Finding common ground: a comparison between coastal nature-based solutions in the Netherlands and British Columbia, Canada
Bibliographic record
Abstract
Many coastal communities worldwide are facing challenges caused by increasing sea levels. However, urban development, population growth and industrialisation in low-lying delta regions persist. This includes the Netherlands and British Columbia, Canada. Both regions explore new and innovative flood risk and adaptation strategies by initiating nature-based solutions (NBS) pilot projects and integrating research and community initiatives. The aim of this paper is to learn from the experiences with these NBS pilots and support practitioners with insights and knowledge about the prospectives and implementation process of NBS. Our study takes a bird's eye view by diving into four NBS case study projects that try to enhance flood defence and quality of life while considering ecosystems and community values simultaneously. To better understand current initiatives on NBS, we first describe the historical trajectories of flood risk management and climate adaptation policy in both countries. Then we analyse two urban and two suburban case studies to identify and compare enablers and barriers that surround the implementation of NBS. We use the Pilot Paradox as a framework to reflect on the enablers and barriers, and to formulate recommendations for barriers that are common ground. We found that upscaling of the pilots forms an important challenge in both countries. We also found that Canada is interested in exchanging technical knowledge, experiences, and insights with other countries through the involvement of international researchers, consultants, and students in projects. Such collaboration between countries, communities, practitioners, and academics could accelerate the development of innovative climate adaptation strategies worldwide.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".