Relationships between environmental governance and water quality in a growing metropolitan area of the Pacific Northwest, USA
Bibliographic record
Abstract
Abstract. We investigate relationships between environmental governance and water quality in two adjacent growing metropolitan areas in the western US. While the Portland, Oregon and Vancouver, Washington metro areas share many common biophysical characteristics, they have different land development histories and water governance structures, providing a unique opportunity for examining how differences in governance might affect environmental quality. We conceptualize possible linkages in which water quality influences governance directly, using monitoring efforts as a metric, and indirectly by using the change in the sale price of single-family residential properties. Governance may then influence water quality directly through riparian restoration resulting from monitoring results and indirectly through land use policy. We investigate evidence to substantiate these linkages. Our results showed that changes in monitoring regimes and land development patterns differed in response to differences in growth management policy and environmental governance systems. Our results also showed similarities in environmental quality responses to varying governance systems. For example, we found that sales prices responded positively to improved water quality (e.g., increases in DO and reductions in bacteria counts) in both cities. Furthermore, riparian restoration efforts improved over time for both cities, indicating the positive effect of governance on this land-based resource that may result in improved water quality. However, as of yet, there were no substantial differences across study areas in water temperature over time, despite an expansion of these urban areas of more than 20 % over 24 years. The mechanisms by which water quality was maintained was similar in the sense that both cities benefited from riparian restoration, but different in the sense that Portland benefited indirectly from land use policy. A combination of long-term legacy effects of land development, and a relatively short history of riparian restoration in both the Portland and Vancouver regions, may have masked any subtle differences between study areas. An alternative explanation is that both cities exhibited combinations of positive indirect and direct water quality governance that resulted in maintenance of water quality in the face of increased urban growth. These findings suggest that a much longer-term water quality monitoring effort is needed to identify the effectiveness of alternative land development and water governance policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".