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Shackles or Insurance? -- A Comparative and Political Economic Study of Carbon Taxation

2024· article· en· W4390564950 on OpenAlexaff
Guanjin Lu

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2024
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTariffLegislaturePoliticsPromotion (chess)Carbon taxValue (mathematics)EconomicsPublic economicsPoint (geometry)Environmentally friendlyConsumption (sociology)BusinessGreenhouse gasInternational economicsPolitical scienceLawSociology

Abstract

fetched live from OpenAlex

Although the carbon tariff and taxation are not a new initiative, the up-to-now debate is wide around the world since EU has initiated the carbon emission act. Main concerns regard to whether the taxation is imposed as a restriction and bondage or a diversion and promotion for environmentally-friendly industries. This paper will go through scholarly exploration on both sides. Specifically, historical reviews on carbon tariffs will shed light on the nature of this protocol in regulating environmental-friendly manufacturing and traditional energy consumption. The second chapter will adopt political economic point of view to analyze the value of carbon tariff, and its cultural impacts will also be taken into consideration. The concluding segment shall furnish a limited assortment of alternative avenues, thereby affording an array of choices to those tasked with making pivotal determinations. In general, this paper is a legislative analysis on carbon tariff, findings suggest that future practices and comparative policy should been strictly taken in different communities.

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)0.002
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0050.007
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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