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Record W2075026086 · doi:10.1093/reep/reu017

Regulation of Natural Gas in the United States, Canada, and Europe: Prospects for a Low Carbon Fuel

2015· article· en· W2075026086 on OpenAlexaboutno aff
Jeff D. Makholm

Bibliographic record

VenueReview of Environmental Economics and Policy · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNatural gasFossil fuelNatural gas pricesNatural resource economicsEconomicsDownstream (manufacturing)Greenhouse gasPipeline transportCoalInternational tradeCommerceBusinessEnvironmental scienceWaste managementEngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

The United States and Canada have seen a competitive and technological revolution in unconventional natural gas production in the 21st Century—dramatically lowering the price of gas and displacing high-carbon coal with low-carbon gas for power generation. This gas revolution came from an earlier revolution in the regulation of gas pipelines, which ended the obstruction of gas markets by pipeline interests. Neither revolution has spread to Europe, where increasingly protectionist EU legislation has effectively blocked competitive pipeline entry and related gas markets. As a result, unconventional gas is untapped, coal displaces gas for power generation, and oil-linked gas prices have cost EU consumers a staggering $425 billion more than their US counterparts have paid since 2009 for about the same quantity of gas. Europe faces a serious institutional challenge to adopting the kind of pipeline regulation that facilitates the competitive flow of natural gas supplies and the accompanying lower carbon emissions. (JEL: D23, K23, L14, L51, L95, N70, Q54)

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.072
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.006
Scholarly communication0.0070.003
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.196
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2015
Admission routes1
Has abstractyes

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