Economic evaluation of alternative urban park designs that conserve irrigation water
Bibliographic record
Abstract
Abstract Increasing the area of drought‐tolerant native vegetation in urban parks is a potential strategy to adapt to growing water scarcity under climate change. With a case study in Perth, Australia, we undertake benefit–cost analyses to understand the potential impacts of modifying urban park landscape designs away from conventions dominated by watered grass towards alternatives with more native vegetation. Considering the costs of establishing and maintaining alternative designs alone, we find that local governments can save money by reducing the extent of watered grass cover in parks. Incorporating nonmarket benefits into the analyses provides evidence of positive community net benefits from making changes to conventional park designs. We show that an alternative park design featuring a mix of 60% native vegetation and 40% watered grass delivers the highest net benefits for both new park development and retrofits to existing parks. These findings suggest it is time to rethink urban park design norms in Australia to better align landscaping choices with community preferences under a changing climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".