Spatial and Non-Spatial Factors Influencing Willingness to Pay (WTP) for Urban Green Spaces (UGS): A Review
Bibliographic record
Abstract
With numerous ecosystem services of urban green spaces (UGS), contributing to sustainability and a better quality of life, UGS provision is perceived as a pivotal role in urban planning. However, concern arises as to what extent local governments have effectively provided good quality and adequate quantity of UGS for the public? Provisioning those UGS aspects has been given a low priority due to insufficient resources and the limited budget allocated by local governments. As such, maintenance and management effectiveness of UGS is detrimentally affected, resulting in disused, overused spaces and thus hot spots for crimes. Therefore, public monetary contribution via taxation is suggested as an alternative to ensuring the continuity and sustainability of UGS services. This review paper is vital to identify and showcase specific factors and mediators, influencing the willingness to pay (WTP) of residents/users for UGS services. Methodologically, after conducting Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for the purpose of article screening and selection based on the two primary databases (Google Scholar and Elsevier), this paper reviewed 18 journal articles, from the year 2013 to 2020. Along with the indirect sub-factors, namely environmental behaviour/attitude and socioeconomic profiles of users, there are three main spatial and non-spatial variables (factors) identified: (i) accessibility/proximity to the nearest UGS; (ii) quantity/adequacy of UGS; and (iii) quality of UGS within a township area, influencing satisfaction and enjoyment as well as reasons and frequency of park visiting of users (mediators), which consequently affect their WTP for UGS.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".