Economic and environmental impacts of biofuel policy in Canada: An application of input-output modelling
Bibliographic record
Abstract
ABSTRACTThe government of Canada has committed that Canada’s total GHG emissions be reduced by 17 percent of 2005 levels by 2020. To achieve this, the federal government of Canada announced its renewable fuels strategy in 2007, which introduced mandated requirements for the use of ethanol and biodiesel. The Federal government has also introduced several initiatives by setting new emissions standards for heavy-duty vehicles, and phase in regulations for the generation of electricity from coal by 2015. These regulations required 2% renewable content in diesel fuel and heating distillate oil by 2012, for a total production of approximately 600 million litres of biodiesel per year. In addition, renewable content standards for gasoline were targeted at 5% by 2010. This represents approximately 2.1 billion litres of ethanol being required per year according to the Canadian Renewable Fuels Association. With this backdrop, the study aims at estimating the macroeconomic impacts of the ethanol and biodiesel sectors in Canada using an Input-Output model. Biofuel sectors including ethanol, biodiesel and corresponding by-products have been incorporated into the 2008 Input-Output Model of Canada. Simulation exercises have also been attempted to reach the mandates using a modified Leontief model. Results show that the macroeconomic impact of the ethanol sector leads to an increase in industrial output, GDP and employment. Further, the agriculture sector is affected because of feedstock use in the biofuel sector. Mining and manufacturing industries also show a considerable impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".