Climate change adaptation planning for Global South megacities: the case of Dhaka
Bibliographic record
Abstract
Megacities in low- and middle-income countries face unique threats from climate change as vulnerable populations and infrastructure are concentrated in high-risk areas. This paper develops a theoretical framework to characterize adaptation readiness in Global South cities and applies the framework to Dhaka, Bangladesh, a city with acute exposure and projected impacts from flooding and extreme heat. To gather case evidence from Dhaka we draw upon interviews with national and municipal government officials and a review of planning documents and peer-reviewed literature. We find: (1) national-level plans propose a number of adaptation strategies, but urban concerns compete with priorities such as protection of coastal assets and agricultural production; (2) municipal plans focus on identifying vulnerability and impacts rather than adaptation strategies; (3) interviewees suggest that lack of coordination among local government (LG) organizations and lack of transparency act as barriers for municipal adaptation planning, with national plans driving policy where LGs have limited human and financial resources; and (4) we found limited evidence that national urban adaptation directives trickle down to municipal government. The framework developed offers a systematic and standardized means to assess and monitor the status of adaptation planning in Global South cities, and identify adaptation constraints and opportunities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".