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Record W3209462045 · doi:10.1111/ajae.12272

Curvature and competitiveness: Carbon taxes in cattle markets

2021· article· en· W3209462045 on OpenAlexaff
Brandon Schaufele

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsWestern University
Fundersnot available
KeywordsEconomicsCarbon taxMarginal abatement costMonetary economicsCounterfactual thinkingMarket powerLiberian dollarRevenueAgricultural economicsMicroeconomicsGreenhouse gas

Abstract

fetched live from OpenAlex

Abstract Environmental regulation can interact with agricultural markets to produce underappreciated competitiveness and leakage effects. This paper measures effective carbon tax stringency by structurally recovering the domestic supply schedule for a trade‐exposed beef cattle industry such that elasticities and carbon tax rates change with product prices (i.e., due to the curvature of the supply function). Two basic propositions from the economics of taxation—that excess burdens increase in elasticities and tax rates—are shown to cause the stringency of uniform carbon policy to vary nonlinearly with output prices. Based on the domestic supply function, the relationship between marginal excess burden, a measure of policy stringency from the industry's perspective, and product prices is estimated. Several policy‐relevant counterfactual scenarios are explored. Results show that with moderately high output prices, supply elasticities are small and the efficiency cost of a $40/tCO 2 e carbon tax (gross of environmental benefits) is less than $0.01 per dollar tax revenue. As prices decline, supply curves become increasingly elastic and marginal excess burdens grow rapidly.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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.

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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