The future for Ontario cider production and consumption: Assessment of Ontario's excise tax regime
Bibliographic record
Abstract
Abstract A competitive tax rate is necessary for products aiming to grow their market share, yet little is known about the cider beverage competitive tax landscape in the Province of Ontario, Canada. This report argues for the adoption of the similar tax treatment of Ontario beer towards Ontario cider. The purpose of assessing current cider tax treatment within Canada is to address the growing concern from local cider producers on this commodity's taxes. An analysis of current trends within the cider market, particularly concerning consumption and trade data, is examined for Canada, the United States, Belgium, France, Denmark, Netherlands, and the United Kingdom. In addition, an overview of consumption trends within Europe is assessed. The tax treatment between European alcoholic beverages is compared, with France charging lower tax on Cider than beer to the Netherlands charging over six times the rate on cider at 1.33 CAD/L. In contrast, the United States charges only 0.06 CAD/L on cider versus 0.04 CAD/L on beer. Canada charges 0.32 CAD/L on Cider and 0.24 CAD/L on beer. The findings from Europe's cider analysis suggest restructuring and lowering Ontario's taxation on cider's local production and the multiplier effect on the provincial and national economy. Sensitivity analyses demonstrate a minor decline in the tax rate would generate significantly more tax revenue to the government and, at a 2.4‐time multiplier, a positive effect economic impact of between 14 million and 28 million CAD while generating direct tax to the government of between 278,647 CAD and 479,274 CAD. Recommendations are provided for the Ontario government to implement a tax rate that favors both the government and local farmers and local processors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".