimize land conservation strategies with limited resources, it is necessary to understand people's preferences and willingness to pay (WTP) for green open space. The hedonic pricing method (HPM) is widely used. However, the conventional HPM assumes no value spillovers between neighbouring properties. Here we adopt a spatial regression approach that allows us to relax the no‐spillover assumption. Through an analysis of access to different types, intensity and developability of open green space on house prices in the City of Edmonton, Canada, we illustrate how spatial HPM can be used to quantify direct and spillover values of different dimensions of open green space. We find that WTP for open space has significant spillover effects, ignoring such spillovers would under‐estimate the total value of open space protection and thus the socially optimal amount of land conservation. All else equal, people are willing to pay most for houses that are close to non‐developable green open space and woodlands. The highest price premiums are for woodlands and non‐developable green open space, followed by living near the University of Alberta farm. On the contrary, people need small compensation to live near large commercial farms. The results suggest a NIMBY attitude toward preservation of commercial agriculture.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".