Public Perceptions of Urban Green Spaces: Convergences and Divergences
Bibliographic record
Abstract
In the context of rapid climate change, it is important to understand public perceptions of urban green spaces (UGSs), because green spaces have enormous potential as instruments for climate change adaptation and mitigation, and because the development of such spaces both requires and benefits from public support. This article attempts, through an extensive literature review, to understand convergences and divergences in perceptions of urban green spaces (UGSs) of city dwellers around the world and to identify gaps in the existing research. Additionally, the article explores research into the benefits associated with urban green spaces, including health (e.g., physical and mental), social (e.g., social networks and social relationships), economic (e.g., employment and income generation), and environmental (e.g., ecosystem services and biodiversity). This article further seeks to identify the extent to which urban residents have been found to perceive the roles UGSs can play in climate change adaptation and mitigation, and cultural exchange. Based on studies conducted in different countries over the past decade, this paper integrates environmental, social, cultural, and economic aspects of urban greening to provide insight into the similarities and differences in perceptions of urban green spaces and suggest approaches to building climate change resilient urban communities. This paper finds justification for: encouraging the use of integrated, multidisciplinary approaches, using innovative tools, for both the study and practical development of UGSs; conducting a greater number of studies of newer urban areas in developing countries; and considering the diverse disadvantages as well as the advantages of UGSs in order to support the continued development and expansion of this critical climate-friendly infrastructure. The more that residents' perceptions of and attitudes toward UGSs are incorporated into the design of such spaces, the more successful they will be at providing the myriad benefits they have the potential to offer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".