Estimating the value of ecosystem goods and services for private and public decision making in agriculture
Bibliographic record
Abstract
The main objective of this research is to explore new economic methods to evaluate and promote ecosystem services from agriculture. The three studies present economic methods to assess and improve the sustainable development of the agricultural sector.The first study evaluates agricultural land as a source of natural capital in the province of Quebec using a geographic information system coupled with a spatial hedonic pricing model of agricultural land transactions during the period 1990-2010. The results show that although the total area has increased slightly, the average real value per hectare has decreased over time suggesting the average quality has decreased. This would suggest that the value of natural capital of agricultural land has decreased over time. The results support the existence of a law protecting agricultural land from other uses to protect the value of the natural capital. In addition, better land development planning could be implemented to avoid unrecoverable loss.The second study investigates the cost of supplying an ecosystem good, i.e. improved water quality, from an agricultural watershed by adopting beneficial management practices. The scale at which the environmental policy is implemented has an impact on the cost of supplying the improved water quality. Setting the policy at the watershed scale is a lower cost alternative than setting the policy at the individual farm level. The study also investigated the factors affecting the ability of the farms to reduce pollution. The most crucial factor was farm size because it is more expensive for smaller farms to reduce pollution emissions since they have fewer opportunities. Policies should take this into consideration when designing programs supporting environmental protection initiatives. The third study evaluates how price premiums on food items can be used as incentives for agricultural producers to provide additional ecosystem services. Milk is used as a case study and three evaluation methods were used. Contingent valuation, choice modelling, and hedonic pricing were used to evaluate the price premium for environmental and health attributes. The results show that the location of production and health attributes were the most valued attributes while environmental protection and animal welfare provided low or no price premiums. The results generated from the three methods also provide additional information about the preferences of consumers and their behaviour. The ecosystem services that generate private benefits such as health attributes induced a larger market price premium while those that were of a common-pool nature did not generate a price premium. The dissertation illustrates how the agricultural sector can play a major role in future sustainable development. Agricultural policies in sustainable development are more effective for common-goods while markets can support environmental initiatives that influence private well-being.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".