The Economic Value of Health Benefits Associated with Urban Park Investment
Bibliographic record
Abstract
The allocation of resources towards the development and enhancement of urban parks offers an effective strategy for promoting and improving the health and well-being of urban populations. Investments in urban parks can result in a multitude of health benefits. The increased usage of greenspace by park users has been linked to positive physical and mental health outcomes. Additionally, the expansion of greenspace in urban areas can mitigate harmful impacts from air pollutants, heat, noise, and climate-related health risks. While the health benefits attributed to urban parks and greenspaces are well documented, few studies have measured the economic value of these benefits. This study applied a novel ecohealth economic valuation framework to quantify and estimate the potential economic value of health benefits attributed to the development of a proposed park in the downtown core of Peterborough, Canada. The results indicated that development of the small urban park will result in annual benefits of CAD 133,000 per year, including CAD 109,877 in the avoided economic burden of physical inactivity, CAD 23,084 in health savings associated with improved mental health, and CAD 127 in health savings attributed to better air quality. When including the economic value of higher life satisfaction, the economic benefit is more than CAD 4 million per year. The study demonstrates the value of developing and enhancing urban parks as a strategy to improve population health and well-being, and as a means of cost savings to the medical system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".