Economic Incentives, Transaction Costs and Carbon Trading: The Economics of Alberta’s Reduced Age to Harvest Protocol
Notice bibliographique
Résumé
Economic Incentives, Transaction Costs and Carbon Trading: The Economics of Alberta’s Reduced Age to Harvest Protocol. Climate change is a global problem that requires individual countries to take action to reduce their greenhouse gas (GHG) emissions. Canada’s GHG emissions have increased from 613 megatonnes (Mt) in 1990 to 726Mt in 2013; an 18 percent increase (Environment and Climate Change Canada, 2015). The agriculture sector produces approximately 10 percent of the GHG emissions in Canada and methane and nitrous oxide are the two largest sources of emissions (AAFC, 2015). The main sources of methane emissions are from cattle and sheep production and animal manure. The amount of methane produced during the production process can be reduced by changing the animal’s diet, the age at harvest and the addition of various agents and compounds to the feed (AAFC, 2014a). GHG emissions in the province of Alberta increased from 175Mt in 1990 to 267 Mt in 2013 (Environment and Climate Change Canada, 2015). To address this increase in GHG emissions, Alberta introduced a carbon traded system where large emitters of GHG emissions are regulated (Boyle, 2008-09). Regulated firms can satisfied their required reductions by: (1) reducing their GHG emission intensity, (2) purchasing Emission Performance Credits from other regulated firms, (3) purchasing Technical Fund Credits or (4) purchasing Carbon Offset Credits. The province of Alberta has been active in the development of agricultural protocols that can be used to produce Carbon Offset Credits. However, agricultural producers will only undertake these management changes if there are economic incentives to do so. This paper investigates the transaction costs that will be incurred in generating Carbon Offset Credits from the Reduce Age to Harvest protocol and the economic incentives that will be generated for the aggregator and the feedlot operator. The Reduce Age to Harvest (RAH) Protocol was designed to quantify the GHG emissions that result from reducing the age of cattle when harvested. The protocol is designed to measure the GHG emissions per kilogram of carcass weight of beef. The RAH protocol includes the GHG emissions from the pasture, backgrounding, and feedlot operation as well as manure storage and handling. When the age to harvest is decreased the amount of GHG emissions also decreases (AESRD, 2011). A greenhouse gas calculator that was specifically designed for the feedlot industry was used to estimate the impact of decreasing the age of harvest from 19 months to 15 months. The change in management required bringing steer or heifer calves into the feedlot instead of yearling animals. This change in management resulted in a reduction of 2.3 tCO2e per head for steers and 2.49 tCO2e for heifers. The transaction costs of developing Carbon Offset Credits plays an important role in estimating the economic incentives of the RAH protocol. The RAH protocol will require the services of an aggregator who will manage the transaction costs of generating the Carbon Offset Credits from the feedlot operator to selling the Offset Credits on the market. In order to estimate the costs of the aggregation function, a series of semi structured and structured surveys and interviews were undertaken with aggregators who worked with feedlot operators as project developers. The semi-structure survey was an open discussion with one aggregator to define the types of costs that would be associated with developing a project using the RAH protocol. These costs were broken down into three categories. The first category was fixed costs that would be required no-matter the number of credits that would be generated and included: legal costs, accounting costs, office space, project planning, website development, computer software development, computer storage, etc. The second category of costs varied with the number of carbon offset credits that are generated. These included data collecting costs, de-listing costs, registration costs, and insurance costs. The third set of costs was based on the number of projects in the aggregator’s portfolio. These included the project listing costs and the verification costs. Once these detailed costs were identified a group of aggregators were surveyed to provide estimates of the various costs. Once these estimates were received, a consensus group of estimates were developed. These were then reviewed by the aggregators to provide an indication of the accuracy of the estimates. Once these costs were deemed acceptable a net present value calculation was undertaken to determine the economic feasibility of providing project development services for a RAH project. This analysis includes a sensitivity analyses on the variables that had an impact on the economic feasibility of being an aggregator. The economic incentive for the feedlot operator was also investigated. This included variations in the carbon price, the share of carbon revenue between the aggregator and the feedlot operator and the change in the cost of production from the change in management. This study estimated the benefits and costs for the aggregator and the feedlot operator of generating Carbon Offset Credits using the RAH protocol in the Alberta Carbon Market. Three carbon price scenarios were investigated and a sensitivity analysis was undertaken on some of the key variables that would impact the results. The analysis indicated that there are economic incentives for aggregators to get involved in the Carbon Offset Market with the RAH protocol. The economic incentives change with the carbon price, quantity of offset credits generated and the share of carbon revenue going to the aggregator. There are also favourable economic incentives for the feedlot operator. These results are sensitive to the carbon offset price, the share of offset revenue received and the change in the cost of production. The analysis indicated that the economics from carbon offset generation is positive, however, the revenue generated is substantially less significant than the revenue from the cattle production.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».