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Record W4387983349 · doi:10.3366/gels.2023.0090

Making an Energy Transition Happen – Implementing Net Zero Commitments

2023· article· en· W4387983349 on OpenAlexaboutno aff
Peter Cameron

Bibliographic record

VenueGlobal Energy Law and Sustainability · 2023
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsNet (polyhedron)Zero (linguistics)Transition (genetics)Net energyZero-energy buildingEnergy transitionEnergy (signal processing)BusinessPhysicsMathematicsChemistryQuantum mechanicsMedicineBiologyLinguisticsGeometry

Abstract

fetched live from OpenAlex

The theme of this double issue of Global Energy Law and Sustainability is the implementation and fleshing out of Net Zero policies.Targets have been set, plans have been made and much has been said.For some, too much said, and too little done.Yet, it is now clear that many governments, legislatures and regulators around the globe are taking serious steps to tackle the specific challenges these commitments entail.From the US Inflation Reduction Act to a swathe of measures taken by the European Union, and many other, less wellknown, measures taken by large and small nations from India to Canada and Australia to Morocco, action on Net Zero has been evident.Often, it has been startling in its vigour and innovation.Yet these actions are being shaped by a geopolitical climate that is very different from that of 2015 when nearly 200 states signed up to the Paris Agreement.To a greater or lesser extent, they are influenced by the elevation of energy security to the top rank of the public policy agenda.Rarely, has it ever occupied such a pole position.Its early impacts have been subject to very different kinds of interpretation.For some, the short-term measures taken by certain governments to address shortages, actual or perceived, and price hikes, all too real for many citizens, have indicated a departure from the Net Zero commitments that the same governments entered into.Indeed, a few of these measuressuch as the conclusion of long-term natural gas contractsare likely to have effects that embed fossil fuels in their economies for many years ahead, giving support to such a view.Others, however, have noted how the largely domestic base of renewable energy supplies has given it an advantage in a context of government anxiety about import dependence.Experts, including the OECD's International Energy Agency, have been optimistic about the medium to long term shift towards cleaner energy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.022
GPT teacher head0.323
Teacher spread0.300 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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