Greening streets, gaining insights: Unpacking resident perceptions of urban greening
Bibliographic record
Abstract
As the world becomes more urbanized, the need for managing urban stormwater runoff has increased. Cities are increasingly adopting green infrastructure (GI), such as rain gardens and street trees, to capture and treat stormwater on-site more cost-effectively than traditional gray infrastructure, such as sewers and pipes, which are designed to convey stormwater to nearby waterbodies. In addition to managing stormwater, GI offers co-benefits, including improved air quality, reduced heat, and enhanced neighborhood aesthetics. Streets are often a primary target for GI implementation, as they comprise a large portion of impervious surfaces in urban areas. Previous studies suggest that increasing public awareness of GI benefits can boost support for such projects, encourage participation, and even influence behavior change. However, misunderstanding or a lack of awareness about GI can create additional barriers, amplifying public concerns and hindering implementation progress. Addressing these misconceptions and understanding public concerns are critical steps in overcoming potential obstacles to support. This study synthesizes literature on anticipated co-benefits from GI and investigates general perceptions toward GI on streets and factors in influencing perception, the specific benefits and concerns that are valued, as well as how these relate to familiarity with GI or environmental awareness. We conducted resident surveys in two cities, New York City and Philadelphia, that have implemented GI on streets since around the 2010s. The results suggest that understanding the purpose of GI can increase both awareness of its benefits and doubts about its potential concerns. The findings imply that effective community communication, which clearly explains the benefits provided and addresses concerns, can better foster public support for GI. This study provides valuable insights for urban planners and GI practitioners, offering a more nuanced understanding to guide the development of targeted community engagement and education strategies. • This study synthesizes and expands on literature on anticipated co-benefits of GI. • Separate surveys in New York City and Philadelphia revealed parallel GI perceptions. • An analysis based on participant characteristics reveal more nuance of perceptions. • This study supports urban greening and NbS efforts through its empirical research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".