Relationships between environmental governance and water quality in growing metropolitan areas: a synthetic view through the coupled natural and human system lens
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 biophysical characteristics, they have different land development histories and water governance structures, providing a unique opportunity for examining a coupled human and natural system (CHANS). We conceptualize feedback loops in which water quality influences governance directly, using monitoring efforts as a metric, and indirectly, using the metric of changes in the sale price of single-family residential properties. Governance then influences water quality directly through, for example, changes in the monitoring regime and riparian restoration and indirectly through land use policy. We investigate these hypotheses by presenting evidence of these linkages. Our results show that changes in monitoring regimes and land use differed in response to differences in governance systems. On the other hand, property sale prices increased in response to water quality improvement for both studied watersheds. Our results show that sales prices responded positively to improved water quality (i.e. DO) 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 changes in water temperature over time. While urban areas expanded more than 20% over 24 yr, water temperature did not change. 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 benefitted indirectly from land use policy. A combination of a long-term legacy effect of land development and a relatively short history of riparian restoration in both the Portland and Vancouver regions may have masked any subtle differences in both regions. 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 long-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 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 | 0.001 |
| 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".