Outlooks and Challenges for Urban Green Space Development: A Review Case Study in Thailand
Bibliographic record
Abstract
Even though prior studies have provided ample proof of the benefits of urban green space, Thailand's development and exploitation of urban green space remain insufficiently efficient. To assess the prospects for urban green space development in Thailand, our review study highlighted gaps and challenges related to these areas. This review study discussed the rationale for urban green space, its definitions, as well as its benefits and co-benefits (i.e., economic, social, health, and environmental). The review research additionally discussed Thailand's current urban green space issues, associated challenges (such as difficulties valuing and using urban green space, budgetary limitations, low priority for urban green space, and poor urban green space standards), and short- and long-term green space goals. Moreover, this study reviewed the urban green space assessment criteria (e.g., quality, potential urban green spaces, planning and strategy, and location selection), tools, and intriguing green space policies and practice approaches (e.g., planting and protecting trees, increasing public parks, and city taxpayers) from other previous studies and developed countries whose cities rank in the top 10 worldwide in terms of the ratio of green space to population density, for example, the US, Singapore, Germany, Switzerland, Canada, and the Netherlands. There have also been interesting platforms and technology introductions for developing and managing urban green spaces. Finally, the review study proposed guidelines for green space development that may be beneficial for Thai policymakers to improve green space based on lessons learned from other developed countries, such as being more accessible, a proper size, an appropriate distance from neighborhood residents, having suitable facilities and equipment for the users, maintaining the beauty and cleanliness, having recreational activities, tax incentives, and advanced technology platforms. Additional research is required to examine the damage costs associated with urban green space, policy, and cost-benefit analysis to make it more practicable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".