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Record W2943499476 · doi:10.1007/978-3-030-15577-3_73

Will Carbon Tax Constrain Oil Production in Canada?

2019· book-chapter· en· W2943499476 on OpenAlexaboutno aff
I. Kopytin, A. Maslennikov, M. Sinitsyn, S. Zhukov, С. А. Золина

Bibliographic record

VenueSmart innovation, systems and technologies · 2019
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)Natural resource economicsEnvironmental scienceBusinessEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

The article aims to assess how introduction of carbon tax will impact oil production in Canada in the long run. Two oil exporting countries, Norway and Canada, introduced carbon tax in 1991 and 2018 respectively. In Norway carbon tax has not constrained oil production and development of costly hydrocarbon reserves in the Arctic areas. We build a simple econometric model for Canada’s oil demand and supply until 2040 in reference and low carbon scenarios. Carbon tax is explicitly inbuilt into the model based on the assumption that producers fully pass costs of carbon tax onto consumers of petroleum products. Demand is modelled bottom-up individually for economic sectors, including road transport, air transport, marine and water transport, industry, commercial sector, etc. On the basis of modelling results we argue that in the projection period carbon tax will have a minor constraining impact on oil production growth in Canada. Demand for petroleum products will contract more deeply compared to crude oil production. The continuously increasing export orientation of the Canadian oil sector will partially shield it from the carbon tax. Given the global advancement of low carbon paradigm, analysis of Norway and Canada experience with carbon tax is crucially important for all large oil producing countries.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.201
Teacher spread0.149 · 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 teacher head, not a consensus.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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