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Record W4303431758 · doi:10.21834/ebpj.v7isi8.3920

Laws Governing Renewable Energy Production of Malaysia and Canada

2022· article· en· W4303431758 on OpenAlexaboutno aff
Efy Azirah Majid, Nur Alia Zaheera Mohd Faizal, Ain Nursyahirah Mohd Fadzir, Siti Aishah Rizal

Bibliographic record

VenueEnvironment-Behaviour Proceedings Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyLicenseInefficiencyBusinessProduction (economics)Environmental economicsEnvironmental resource managementPolitical scienceEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

The transition of the usage of fossil fuels to renewable energy after centuries of technological and economic support requires a set of efficient legal frameworks. Achieving widespread implementation of renewable energy involves substantial challenges, namely inefficiency of policies, lack of financial assistance, and unsecured economic instruments. Adopting the comparative method and research interviews, this research compares the comprehensiveness of the laws governing renewable energy in Malaysia with Canada. Further, to critically analyze the correlation on the effectiveness of legal frameworks on the production of renewable energy and to provide critical insight into the legal frameworks governing renewable energy in Malaysia. Keywords: law; renewable energy; Malaysia; Canada eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI8.3920

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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