Assessing the recreational value and preferences in the city forest of Freiburg, Germany
Bibliographic record
Abstract
Urban forests provide important cultural ecosystem services to the population of cities. In this study, we assessed the recreational value of the urban forest of the city Freiburg and the forest visitors’ preferences for recreation. Both economic and non-economic valuation techniques were employed, and potential conflicts and synergies with other forest ecosystem services considered. We conducted an on-site survey applying contingent valuation (CV) and a preference study of forest stand characteristics for recreation. The CV method showed that the estimated annual willingness-to-pay (WTP) by visitors for improving the forest for recreation was 14.42€ per person, or 2.52€ per trip, while WTP to the forest management for climate change adaptation was 17.39€ per person, or 2.74€ per trip. Extrapolated to the annual number of visits to the forest (4 million), the total recreational value can be estimated at as much as 10–11 million € per year. The preference study revealed that recreationists prefer tree stands that are diverse in species and tree size composition with medium amounts of ground vegetation and undergrowth as well as high amounts of deadwood. While deadwood was traditionally removed for forest recreation, our results indicate a different perception of deadwood. Given that structurally diverse forests are preferred and most people perceive no conflict between timber production and recreation, our study supports the current forest governance system following a multifunctional forest management approach. • On-site recreation survey in urban forest of Freiburg, Germany. • Recreationists prefer diverse forests with high amount of deadwood. • Recreation value (€10–11 M/yr) exceeds its costs and timber income. • Perceived trade-offs with other ecosystem services are small.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".