GREENING THE GREY: IMPLEMENTING GREEN URBAN SOLUTIONS, AS ADAPTATION RESPONSE TO CLIMATE CHANGE, IN A PILOT PROJECT IN LEGAZPI, BASQUE COUNTRY, SPAIN
Bibliographic record
Abstract
This paper focuses on the implementation of green urban infrastructure solutions (GUIS) in an urban pilot project in Legazpi, Gipuzkoa (Spain).It shows the environmental benefits derived from an overall GUIS project, in terms of climate change adaptation, such as ameliorating stormwater runoff, reducing urban hot spots and improving urban air and water quality.The design process followed in this project started with a community engagement with the residents of Legazpi.A series of international case studies of GUIS were presented.The conclusions of the community engagement informed the final design and construction project.A selection of GUIS were implemented including permeable paving with high albedo finishing materials; bio-retention areas; stormwater retention tanks; and a vegetated pergola.During the design phase, the contribution of the applied GUIS to climate change adaptation was analyzed.It shows that proposed GUIS contribute to reduce the runoff by 25%, the urban temperatures by up to 20°C, and sequester the 7% of the CO2 emissions from the site.The paper includes lessons learned and the barriers identified when implementing these GUIS.It demonstrates that implementing GUIS in an urban renovation project in Legazpi, are effective to mitigate climate change consequences.Larger projects and more experience are needed in both the construction sector and the technical professionals, to move from pilot projects to common practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".