The impact of water law enforcement on corporations : a comparison of British Columbia’s Lower Fraser Basin and Washington State’s Puget Sound area
Bibliographic record
Abstract
The understanding of the interface between ecological and regulatory systems is necessary for the evaluation of the effectiveness of current environmental management strategies and for the design and implementation of new ones. The aim of this thesis is threefold. First, to compare and contrast the US and the Canadian environmental enforcement systems as they manifest in similar ecological systems, the Lower Fraser Basin - Puget Sound (LFB-PS) watersheds. Secondarily, to assess the tangible and intangible costs and benefits that emerged as a result of experiencing a water quality related enforcement action during the 1997-2000 time period for a random sample of companies located in these watersheds. Thirdly, to examine the feedback and learning effects that resulted from these incidents. Through case analysis based on literature review and interviews with members of the regulatory community the two enforcement systems are compared. Systematically collected primary data via confidential interviews with companies provided for the quantitative and qualitative analysis of the tangible and intangible costs, as well as for an assessment of and the nature and effectiveness of existing feedback loops and learning processes. The results have shown that even though the connection between ecosystem inputs and enforcement inputs in the PS area have proven to be tighter and relatively more complex than in the LFB, they did not seem to lead to an improved permitting process. Compliance rates are generally higher in the PS indicating more effectiveness of that system on the input side. In terms of charges and compliance effects, penalties were not higher in the PS area only when measured by direct and indirect associated costs. Comparing the complexity of the two systems has led to the discovery that the PS system has more components, levels, number of interactions, available feedback mechanisms, and longer history. However, the actual number of adjustments in the system that ultimately determine learning and change was lower since the meaningfulness of the information exchanged and the frequency of the utilization of available feedback mechanisms was less. Overall, the PS system is better for ensuring organizational compliance with water quality standards in the short term but not on the long term. For improving environmental performance, the LFB system is more equipped and adaptable due to its smaller system size and its focus on constructive feedback.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".