Performance-based approaches to agri-environmental water quality policy in Canada
Bibliographic record
Abstract
Performance-based approaches to managing water quality on agricultural landscapes offer the potential to improve the effectiveness of water quality outcomes compared to current practice-based approaches. Performance-based approaches, however, require varying degrees of precise measures or modeling of water quality and differentiated payment structures to achieve these effective outcomes. The potential to implement performance-based approaches for water quality management on agricultural landscapes was assessed through three broad objectives: 1) review and evaluate performance-based approaches used for a similar purpose in other jurisdictions; 2) assess the social context of the study region of southwest Alberta, with the intention that this region would serve as a test case for implementing performance-based approaches; and 3) determine the suitability of performance-based approaches for the study region based on social and institutional context. Several performance-based approaches were identified through the review and evaluation of approaches that have been implemented elsewhere, these were: water quality trading, differentiated payments for ecological goods and services, cross-compliance, and emissions charges. The drivers and enabling conditions were evaluated and social and institutional factors were often important for the social, environmental, and/or economic successes of the approaches. The social context, or social norms and values, related to agriculture and water quality within the study region was assessed using interviews with watershed landowners and surveys with rural and urban residents. Respondents were generally in favour of a combination of polluter pays and beneficiary pays principles. Implementation of an environmental standard of care was a common suggestion; agricultural landowners who achieved water quality beyond the standard could be eligible for incremental payments based on water quality improvement. Suitability of performance-based approaches to the social and institutional context of the study region revealed that a suite of measures may be required to align with social norms and values. Cross-compliance and differentiated payments for ecological goods and services were two approaches that provided a suitable mix of polluter pays and beneficiary pays principles; however, institutional barriers exist to implementing these approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".