Risk: A Fundamental Barrier to the Implementation of Low Impact Design Infrastructure for Urban Stormwater Control
Bibliographic record
Abstract
Rapid transformations of the urban landscape to cope with infrastructure needs have adverse consequences on the aquatic environments from urban stormwater runoff flows and the associated pollutants washed into the rivers and lakes. The implementation of low impact design (LID) infrastructure is considered a better approach to conventional management and control of urban runoff but has been slow, or non-existent, in many new urban cities. In addition to institutional, technological, social and economic barriers, risks and concern for contractual liabilities are identified as factors prohibiting uptake of low impact design. This article is based on findings from interviews; a survey and online questionnaire. The data obtained from the online survey was analyzed using non-parametric tests. First, the study showed risk as the most dominant factor inhibiting implementation of LID infrastructure. In addition, the results show a significant difference exists between professionals with LID implementation hands-on experience versus their counterparts with theoretical knowledge alone. Due to self-efficacy of professionals with practical experience, they are more inclined to favour and promote LID infrastructure, and hence possess better propensity for contractual liabilities risk taking. Summarily, the article proposes dissemination of relevant information among practitioners to improve LID knowledge apprehension and utilization. This will reduce perception of risk that will promote uptake. In addition, team collaboration with equitable contractual risk sharing for LID project planning and implementation is advocated.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".