Estimating the Recreational Value of Mont-bellevue Park as a Tool for Ecosystem Service Conservation
Bibliographic record
Abstract
Urban green spaces provide essential cultural ecosystem services that enhance human well-being, social cohesion, and ecological resilience. This study quantifies the recreational and intangible values of Mont-Bellevue Park, Sherbrooke, Canada, as a nature-based solution contributing to urban sustainability. Using the Travel Cost Method (TCM) based on 1,232 summer visitors’ responses, we estimate the economic value of park use and identify socio-demographic and behavioural determinants of visitation. These data allowed for the characterisation of visitor profiles, estimation of average travel costs, and assessment of the recreational value of Mont-Bellevue Park during the summer season. Statistical analyses were performed using STATA 17 and Microsoft Excel 2021. Results from a negative binomial model reveal that travel cost negatively affects visitation, while age, education level, and specific activities such as walking and cycling significantly influence park use. The average consumer surplus per visit is CAD 21.23, amounting to CAD 449 per visitor annually, with a total surplus of approximately CAD 553,000 for all summer users. The monetary estimate of recreational welfare can serve as an environmental performance indicator to assess the social and economic impacts of municipal programs, including reforestation, urban biodiversity enhancement, or environmental education initiatives. These findings demonstrate the substantial non-market benefits provided by Mont-Bellevue Park, highlighting its role in promoting mental and physical health, social well-being, and climate resilience. Our study provides evidence to support urban planning, equitable access, and conservation policies that integrate cultural ecosystem services and nature-based solutions into sustainable city development.
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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".