Comparing Willingness-to-pay and Willingness-to-accept Approaches for Valuing Farmland Protection and Conversion in Alberta
Bibliographic record
Abstract
Over the last three decades, Alberta has experienced substantial urban sprawl, with some of the province’s most productive agricultural land developed into residential, light industrial and retail uses. The converted farmland provided not only market commodities, but also a variety of environmental services. Many of these environmental services are non-tradeable public goods and their value cannot be directly estimated from market data. We use choice experiments to calculate these non-market values using Willingness to Pay (WTP) and Willingness to Accept (WTA) approaches. The general objective of our study is to inform decision makers about the values gained and lost when land is converted from agriculture to other uses. In Alberta, government policies make municipalities responsible for land use planning and authorization of permitted land uses. The six most populated urban areas in Alberta were chosen as study areas: Edmonton, Calgary, Lethbridge, Red Deer, Grande Prairie and Medicine Hat. In each choice experiment survey, people were required to consider whether they prefer the current development trend to an additional conservation (WTP) or additional development (WTA) strategy. Data were collected through a procedure that included an efficient design, consequentiality questions, focus groups, pre-tests, soft launch, and full launch. The full launch of the online surveys collected complete data from 1,303 respondents. Multinomial Logit, Latent Class, and Random Parameter Logit Models were used to analyze the choice experiment data and calculate respondents’ willingness-to-pay and willingness-to-accept compensation for protection and conversion of agricultural land located near urban areas. The WTP and WTA results can be used to gauge public support for the acceptance or denial of applications for land re-designation, which could be considered a passive or reactive policy tool. The results can also be used in the design of more proactive and targeted policy tools, such as conservation easements, that could be used to identify and protect the most highly valued agricultural land or development fees, such as transferable development credits or conservation offsets, that could be levied on developers interested in converting land from agriculture to developed uses. Both the passive and active approaches require conversations and public debate about acceptable limits to private property rights and the public interest in land use.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".