What is equitable urban forest governance? A systematic literature review
Bibliographic record
Abstract
Urban forest governance comprises the formal and informal rules, institutions, and processes that influence collective decision-making in urban forest management. As such, it shapes key processes and outcomes that are implicated in urban environmental justice, including whose priorities and values are reflected in urban forest management and how and where urban trees are distributed. However, despite its central role in determining urban forest processes and outcomes, equity within urban forest governance remains obscure. To address this, we conducted a literature review to identify how equitable urban forest governance is conceptualized and evaluated in the literature, and what gaps in knowledge remain. Our review found that while distributional justice was the prevalent framing in the literature, recommendations for collaborative governance approaches reflect a shift towards procedural and recognitional justice. Most studies, however, used a top-down approach to evaluate policy outcomes and few incorporated community experiences or involvement within governance processes, leaving the roles and experiences of community actors underexplored. Our findings suggest that the existing literature has thus far failed to explicitly interrogate procedural and recognitional justice within urban forest governance. This highlights a critical need to more clearly incorporate procedural and recognitional justice themes and approaches into future urban forest governance theory, research, and practice. Based on our review, we offer a guiding analytical framework that identifies key considerations for equitable urban forest governance. • Distributional justice was the most common way authors framed their work. • The most common methods were spatial analysis and/or interviews with leading actors. • Collaborative governance reflects a shift toward procedural and recognitional justice. • Future studies should apply community-centered procedural/recognitional approaches. • Our review informs an analytical framework for equitable urban forest governance.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".