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Record W7135591968

Environmental taxes in OECD countries

2013· dissertation· cs· W7135591968 on OpenAlexaboutno aff
Martina Franková

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2013
Typedissertation
Languagecs
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon taxEnvironmental taxRevenueTax revenueTax deferralTax reformIndirect taxGreenhouse gas
DOInot available

Abstract

fetched live from OpenAlex

The object of the diploma thesis is to analyse the environmental taxation in the OECD countries, to inform the readers about the development of environmental taxes and current trends in tax revenues from environmental taxes. The thesis is also focused on the structure of revenues from environmental taxes, the significant part is created by energy taxes, especially by taxes on motor fuels. Attention is also paid to taxation of carbon dioxide, according to the OECD the carbon taxes are one of the effective tools to reduce CO2 emissions, which is important to achieve the targets set under the Kyoto Protocol. The explicit carbon taxes are applied in the 12 tax systems of 12 OECD member countries and in the Canadian province of British Columbia. An increase of the countries applying carbon taxes since 2010 demonstrates the increased demand for this tool in recent years. The object of the last part of the thesis is to analyse whether declining tax burden on labour while increasing tax burden on energy is put into practice.

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)0.002
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 score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.016
GPT teacher head0.218
Teacher spread0.203 · 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
Published2013
Admission routes1
Has abstractyes

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