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Defining Major Oil and Gas Companies’ Development Strategies in the Era of Energy Transition

2021· article· en· W3209411141 on OpenAlexaboutno aff
Valery I. Salygin, Daniil Lobov

Bibliographic record

VenueMGIMO Review of International Relations · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFossil fuelRenewable energyChinaScale (ratio)Sustainable developmentPetroleum industryEconomic growthEconomicsGeographyPolitical scienceEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

The energy market today is turbulent. Nations follow different energy trends and shape their policies towards Energy Transition and sustainable development. To avoid risks and pursue opportunities, oil and gas companies must adapt their longterm strategies to macro-trends and national regulations.The study's objective is to investigate how major oil and gas companies’ development trends correlate with trends and strategies at the national level. The hypothesis is that oil and gas companies’ operations and innovation portfolios are linked to national energy mixes and environmental regulations. To do this, the authors examined the energy markets of 54 countries with the focus on Brazil, Canada, China, EU, Norway, Russia, Saudi Arabia, the UK, the USA, operational indicators, and innovation development trends of 18 major oil and gas companies. The production volumes have been translated into an ordinal scale and analyzed with the use of Spearman correlation.The study confirmed a weak correlation between oil and gas companies' operational indicators and national strategies. Companies operating in countries with strict environmental regulations, primarily in the European Economic Area, have been more likely to adapt their businesses to energy transition while building up oil and gas production; they also have had more diversified innovation portfolios. As more countries moved towards later generations of environmental regulations, the increase in renewable energy investments was found in more oil and gas majors.

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 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 score0.779
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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