Urban Regreeneration: Green Urban Infrastructure as a Response to Climate Change Mitigation and Adaptation
Bibliographic record
Abstract
This research focuses on how green urban infrastructure contributes to adapt and mitigate climate change consequences.It analyses the benefits derived from an overall green urban regeneration, including green roofs, green faç ades and sustainable urban drainage.This would contribute to both climate change adaptation and mitigation solutions, including the following: reduction in cooling and heating demand; bio-retention of stormwater and consequently ameliorating risks of floods; reducing hot spots which create urban heat island and improving urban health.This paper first categorizes green urban infrastructure solutions.It analyses a series of case studies conducted in Germany, Spain, Canada and the USA; in order to identify the contribution to mitigate and adapt to climate change.It develops a set of measurable figures which define the contribution of an overall green urban solution intervention.It applies this assessment specifically to an urban space of Amurrio (Araba, Spain) and to an existing building of Balmaseda (Bizkaia, Spain).It finally analyses evaluation tools for governments and planning institutions to improve planning strategies and policy developments.The paper presents the results, concluding that climate change mitigation and adaptation green urban solutions are mainly achieved when applied in the larger scale of a whole city.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".