Adoption determinants and policy tools for residential green stormwater infrastructure: A review synthesizing differences and commonalities among lot-level practices
Bibliographic record
Abstract
Climate change adaptation in intensifying urban environments benefit from green stormwater infrastructure (GSI) investments on private residential yards. Nevertheless, planners are challenged to devise policy tools to mesh such a decentralized GSI approach with current land-use and social systems. Prior research has addressed the multi-scalar socio-economic barriers hindering household uptake, including technical and governance considerations. However, the interconnectedness of these factors has not received much attention. Additionally, studies into GSI often analyze a specific GSI but then overgeneralize their conclusions about adoption determinants for a range of GSI practices. Our review aims to refine previously ambiguous generalizations by comparing adoption factors for three distinct GSI practices: rain gardens, rainwater harvesting, and permeable pavers. We systematically identified 1753 studies and conducted a full content analysis of 56 studies, defining 17 explanatory factors and discussing their independent influences on the three GSI practices. Our results suggest that adoption factors vary between GSI practices, highlighting the need for practice-specific policy tools. Using the Fogg Behavioral Model as a qualitative framework, we illustrate and synthesize the interplay between motivational and ability factors and propose potential targeted policy interventions for each GSI practice. Evidence from different contexts on the three practices suggests that neither stimulative policy instruments (e.g., providing cost subsidies) nor restrictive tools (e.g., enforcing minimal infiltration rates) are efficient in isolation. Our results can guide scholars, decision-makers, and professionals to craft practice-specific integrated policy packages, accounting for socioeconomic factors to achieve transformative GSI uptake.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".