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Record W4402286435 · doi:10.55016/ojs/sppp.v11i1.53017

Energy and Environmental Policy Trends: The Growing Opportunity for LNG in China

2018· article· en· W4402286435 on OpenAlexaff
Jennifer Winter, Sam Green

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChinaEnergy (signal processing)Natural resource economicsEnvironmental policyEconomicsEnvironmental protectionBusinessEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

THE GROWING OPPORTUNITY FOR LNG IN CHINA Natural gas consumption in China has been growing rapidly since 2006 and was 237 billion cubic metres in 2017. Domestic natural gas production has not kept pace, leaving substantial opportunity for LNG importers. In 2016 the Chinese National Development and Reform Commission released the 13th Five-Year Plan for the Natural Gas Industry, which set a goal of natural gas accounting for 10% of total primary energy consumption by 2020. China’s total energy consumption in 2016 was equivalent to 3,053 million barrels of oil; natural gas accounted for 189.3 million barrels of oil equivalent, 6.2% of total energy consumption.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.890
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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