Optimal approaches in global warming mitigation and adaptation strategies at city scale
Bibliographic record
Abstract
Abstract Case studies from global cities contribute to more focused analyses of global warming challenges and demonstrate the performance and effectiveness of mitigation and adaptation strategies to identify lessons about success at the city scale. The case studies were chosen to demonstrate aspects of the critical messages for action priorities in global warming mitigation and adaptation. This work focuses on best practices and initiatives for mitigation and adaptation approaches from developed and developing economies, including North American cities, European cities, Asian cities, and other global cities worldwide. The case studies were grouped to examine, identify, and emphasize important factors in various areas (e.g., local programs and alliances, governance, stakeholder engagement, community actions, and scientific research) that determined the success of adaptation strategies in various global cities. Many recent studies showcase mitigation approaches, particularly those relating to blue-green infrastructure and nature-based strategies. The case studies selected reflect vulnerable regions and demonstrate how increasing global warming significantly concerns individuals, societies, and their infrastructure. The selected studies include Amsterdam in Netherlands; Singapore, as a city in a garden; Boston in USA; Ahmedabad Heat Action Plan in India, aimed at implementing strategies with the objectives of climate adaptation planning; Copenhagen, as a coastal town, is more susceptible to flooding; Portland, the most progressive city in USA; Hamburg in Germany, one of the biggest harbours in Europe; and the 'Rain City Strategy', in Vancouver, Canada. Not all global cities respond the same way, but undertaking joint complex efforts helps mitigate the impacts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".