Steering towards the most adequate performance indicator for an economic and hydrological evaluation of low-impact development practices
Bibliographic record
Abstract
Low-impact development practices, including rain gardens and bioretention systems, can help mitigate combined sewer overflows (CSO) in urban areas. However, selecting the most appropriate economic indicator for evaluating their cost-effectiveness remains a key challenge for decision-makers. A methodology was developed in this study to guide the selection of the most suitable low-impact development implementation scenario for reducing CSOs. Various performance indicators were compared, including net present value, benefit-cost ratio, cost-effectiveness, and benefit per runoff reduction, to identify the optimal scenario. Applied to two case studies in Quebec, Canada, under various implementation scenarios using continuous rainfall data, the methodology revealed that bioretention systems achieved 83-100 % CSO frequency reduction in Laval and 43-58 % in Montreal, rain gardens showed superior benefit-cost ratios (up to 3.43 in Laval, 1.84 in Montreal) and net present values reached 4.47M CAD in Laval and 80.1M CAD in Montreal at 4-5 % implementation rates. The findings also demonstrate that relying solely on the benefit-cost ratio can be misleading, as it overlooks total investment considerations. Instead, a multi-criteria approach integrating hydrological performance and economic indicators is essential for informed decision-making. This study provides a framework for optimizing low-impact development implementation in urban drainage networks, ensuring regulatory compliance and maximizing long-term benefits.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".