Urban stream daylighting as a nature-based solution: transformative or incremental? A scaffolded review of Seoul’s Cheonggyecheon and Zürich’s <i>Bächkonzept</i>
Bibliographic record
Abstract
As the climate crisis drives the uptake of nature-based solutions (NbS), daylighting culverted urban streams is gaining traction. Yet, questions abound on how to approach stream daylighting and on its impacts. This study responds through a scaffolded literature review: 1) a scoping review on transformative and incremental adaptations that led to a comparative framework of four transformative-incremental adaptations criteria and their corresponding 18 parameters; and 2) a systematic content analysis of two fundamentally different approaches to stream daylighting: Seoul’s (S. Korea) mega-project that daylighted over 6 kilometers of the Cheonggyecheon and Zürich’s (Switzerland) small-scale Bächkonzept that daylighted over 25 kilometers of small stream segments. The findings reveal that, notwithstanding temporal, scale, and cost differences, Seoul’s Cheonggyecheon and Zürich’s Bächkonzept are both transformational, albeit differently. Yet, neither fits the abrupt (maintained functions, changed structures) nor the directed (changed functions, maintained structures) transformations; instead, lessons are gleaned from these two daylighting interventions.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".