From Mandates to Outcomes: How Federal Policies Shape Local Green Infrastructure Planning and Implementation
Bibliographic record
Abstract
Problem, research strategy, and findings: Green infrastructure (GI) has attracted both national and international attention as a strategy to achieve sustainability and resilience goals. In the United States, GI has gained prominence in stormwater management, supported by federal initiatives. Since 2007, the U.S. Environmental Protection Agency has promoted the incorporation of GI into regulatory frameworks, significantly influencing local planning and practices. While previous studies have shown that regulatory drivers shape GI planning focused on stormwater management, the extent to which federal enforcement actions influence local planning processes and outcomes remains underexplored. To address this gap, I conducted a comparative analysis of two local governments with significantly different levels of federal direction for their GI projects: Cleveland (OH) and St. Louis (MO). The analysis of planning documents, supplemented by interviews and street-level implementation images, revealed that federal enforcement has guided local planning along two distinct pathways: one aiming to achieve co-benefits and the other prioritizing cost effectiveness. The findings highlight tensions in compliance-driven GI projects and their implications for balancing regulatory goals with broader community benefits. Takeaway for practice: Planners must navigate the tension between cost-effective Clean Water Act compliance and broader community benefits, using adaptive management and governance to align urban greening approaches and create synergies
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".