Green Infrastructure, Climate Change and Spatial Planning: Learning Lessons Across Borders
Bibliographic record
Abstract
Climate change will further induce a generalized rise in temperature, heat waves, exacerbation of heat island effect, alteration of the precipitation regime variability with higher occurrence of high precipitation and flood events, reduction of quantity and quality of freshwater resources, disruption of agricultural production, leading to food security risk, degradation of recreational and aesthetic amenities, and loss of biodiversity. On other hand, Green Infrastructure, that is, the network of natural and semi-natural spaces within and around urban spaces, brings a constructive and protecting element that may mitigate and adapt to the local level impacts of climate change, strengthening local resilience. This paper presents a comparative study of various green infrastructures' implementation based on analytics in the United States of America, United Kingdom and Portugal, and focuses on the degree of its alignment with the public policies of mitigation and adaptation to the impacts of climate change. Pursuant to the identification of successes and failures, this paper infers common strategies, goals and benchmarking on outcomes for more adequate decision implementation and sustainable spatial planning, considering the importance of green infrastructure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".