Urban Governance for Adaptation: Assessing Climate Change Resilience in Ten Asian Cities
Bibliographic record
Abstract
Summary Rapidly expanding urban settlements in the developing world face severe climatic risks in light of climate change. Urban populations will increasingly be forced to cope with increased incidents of flooding, air and water pollution, heat stress and vector‐borne diseases. This research, undertaken with a set of partner research institutes, examines how to manage climate‐related impacts in an urban context by promoting planned and autonomous adaptation in order to by improve resilience in a changing climate. It investigates the linkages between the characteristics of pro‐poor good urban governance, climate adaptation and resilience, and poverty and sustainable development concerns. The paper develops an analytical framework by combining governance literature with rapid climate resilience assessments conducted in ten Asian cities. Based on this empirical data, we argue that a number of key characteristics can be identified to assess and build urban resilience to climate change in a way that reduces the vulnerability of the citizens most at risk from climate shocks and stresses. These characteristics form the basis of a climate resilient urban governance assessment framework, and include (1) decentralisation and autonomy, (2) accountability and transparency, (3) responsiveness and flexibility, (4) participation and inclusion and (5) experience and support. This framework can help to assist in the planning, design and implementation of urban climate change resilience‐building programmes in the future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".