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Record W4327557164 · doi:10.1093/jeea/jvad020

Carbon Taxation and Greenflation: Evidence from Europe and Canada

2023· article· en· W4327557164 on OpenAlexaboutno aff
Maximilian Konradt, Beatrice Weder di Mauro

Bibliographic record

VenueJournal of the European Economic Association · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsEconomicsInflation (cosmology)RevenueMonetary economicsEmpirical evidenceCarbon taxRelative priceCarbon priceGoods and servicesCarbon fibersSample (material)Inflation taxMacroeconomicsMonetary policyGreenhouse gasEconomyFinance

Abstract

fetched live from OpenAlex

Abstract This paper studies the effects of carbon pricing on inflation dynamics. We construct a sample of carbon taxes implemented in Europe and Canada over three decades and estimate the response of inflation and price components to carbon pricing. Our empirical results suggest that carbon taxes did not significantly increase inflation, with dynamic effects estimated around zero in most specifications. Instead we find support for relative price changes, increasing the cost of energy but leaving the price of other goods and services unaffected. This is consistent with previous findings on the limited aggregate economic costs of carbon taxes. Based on the cross-section of taxes in Europe, we provide suggestive evidence that the response of inflation was especially muted in countries with revenue-neutral carbon taxes and autonomous central banks that can accommodate potential inflationary pressure associated with carbon pricing.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.212
Teacher spread0.161 · 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".

Quick stats

Citations88
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the European Economic AssociationSame topicClimate Change Policy and EconomicsFrench-language works237,207