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Record W3033497119 · doi:10.1111/jpet.12450

Environmental certification programs: How does information provision compare with taxation?

2020· article· en· W3033497119 on OpenAlexaff
Andrea Podhorsky

Bibliographic record

VenueJournal of Public Economic Theory · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsYork University
Fundersnot available
KeywordsCertificationCompetition (biology)Monopolistic competitionQuality (philosophy)Environmental qualityEconomicsFree entryPublic economicsBusinessMicroeconomicsIndustrial organizationMonopoly

Abstract

fetched live from OpenAlex

Abstract This paper develops a monopolistic competition framework to assess whether environmental certification programs can serve as effective substitutes for more traditional policy instruments such as environmental taxation or a minimum quality standard (MQS). I show that if firms can organize themselves and choose the certification standard collectively, then there is a beneficial role for a regulator to intervene. Also, the degree of substitution between differentiated goods that impose environmental damage and a “clean” outside good, the degree of competition in the industry and the extent of environmental damage caused by minimal quality goods are important considerations in the choice between a certification program and a tax or a MQS. While the comparison between a certification program and a tax depends on numerous factors, I find unequivocally that certification is a poor substitute for taxation whenever the outside good is a close substitute for differentiated goods, there is a high degree of competition in the industry or if minimal quality goods impose considerable environmental damage.

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)0.018
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.108
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.004
Scholarly communication0.0070.009
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0180.001

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 head0.039
GPT teacher head0.200
Teacher spread0.160 · 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 designTheoretical or conceptual
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

Citations11
Published2020
Admission routes1
Has abstractyes

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