20∗20∗60: A multilevel climate change analysis framework
Bibliographic record
Abstract
Cities worldwide have established plans and policies to achieve climate-neutral and climate-resilient objectives in recent decades. Researches have demonstrated that Climate Change Action Plans generally fail to include mitigation and adaptation approaches in their planning processes, despite their importance. A proposed multilevel assessment of Climate Change Action Plans, urban regeneration, and building projects was used to analyze the ten cities most sustainable in terms of developing environmental strategies, including local climate action to determine the degree of adaptation and mitigation integration in cutting-edge contexts and to identify measures that show synergies and co-benefits for urban design practices. Climate Change Action Plans, urban regeneration and building projects have been evaluated through scoring methods to determine firstly the level of integration among adaptation and mitigation and secondly the most used urban design solutions that addresses both approaches. Almost all of Climate Change Action Plans have "moderate" and "early" integration, with the most advanced in North American cities including Toronto, Montreal, New York, and San Francisco. Climate Change Action Plans partly influence urban regeneration projects. Among the cities studied, Royal Seaport and Hammarby Sjöstad in Stockholm stand out as the most advanced in terms of including measures for both mitigating and adapting to climate change, as well as the extent of activities carried out. North American building projects have the highest adaptation and mitigation strategies. Climate Change Action Plans, urban regeneration initiatives, and building projects analyzed have displayed measures to include both climate change mitigation and climate change adaptation benefits into urban design.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".