Sustainable Policies to Improve Urban Ecosystem Resilience
Bibliographic record
Abstract
In the contemporary debate about key issues of urban science (sustainability, environment, governance, etc.) resilience has a fundamental role.It is defined as the ability of a complex system to cope with external stresses through adaptation and mutation strategies and to return to an equilibrium state (not necessarily equal to the original one).In particular, ecosystem resilience is based on the concepts of diversity (biodiversity), redundancy (ecological variability), cycles of adaptation (multiple equilibrium states), and interaction between spatial scales (hierarchy) and temporal (activation of different times responses).In an urban context with high soil sealing, such as historical centers, it is essential to find new methods and application techniques that will be able to integrate the use of natural solutions with artificial ones and increasing ecosystem resilience and urban ecological quality.In fact, in these contexts, the percentage of permeable and green public areas available is not capable of performing ecosystem functions: so, it is necessary to act on private property through green punctual interventions (such as green roofs and walls), that may become fundamental elements of the municipal ecological network.In fact, these actions bring benefits from several points of view (environmental, economic, building comfort, etc.)The aim of the paper is to study the relationship between technical aspects and urban policies, and, in particular, to resolve the main question: how to encourage private owners to invest in green interventions for improving buildings' efficiency and environmental quality?In this process it is important to define sustainable policies acting on private property that consider both individual and global interests.Further, the article focuses on different case studies and examples taken from the United States with the objective to define similar policies in the Italian context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".