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Record W4225126457 · doi:10.33002/jelp02.01.01

Transition to Sustainable Energy as a Tool for Decarbonisation in Nigeria: Regulatory Challenges

2022· article· en· W4225126457 on OpenAlexvenueno aff
Izuoma Egeruoh-Adindu

Bibliographic record

VenueJournal of Environmental Law & Policy · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersGovernment of the United Kingdom
KeywordsRenewable energyElectrificationFossil fuelNatural resource economicsClimate change mitigationBusinessEnergy transitionEfficient energy useGreenhouse gasSustainable developmentEnergy mixClimate changeDeveloping countryEnvironmental economicsEconomicsEconomic growthElectricity generationEngineeringElectricityPolitical scienceEcology

Abstract

fetched live from OpenAlex

Global energy mix is shifting from fossil fuels to renewable energy. Most developed nations are working towards decarbonizing their economy while ensuring sustainable energy. This energy transformation is also expected to gain momentum in the developing world as new ecosystems are forming and new technologies are emerging. These developments in technology that developed nations have keyed into are helping to grow renewable, develop new energy carriers, improve energy efficiency, reduce emissions and create new markets for carbon and other by-products as part of an increasingly circular economy. At COP26, it was made compulsory for developing countries to transition from fossil fuels to a decarbonized economy. Climate change financing is now viewed as part of adaptation, mitigation and economic development measures. These measures are expected to help reduce the harsh effects of global climate change. This is not the case in Nigeria where many of these commonly pursued steps to decarbonisation, such as increased electrification, wide-scale use of renewable energy and intensifying energy efficiency measures are mired by regulatory challenges. This article using doctrinal research methodology aims to explore how developing countries like Nigeria with heavy reliance on fossil fuel can accelerate decarbonisation over the next decade and achieve the timelines for the 2030 National Determined Contributions (NDCs), which has been advanced from 2025 to the end of 2022 at the COP26.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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