Economics of Bioenergy
Notice bibliographique
Résumé
Amani Elobeid 1 and Miguel Carriquiry 2 and Silvia Secchi 3 and Tun-Hsiang (Edward) Yu 41, Center for Agricultural and Rural Development, Iowa State University, Ames, IA 50011-1070, USA2, Facultad de Agronomia, Univeridad de la Republica, Avenue Eugenio Garzon 780, Montevideo 12900, Uruguay3, Department of Agribusiness Economics, Southern Illinois University, Carbondale, IL 62901-4619, USA4, Department of Agricultural and Resource Economics, The University of Tennessee, Knoxville, TN 37996-4518, USAReceived 7 November 2013; Accepted 7 November 2013This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.There has been worldwide support for the production and use of renewable energy sources, especially through major policy initiatives relating to climate change and bioenergy. These policies include the United States' Environmental Protection Agency's Renewable Fuel Standard (RFS2) and the American Clean Energy and Security Act (ACES) of 2009 (H.R. 2454), Brazil's 2009 National Climate Change Policy, Canada's 2006 Renewable Fuels Regulations, and the European Union's 2009 Energy and Climate Change Package. The rationales behind these policies have been multifold, ranging from less reliance on imported fuels, reducing energy prices, and improving the rural economy to mitigating climate change. Some have argued that bioenergy has not delivered on its promise of energy security and reduction in greenhouse gas emissions, especially in the case of field crops utilized to produce biofuels, bioheat, and biopower. The economic and environmental consequences of this bioenergy expansion have not been fully understood, primarily because bioenergy markets are not well developed and are still in flux. The main objective of this special issue is to better understand the emergence of bioenergy markets and explore the direct or indirect consequences of the expansion of this alternative energy source on the economy, energy, and commodity markets and associated environmental impacts at the country, regional, or global level. The broad range of environmental and economic impacts of biofuel production as well as their dependence on specific feedstocks and landscapes is reflected in the articles of this special issue.Y. W. Zhang and B. A. McCarl analyze the effects of autonomous adaptation-adjusted climate change and the Renewable Fuel Standard (RFS2) on US agriculture. For this analysis, the authors use the agricultural component of the FASOMGHG model, which models land use allocation within the US agricultural sector. The results show that while the impacts of climate change and RFS2 act in opposite directions, there is a net positive effect on agricultural consumer welfare and agricultural producer income. The results also suggest that, for RFS2 biofuel production, climate change promotes the use of crop residues and energy crops (other than switchgrass) for cellulosic ethanol production.F. Taheripour and W. E. Tyner use a multiregional computable general equilibrium model (GTAP-BIO), modified to include second-generation biofuels, to evaluate induced land use change (ILUC) emissions for alternative biofuel pathways in the United States. They calculate the ILUC emissions using four existing major emission factors and examine the uncertainties related to these factors and their consequences for the estimated ILUCs. The results show that the production of biofuels from dedicated energy crops shifts existing marginal cropland-pasture to crop production and also causes moderate deforestation. The largest land use change is generated from growing switchgrass as a biofuel feedstock while the lowest land use change is generated from Miscanthus for bio-gasoline production. This result is mainly due to the assumed yields for the two crops. …
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».