What does neighbourhood climate action look like? A scoping literature review
Bibliographic record
Abstract
Abstract Cities are recognized as an important scale for framing and implementing plans and policies for action on climate change. Within the structure of cities, it is in urban neighbourhoods that climate action becomes tangible and has the potential to engage communities. Despite its importance, scholarly literature has played limited attention to the scale of the neighbourhood as a site for locating climate action. The objective of our paper is to provide an overview of the role of neighbourhoods in leading bottom-up climate action and its implications for urban planning based on a qualitative scoping review. Our findings indicate that neighbourhoods are conceptualized as a physically bounded scale for climate action as well as a web of social networks and relationships enabling this action. Neighbourhood climate action aims to achieve neighbourhood scale sustainability and resilience by engaging with residents, municipalities, local academic institutions, neighbourhood associations and non-governmental agencies. Scholars engage with a wide range of concepts like place-based attachment and social mobilization as well as established practice-oriented tools in defining and measuring neighbourhood climate action. However, the neighbourhood scale struggles with limited resources and power in creating sustained climate action as well as in engaging with and addressing socio-economically marginalized communities.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".