Estimating the Willingness-to-Pay for Agri-Environmental BMP Adoption in Alberta's South Saskatchewan Region
Bibliographic record
Abstract
The overall objective of this study was to determine the economic benefits associated with wildlife habitat and water quality enhancement in Alberta’s South Saskatchewan Region as results of agri-environmental BMP adoptions. Stated preference questionnaires were designed to elicit the associated non-market values by using water quality ladders and numbers of at-risk species as the survey attributes for water quality and wildlife habitat questionnaire respectively.Parametric results from logit models showed that Alberta South Saskatchewan Residents valued wildlife habitat or water quality improvements. The estimated mean annual household WTP for wildlife BMP programs ranged from $71 - $206 for one-unit improvement in the numbers of net not-at-risk species. The wildlife habitat survey results showed that urban, female as well as higher household income respondents were more willing to pay for BMP programs. In addition, awareness on farming practices and the period of the programs also could impact the values of WTP. On the other hand, the estimated mean annual household WTP ranged from $100 - $113 for changing Bow River Basin or Oldman River Basin water quality from fishable to swimmable, while only gender impacted the values of WTP. The survey data also indicated that there was a consensus between rural and urban individual regarding future government spending on water quality improvements, but no consensus on future wildlife habitat investments.The estimated aggregated welfare measure was approximately $655 - $818 million for wildlife habitat programs, and $338-$381 million for one-level water quality improvement in Bow River Basin and Oldman River Basin. Overall, the results support Growing Forward, however, the funding amount provided to agricultural producers are much less than our estimated aggregated welfare measures. To improve future BMP adoptions as well as to enhance wildlife and waterquality in SSR, government authorities need to relax the requirement of Environmental Farm Plan (EFP) and to take actions to improvement BMP and GF awareness.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".