Multi-level barriers and opportunities for urban greening and depaving initiatives as climate change adaptation measures: Quebec City case study
Bibliographic record
Abstract
Urban greening and depaving initiatives are increasingly recognized as climate change adaptation strategies in cities worldwide. However, several individual and institutional barriers, including social acceptability, can hinder the successful implementation of these initiatives. This study aims to understand the decision-making processes and citizen perceptions associated with urban greening and depaving initiatives in Quebec City. Using a mixed-methods approach, we conducted detailed interviews with 18 stakeholders and a questionnaire-based survey of 770 residents in the city. Our findings show that these initiatives could be characterized into five project types, comprising community-supported projects and projects led by different city departments, including engineering, urban planning, and mobility, with smaller contributions from departments such as recreation park services. While a horizontal structure between these departments fostered collaboration, a top-down approach to the decision-making process limited the implementation of such initiatives. In addition, economic, technical, and organizational barriers were limiting factors, particularly the perceived lack of citizen support for these initiatives. Nevertheless, the survey results revealed strong support from citizens, with more than 80% of participants expressing moderate to high levels of support. In addition, while nearly half of the survey participants (47%) identified private car use as their preferred mode of transport, the participants showed a willingness to reduce parking spaces to support depaving initiatives. To overcome the barriers to and promote the successful implementation of urban greening and depaving initiatives, greater collaboration between decisionmakers, professionals, and the community in Quebec City is needed. Our results have significant implications for advancing such initiatives beyond Quebec City. By examining stakeholders’ perceptions, our findings can inform decision-making processes and promote the adoption of public policies to support these initiatives in city planning agendas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".