What’s behind the barriers? Uncovering structural conditions working against urban nature-based solutions
Bibliographic record
Abstract
Nature-based solutions (NBS) are a promising and innovative approach to address multiple sustainability challenges faced by cities. Yet, NBS are not integrated into mainstream urban development practices. Based on a qualitative comparative case study of Germany, Hungary, the Netherlands, Spain, Sweden, and the United Kingdom, this study shows how barriers to mainstreaming urban NBS are shaped by the structural conditions in urban infrastructure regimes, which offers an improved, context-sensitive understanding of why such barriers persist. We identify underlying structural conditions shaping seven key barriers to urban NBS: limited collaborative governance, knowledge, data and awareness challenges, low private sector engagement, competition over urban space, insufficient policy development, implementation and enforcement, insufficient public resources, and challenging citizen engagement. This study also advances an understanding of urban infrastructure regimes as complex, heterogeneous systems, made up of different functional domains that define the space available for sustainability innovations. Importantly, our case comparison reveals that similar barriers to NBS mainstreaming in planning processes are caused by different structural conditions across countries. For example, perceived causes of limited citizen engagement are low environmental awareness in Spain, a lack of resources to support participation in Hungary, and NIMBY-ism in the Netherlands. Our findings stress the importance of moving beyond ‘silver bullet’-type approaches to addressing NBS mainstreaming barriers, towards systemic but context-sensitive responses, tailored to specific urban infrastructure regimes. This systematic understanding of barriers and their underlying structural conditions can help both scholars and practitioners identify promising pathways for the mainstreaming of NBS as an urban sustainability innovation.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".