Spatial clustering of willingness to pay for ecosystem services
Bibliographic record
Abstract
Abstract Variations of willingness to pay (WTP) in geographical space have been characterised by the presence of localised patches of higher and lower values. However, to date, spatial valuation studies have not explored whether the distribution of hot (cold) spots of WTP is particular to each environmental good or if it follows similar patterns to other, comparable, environmental goods. We address this question by contrasting the spatial patterns of hot (cold) clusters of WTP for improvements in several ecosystem services. We geocoded individual‐specific WTP estimates derived from a discrete choice experiment exploring preferences for ecosystem service improvements for three different catchment areas in Scotland comprising urban, agricultural, riverine and estuarine ecosystems. The local Moran's I statistic was used to find statistically significant local clusters and identify hot spots and cold spots. Finally, Multi‐type Ripley's K and L functions were used to contrast the spatial patterns of local clusters of WTP among ecosystem services, and across case studies. Our results show that hotspots of WTP for environmental improvements tend to occur close to each other in space, regardless of the ecosystem service or the area under consideration. Our findings suggest that households sort themselves according to their preferences for estuarine ecosystem services.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".