Urban green grabbing: Residential real estate developers discourse and practice in gentrifying Global North neighborhoods
Bibliographic record
Abstract
In the movement towards building greener and more sustainable cities, real estate developers are increasingly embracing not only green building construction but broader strategies and action related to urban greening. To date, their motivations and role in this broader urban greening dynamic remains underexplored, yet essential to dissect how greening is sustained and real estate development legitimized in revitalizing neighborhoods. With an eye to better understand green urban capitalist development processes underway amidst financialized nature and urban growth, and the equity impacts they entail, we explore residential real estate developers urban greening discourses and practices. Through a novel dataset of 42 interviews with private and non-profit residential real estate developers in 15 mid-sized American, Western European and Canadian cities, we uncover three differentiated but interconnected discourses around (i) financial benefits, (ii) consumer- or investor-driven demand and (iii) social dimensions behind developers’ interest in urban greening. We argue that developers embark on urban green grabbing through “green” discursive and material value appropriation and rent extraction strategies. Urban green grabbing is conceptually useful in depicting who benefits and how/when developers extract additional rent, surplus value, social capital and/or prestige from locating new residential projects adjacent to new or up-and-coming green amenities. Our work contributes to debates about urban greening's perceived position as a value-producing and rent-extracting good from both a political economy and political ecology 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".