MétaCan
Menu
Back to cohort
Record W4402146304 · doi:10.1093/qopen/qoae023

Simulating the impact of a carbon tax on food in four European countries

2024· article· en· W4402146304 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueQ Open · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersEmissions Reduction Alberta
KeywordsCarbon taxCarbon fibersBusinessEconomicsEnvironmental scienceNatural resource economicsMaterials scienceGreenhouse gasGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Since agriculture is responsible for a considerable share of anthropogenic greenhouse gas emissions (GHGE), this paper examines the impact of various carbon taxes designed to incentivize environmentally friendly food consumption patterns in four European countries: Finland, Italy, Sweden, and the UK. As the proposed fiscal policies are likely to affect food consumption patterns, the study also assesses the consequent changes in diet quality and welfare. The results from this analysis reveal considerable variations in the reduction of GHGE across countries and tax schemes. While most taxation schemes have only a modest impact on dietary quality, these effects differ among nations. Additionally, the welfare cost of the compensated scheme is relatively small but not insignificant. These findings raise questions about the efficacy of a common European fiscal policy for climate mitigation compared to a more flexible approach where each member state calibrates the tax according to its unique circumstances.

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.

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 score0.421
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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