Economics of Bioenergy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it