Urban green indicators: a tool to estimate the sustainability of our cities
Bibliographic record
Abstract
In Europe, more than 70% of the population lives in an urban area.All the challenges related to land use conflicts, demographic changes, climate resilience and human well-being are concentrated inside the cities, since the population is already living in urban areas, which are more and more compact and dependent on grey infrastructure.In this context, urban green infrastructure represents a sustainable solution to maintain the benefits and services provided by urban ecosystems and an efficient urban planning tool to face the urban challenges.The sustainability of our cities can be reached adopting an innovative vision using the concept of ecosystem services of the urban green infrastructure.Different initiatives to assess the benefits provided by green infrastructure have emerged in the last decade.However, very few take into account the whole range of services provided by urban green infrastructure.The present article provides a systematic search and synthesis of the most important literature to review indicators of urban green infrastructure.The main goal is to give an insight of how urban green infrastructure is measured in practice.Results show the set of ecosystem services that are being considered when assessing sustainability of green infrastructure and identify the most recurrent indicators at the different scales.This work is expected to contribute to the improvement of the evaluation of green infrastructure effectiveness for providing benefits for urban dwellers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".