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Record W3186133498 · doi:10.1111/cjag.12295

Prices for a second‐generation biofuel industry in Canada: Market linkages between Canadian wheat and US energy and agricultural commodities

2021· article· en· W3186133498 on OpenAlexaffvenueabout
Curtis McKnight, Feng Qiu, Marty Luckert, Grant Hauer

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiofuelCellulosic ethanolEthanol fuelAgricultureAgricultural economicsStrawProduction (economics)Corn ethanolEconomicsGasolineBusinessAgronomyBiotechnologyMicroeconomicsEngineering

Abstract

fetched live from OpenAlex

Abstract The production of biofuels is limited in Canada, but the availability of wheat straw as a second‐generation (i.e., cellulosic) feedstock is an exciting prospect for the future development of a biofuel industry. The future success of such a biofuel industry will depend on future ethanol prices and prices related to wheat straw. These prices are likely to be influenced by markets related to the existing first‐generation ethanol industry in the United States. Therefore, the motivation of this paper is to investigate relationships between Canadian wheat prices and US corn, ethanol, and gasoline prices. We employ a DCC‐MGARCH enhanced VEC model to investigate time‐varying relationships among these markets. Results indicate that there are positive relationships between wheat and corn, ethanol and corn, and wheat and ethanol markets. Our results add to a better understanding of the level of integration between select Canadian agricultural markets and US energy markets. More specifically, the price relationships identified highlight several sources of price risk that may affect the financial success of commercially producing second‐generation ethanol from wheat straw in Canada. This information will be of particular interest to prospective industry investors and policymakers.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.160
Teacher spread0.133 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations9
Published2021
Admission routes3
Has abstractyes

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