Climate Urbanism as a New Urban Development Paradigm: Evaluating a City’s Progression towards Climate Urbanism in the Global South
Bibliographic record
Abstract
Climate change impacts, the resulting spatiotemporal changes, and growing uncertainty exert pressure on city leaders and policy makers to create climate adaptive development strategies worldwide. This article introduces climate urbanism as a new development paradigm that advocates for a climate adaptive urban development process, safeguarding urban economics and infrastructure, and ensuring equitable implementation of related strategies. The objective of this article is to determine how far a climate vulnerable city in the Global South has progressed toward climate urbanism. The study employs Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to develop a conceptual framework. Afterward, the analytical hierarchy process (AHP) and indexing are used to develop a multicriteria decision analysis (MCDA) method to assess the selected climate sensitive factors related to climate urbanism. Findings reveal that the city of Khulna’s climate urbanism index score is 0.36, which is extremely low and denotes subpar urban performance. ‘Climate Conscious Governance’ and ‘Climate Smart Infrastructure’ contribute little, while ‘Adaptive and Dynamic Urban Form’ and ‘Urban Ecosystem Services’ contribute even less. The binary logistic regression analysis reveals the significant indicators of (transformative) climate urbanism. The article provides a critical lens for stakeholders to evaluate climate urbanism and promote urban sustainability in the face of climate change.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".