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Ontario feedlot operators' willingness to accept carbon credit revenue for adopting management practices that reduce greenhouse gas emissions

The Canadian agricultural sector was recognised as a potential seller of carbon offset credits in the domestic emission trading system. A number of beneficial management practices may reduce GHG emissions while increasing production efficiency and profitability of agricultural activities. A contingent valuation survey was used to estimate the carbon offset price at which feedlot operators in Ontario would adopt two management practices that reduce GHG emissions: adding roasted soybean seeds to a cattle diet and increasing the intensity of feedlot operations. The value elicitation questions to estimate the mean WTA compensation were designed using a multiple bounded discrete choice format developed by Welsh and Poe (1998). / It was estimated that at a carbon offset price of $ 25.14/t CO2 e provided enough incentive for feedlot operators to intensify their operations and a price of $ 109.51/t CO2e to change their feeding strategies. The mean willingness to accept a cost to change a conventional practice to a greenhouse gas emissions reducing practice was estimated to be 62% of the carbon revenue. The regression analysis demonstrated that producers' willingness to accept compensation was influenced by the individual's characteristics, farm structure variables, and practice attributes. Policy makers may use these results in the design of greenhouse gas reduction strategies for the beef sector.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.101851
Date January 2007
CreatorsHristeva, Polina.
PublisherMcGill University
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Formatapplication/pdf
CoverageMaster of Science (Department of Natural Resource Sciences.)
Rights© Polina Hristeva, 2007
Relationalephsysno: 002666666, proquestno: AAIMR38402, Theses scanned by UMI/ProQuest.

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