Subsidization of the Biofuel Industry: Security vs. Clean Air?
Bibliographic record
Abstract
Agriculture plays an important role both in reducing Greenhouse Gas Emissions (GHG) and dependence on imported oil from economically and politically volatile areas. Certain crops can be used as inputs for the production of alternative fuels. In addition to these two benefits, the development of biofuel industry has other secondary advantages including rural development. As the current price of biofuel is considerably greater than the price of fossil fuel alternatives, the governments around the world are heavily subsidizing the development of this industry. This paper focuses on the growth of biofuel industry in Canada and US. We develop a theoretical model to examine whether or not the same governmental policy (subsidization) yields different results i.e. a different level of optimal subsidies under different current objectives. We consider that subsidizing the development of the biofuel industry in the present is equivalent to buying an option on its use for future goals energy security or reduced GHG emissions- so our research uses option value theory to assess these alternatives. The theoretical model yields an optimal subsidy option function for each of the two countries. Furthermore, under the scenario of obtaining different optimal levels of subsidies in the two countries, trade disputes can arise. A numerical simulation method is proposed to quantify the optimal level of subsidy option for each country.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".