What does a just transition mean for urban biodiversity? Insights from three cities globally
Bibliographic record
Abstract
• Just transitions ideas developed for climate and energy, but less for biodiversity. • Evaluation of just transitions in biodiversity for 3 cities globally. • Just transitions for urban biodiversity encompass wellbeing as well as livelihoods. • Just transitions emphasises processes, and winners and losers in urban biodiversity. • Embedding biodiversity in urban just transitions emphasises necessity of ecosystems. Just transitions – responses to environmental change that minimise negative impacts on the most affected people and places, while ensuring nobody is left behind – are gaining scholarly and policy significance in areas beyond their original focus on carbon-intensive jobs and sectors. Yet attention to what a just transition means for biodiversity, as another aspect of the global environmental crisis, remains limited. Given the critical role that biodiversity plays in supporting livelihoods and wellbeing, this is a notable gap. This paper assesses what a just transition means for biodiversity, focusing on urban environments as the spaces in which many people encounter biodiversity globally. We undertake interview research across three case study cities representing different geopolitical and environmental contexts: Bristol (UK); Yubari (Japan); and Cape Town (South Africa) and ask two questions: what does biodiversity tell us about the concept of just transitions in the lived environment; and what are the consequences of considering just transitions in the context of biodiversity in the lived urban environment? Based on our findings, we set out six principles for a just transition in relation to urban biodiversity, as areas for further empirical enquiry: a shared sense of what a just transition and biodiversity mean in the local context; diverse social and ecological knowledge systems informing decision-making; integration and cohesion across policies; inclusive, meaningful and early engagement; supporting communities during and after implementation; and measures for assessing the effectiveness of outcomes from an ecological and a social perspective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".