What does it take to renature cities? An expert-based analysis of barriers and strategies for the implementation of nature-based solutions
Bibliographic record
Abstract
This paper uses an expert-based methodology to survey the barriers and strategies related to the implementation of nature-based solutions (NBS). The ambition of the paper is to offer a bird's eye overview of the difficulties encountered by NBS deployment and ways to overcome them. With a wide participation of 80 experts from COST Action Circular City, we identify barriers specific to 35 pre-defined NBS of the following four categories: Vertical Greening Systems and Green Roofs; Food and Biomass Production; Rainwater Management; and Remediation, Treatment, and Recovery. The research sheds light on how a major interdisciplinary - yet predominantly technically-oriented - community of scientists and practitioners views this important topic. Overall, the most relevant barriers are related to technological complexity, lack of skilled staff and training programs and the lack of awareness that NBS is an option. Our results highlight concerns related to post implementation issues, especially operation and maintenance, which subsequently affect social acceptance. The paper identifies a "chain" effect across barriers, meaning that one barrier can affect the existence or the relevance of other barriers. In terms of strategies, most of them target governance, information, and education aspects, despite the predominantly technical expertise of the participants. The study innovates with respect to state-of-the-art research by showing a fine-grained connection between barriers, strategies and individual NBS and categories, a level of detail which is not encountered in any other study to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".