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Modelling the costs of energy crops: A case study of US corn and Brazilian sugar cane

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Jan 2010 France Publisher:Elsevier BVJournal:Energy Policy, volume 38, pages 547-561 (issn: 0301-4215,

Authors: Méjean, Aurélie; Hope, Chris;

doi: 10.1016/j.enpol.2009.10.006

Modelling the costs of energy crops: A case study of US corn and Brazilian sugar cane

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Abstract

Abstract High crude oil prices, uncertainties about the consequences of climate change and the eventual decline of conventional oil production raise the prospects of alternative fuels, such as biofuels. This paper describes a simple probabilistic model of the costs of energy crops, drawing on the user's degree of belief about a series of parameters as an input. This forward-looking analysis quantifies the effects of production constraints and experience on the costs of corn and sugar cane, which can then be converted to bioethanol. Land is a limited and heterogeneous resource: the crop cost model builds on the marginal land suitability, which is assumed to decrease as more land is taken into production, driving down the marginal crop yield. Also, the maximum achievable yield is increased over time by technological change, while the yield gap between the actual yield and the maximum yield decreases through improved management practices. The results show large uncertainties in the future costs of producing corn and sugar cane, with a 90% confidence interval of 2.9–7.2$/GJ in 2030 for marginal corn costs, and 1.5–2.5$/GJ in 2030 for marginal sugar cane costs. The influence of each parameter on these supply costs is examined.

Country

France

Related Organizations

French Institute for Research in Computer Science and Automation
France
University of Cambridge
United Kingdom
École des Ponts ParisTech
France
Centre de Coopération Internationale en Recherche Agronomique pour le Développement
France

Keywords

[SHS.ECO] Humanities and Social Sciences/Economies and finances, JEL : [, JEL : ], 338, [ SHS.ECO ] Humanities and Social Sciences/Economies and finances, Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q42 - Alternative Energy Sources [JEL], [ SHS.GESTION ] Humanities and Social Sciences/Business administration, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, Experience, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q42 - Alternative Energy Sources, Uncertainty, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q47 - Energy Forecasting, Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q5 - Environmental Economics/Q.Q5.Q55 - Technological Innovation [JEL], [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Biofuels, [SHS.GESTION]Humanities and Social Sciences/Business administration, JEL : Q:Q4:Q47, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q5 - Environmental Economics/Q.Q5.Q55 - Technological Innovation, Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q47 - Energy Forecasting [JEL], [SHS.GESTION] Humanities and Social Sciences/Business administration, Land suitability, jel: jel:Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q47 - Energy Forecasting, jel: jel:Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q5 - Environmental Economics/Q.Q5.Q55 - Technological Innovation, jel: jel:Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q42 - Alternative Energy Sources

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