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Record W7036327426

Biofuels Do Brazil? - Impact of Multinational Biofuel Mandates on Agri-Food Trade

2012· other· en· W7036327426 on OpenAlexaboutno aff

Bibliographic record

VenueSocio-Environmental Systems Modeling · 2012
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelMultinational corporationProduction (economics)Aviation biofuelRenewable energy
DOInot available

Abstract

fetched live from OpenAlex

Since 2001, a rapid growth of biofuel production has been observed, driven by high crude oil prices, as well as by growing interest in reducing Greenhouse-Gas-Emissions (GHG).High oil prices encouraged innovations to reduce crude oil consumption and triggered governments all over the world to stimulate the production and consumption of biofuel.To assure a certain level of reduction of GHG emissions, mandatory targets, e.g., in terms of binding blending targets, have been established.These quantitative measures set targets for the share of renewable fuels (biofuel) in fuel consumption.Mandatory, but also voluntary, requirements are currently imposed for liquid biofuel in many major world economies except for Russia, Sorda et al. (2010).The consequences of biofuel policies on agricultural markets and GHG emissions have been analyzed in numerous papers.The extensive overview of such studies can be found in Rajagopal and Zilberman (2007).As Rajagopal and Zilberman point out, most of these studies focus on simulating the impact of renewable fuel mandates either at national or at global level.The majority of these studies, however, analyze either the impact of the 2009 EU Directive on Renewable Energy (DRE) or the consequences of the 2007 US Energy Independence and Security Act (EISA) or both; e.g., OECD ( 2008), Al-Riffai et al. (2010), Banse et al. (2008), Hertel et al. (2010).However, none of the studies simultaneously assess the global consequences of biofuel policies in those countries mentioned above.This is an important shortcoming because regions not covered by these analyses, but implementing biofuel targets, are often very important producers and exporters of agricultural commodities.The important question is: how will biofuel programs in these countries affect their future exports if more agricultural commodities are used for domestic purposes, e.g., as feedstocks for biofuel production, and how will the world prices of these products respond?In this paper, we address these questions by analyzing the consequence of obligatory biofuel mandate implementation in all regions having such a policy.As far as we know, this paper is a first attempt to do this.This paper explicitly examines the joint effect of obligatory biofuel mandates in the EU, the US, Canada, Brazil, the Rest of South America, India, and South-East Asia on land, food production, total GHG balance, trade and prices of agricultural commodities.We will also look at how these policies will influence biofuel production in regions where biofuel targets are voluntary, e.g., China, Japan, Australia and New Zealand.By using the CGE-model LEITAP, coupled to the integrated assessment model IMAGE, cross-sectoral effects of biofuel mandates, geographically explicit land use, and environmental effects like GHG balances and carbon stocks will also be taken into account. Biofuel policiesThe wide range of policy instruments is used to encourage and support biofuel production; FAO (2008), Rajagopal and Zilberman (2007), Sorda et al. (2010).Since biofuel production is not profitable in all countries, with the exception of Brazil, it has to be supported to become competitive.This is done by applying such policy instruments as subsidies and tax exemptions.Other forms of support include the policy measures influencing the biofuel supply chain directly or indirectly via subsidies for technological innovation, production factors subsidies, government purchases and

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.021
GPT teacher head0.271
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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