ECONOMIC EVALUATION OF SELECTED POLICIES TO ENCOURAGE ADOPTION OF FIELD SHELTERBELTS IN SASKATCHEWAN
Notice bibliographique
Résumé
Historically, farmers on the Canadian Prairies have planted field shelterbelts on their farms to reduce the damage done by wind erosion due to extreme climatic conditions. However, incidence of such damages has been reduced with improvements in agricultural production methods and cultural practices (such as zero-till, and reduced summer fallowing, among others). Some farmers now regard these field shelterbelts as an economic nuisance. Although many of the barriers to the adoption and retention of shelterbelts by farmers are mostly related to their economic costs, a poor understanding of their environmental benefits may also have played an important role. \nIn response to the future changing climate, reducing greenhouse gas emissions has become a major objective of various national governments, including the Canadian government. Shelterbelts can play an important role in mitigating greenhouse gases through sequestration of carbon. This requires farmers to plant more shelterbelts. To this effect, an understanding of the factors that influence farmers’ decision regarding field shelterbelt adoption, as well as measures to encourage their adoption to increase the environmental benefits of field shelterbelts is relevant. Using a combination of a binary logistic regression model, combined with a spreadsheet-based farm-level net revenue simulation model and other numerical estimation approaches, in this study, the factors that may influence farmers’ decisions to adopt field shelterbelts were empirically estimated. In addition, the study estimated the potential impacts of selected policy instruments in encouraging the adoption of field shelterbelts in Saskatchewan. For this purpose, two policy instruments were selected – distribution of free shelterbelt seedlings to farmers, and negative carbon tax for carbon sequestration through shelterbelts. The value of negative carbon tax was determined by the price of carbon. This study considered four different carbon price scenarios – $ 74.60/tCO2(eq), $ 110.49/tCO2(eq), $ 574.13/tCO2(eq) and the carbon price levels of the Canadian carbon tax system (started at $10/tCO2(eq) in 2018, with $10/tCO2(eq) yearly increment till 2022 and $15/tCO2(eq) yearly increment from 2023 to 2030). The two selected policy instruments were evaluated based on four policy evaluation criteria; (1) Farm-level net revenue, (2) Probability of increasing field shelterbelt adoption, (3) Amount of carbon sequestered, and (4) Fiscal cost per tonne of carbon sequestration. \nThe study results indicated that factors such as farmers’ education level, farm income, and their perceptions about the environmental benefits of field shelterbelts had a positive and significant effect on farmers’ decisions to adopt field shelterbelts. Moreover, between the two selected policy instruments, results showed that the negative carbon tax policy instrument performed better based on most of the policy evaluation criteria (3 out of 4 – impact on farm-level net revenue, probability of increasing field shelterbelt adoption, and total amount of carbon sequestered) in all the soil zones of Saskatchewan. Among the four different rates of the negative carbon tax (only on the carbon sequestered through shelterbelts), study results indicated that negative carbon tax set at a carbon price of $ 574.13/tCO2(eq) was the more effective policy instrument in encouraging the adoption of field shelterbelts in Saskatchewan since it provided the higher rate of adoption than the distribution of free seedlings to farmers. The study’s findings suggest that policy designed to increase the adoption of field shelterbelts should clearly define the policymaker’s objective of whether to maximize policy benefits or minimize policy costs since the choice of an appropriate policy or set of policies may differ under these policy objectives. Particularly, it is recommended that providing free shelterbelt seedlings should be considered if the objective of the policy design is to minimize policy costs, while the negative carbon tax instrument could be considered if the objective of the policy design is to maximize the potential benefits of the policy. In general, the study’s findings suggest that governments’ policy intervention to encourage adoption of field shelterbelts by farmers in Saskatchewan through financial rewards can encourage farmers to plant and maintain field shelterbelts on their farms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».