Do the societal benefits of river restoration outweigh their costs? A cost-benefit analysis
Bibliographic record
Abstract
Switzerland plans to restore 4000 km of rivers by 2090. Despite the immense investment costs, river restoration benefits have not been valued in monetary terms, and a cost-benefit analysis (CBA) does not exist for any river restoration project in Switzerland. We apply stated preference methods to elicit public preferences and willingness to pay for restoring two specific but representative river sites. The benefits of restoration are compared with its costs. Upscaling the results to the national level shows that the government budget allocated for river restoration (CHF 1200/m) is insufficient to cover the costs of local restoration projects. However, the surveyed local populations are willing to pay substantially more for restoring rivers in their area of residence than they are legally obliged to do. The CBA results demonstrate that the benefits outweigh the costs in the two case studies, and hence that restoration efforts are justified from an economic point of view. A sensitivity analysis shows that the main results and conclusions do not change when we change some of the key assumptions underlying the CBA.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".