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Record W4401349260 · doi:10.2118/221702-ms

Nigeria's Energy Transition Plan: A Technical Analysis, Opportunities, and Recommendations for Sustainable Development

2024· article· en· W4401349260 on OpenAlexaff
Wilson Ekpotu, Joseph Akintola, Queen Moses, Martins Obialor, Edose Osagie, Imo-Obong Utoh, Joseph Akpan

Bibliographic record

VenueSPE Nigeria Annual International Conference and Exhibition · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPlan (archaeology)Sustainable developmentEnvironmental economicsComputer scienceBusinessPolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract This work analyses recent Nigeria's Energy Transition Plan (ETP) and its technical assessment to improve the incorporation of Net-Zero Energy Systems (NZES) for the purpose of sustainable energy development in Nigeria. Nigeria is currently at a crucial phase of its energy development, with the goal of shifting towards a more sustainable and ecologically aware energy model. This study assesses the existing ETP, with a specific emphasis on crucial elements including the incorporation of renewable energy, upgrading of the power grid, implementation of energy storage systems, and the establishment of policy frameworks. The objective is to provide strategic suggestions to strengthen Nigeria's energy transition and promote sustainable energy development based on Net-Zero Energy Systems. Given that Power, Oil and Gas, Manufacturing, Cooking, and Transportation industries collectively account for 65% of Nigeria's overall emissions, a streamlined transition framework would facilitate the reduction of emissions from these sectors and the development of sectors associated with solar, hydrogen, and electric cars, expediting the implementation of renewable energy. Important factors to consider include the variety of energy sources used, government financial support for renewable energy, additional capital expenditure for funding clean energy production, and the improvement of infrastructure, resulting in substantial cost reductions for the adoption of renewable 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.266
Teacher spread0.236 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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