Urban green boosterism and city affordability: For whom is the ‘branded’ green city?
Bibliographic record
Abstract
Increasingly, greening in cities across the Global North is enmeshed in strategies for attracting capital investment, raising the question: for whom is the future green city? Through exploring the relationship between cities’ green boosterist rhetoric, affordability and social equity considerations within greening programmes, this paper examines the extent to which, and why, the degree of green branding – that is, urban green boosterism – predicts the variation in city affordability. We present the results of a mixed methods, macroscale analysis of the greening trajectories of 99 cities in Western Europe, the USA and Canada. Our regression analysis of green rhetoric shows a trend toward higher cost of living among cities with the longest duration and highest intensity green rhetoric. We then use qualitative findings from Nantes, France, and Austin, USA, as two cases to unpack why green boosterism correlates with lower affordability. Key factors determining the relation between urban greening and affordability include the extent of active municipal intervention, redistributional considerations and the historic importance of inclusion and equity in urban development. We conclude by considering what our results mean for the urban greening agenda in the context of an ongoing green growth imperative going forward.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".