Urban green grabbing: Residential real estate developers discourse and practice in gentrifying Global North neighborhoods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it