Green Structural Adjustment in the World Bank’s Resilient City
Bibliographic record
Abstract
According to an increasingly prevalent set of discourses and practices within environmental and development finance, cities across the Global South are facing a costly infrastructural crisis stemming from rapid urbanization and climate change that threatens to further entrench poverty and precarity for millions of people. The cost of achieving urban resilience across the world dwarfs available public finance, however, from both development banks and governments themselves. Meanwhile, vast amounts of money on capital markets are searching for profitable investment opportunities. The World Bank is attempting to channel return-seeking investment into urban infrastructure in response to these challenges. To harness this private finance, though, cities must be reformatted in investment-friendly ways. In this article, we chart the emergence of this discourse and associated practices within the World Bank. We call this rescaled and climate-inflected program of leveraged investments coupled with technical assistance Green Structural Adjustment. Drawing on policy documents, reports, and interviews with key staff, we examine programs that include Green Structural Adjustment to show how it aims to restructure local governments to capture new financial flows. Green Structural Adjustment reduces adaptation to a question of infrastructure finance and government capacity building, reinscribing both causes and effects of uneven development while creating spatial fixes for overaccumulated Northern capital in the Global South.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".