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Record W2771443830 · doi:10.55016/ojs/sppp.v10i1.42252

Taxing Feedlots in Alberta: Lethbridge County's Tax on Confined Feeding Operations

2017· article· en· W2771443830 on OpenAlexfundaboutno aff
Bev Dahlby, Melville McMillan, Mukesh Khanal

Bibliographic record

VenueThe School of Public Policy Publications · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
FundersAlberta Beef Producers
KeywordsGeographyAgricultural economicsBusinessPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Lethbridge County introduced a new business tax on confined feeding operations (CFO), notably feedlots, in 2016. It was expected to bring in $2.5 million for county road maintenance in 2017. However, the tax could have a detrimental impact on feedlot owners and is not the fairest way to amass revenue for road repairs. Four criteria can be used to evaluate a particular form of taxation. They are fairness, efficient resource allocation, compliance and administration costs, and revenue stability. This paper examines the potential impacts of the tax and proposes three alternative methods for financing Lethbridge County road maintenance that meet those criteria. These alternatives create less of an impact on feedlot owners and share the tax burden more equitably. They also reduce the potentially negative ripple effects that the CFO levy may have on feed producers, cattle producers, meat packers and consumers. The current tax is based on livestock storage capacity, rather than on production volume. It’s counter-productive in the long run because the feedlot’s fixed costs of production are increased, while its variable costs remain unaffected. This permanent increase in fixed costs, estimated to be as high as 20 per cent of the average operating margin per head of cattle, lowers the return on feedlot investments. Thus, the new tax could result in some feedlots being closed for lack of a high enough return on investment.A more equitable revenue source for road maintenance would be user fees imposed on the trucking industry. This system is already in use in Oregon and New Zealand. It uses GPS technology to track trucks on the roads and then charges the trucking companies a fee based on road use. It would certainly be worthwhile for the province to initiate a pilot program to test the system’s efficacy on Alberta roads. Lethbridge County could also implement a usage levy that would be based on how much it spends on roads, combined with a feedlot’s capacity plus its distance from a provincial highway. Contributions to road maintenance would thus be directly tied to road usage. The new CFO tax does not distinguish between feedlots with a heavy use of county roads and those that don’t use the county roads as much because they are close to provincial highways. The current CFO levy creates an unfair distribution of the tax burden. Under a usage-levy system, 94 feedlots, or 72 per cent of feedlots in the county, would have their tax burdens lessened, while 18 feedlots located on provincial highways would pay no tax. The third option would be a tax based on an equation of how many livestock per feedlot exceed the actual capacity of that farm’s own crops to feed them. Those feedlots relying more heavily on trucked-in crops, and thus dependent on greater use of county roads to feed their animals, would pay higher taxes accordingly. The three alternatives would generate the same amount of revenue as the current levy and with similar degrees of predictability. However, these alternatives are fairer, more equitable and more efficient than the CFO levy. In the interests of maintaining both county roads and a healthy feedlot industry, Lethbridge County should replace the CFO levy with one of them.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.060
GPT teacher head0.346
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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