MétaCan
Menu
Back to cohort
Record W4392232099 · doi:10.18280/ijsdp.190205

Renewable Energy Research in Africa: A Bibliometric Review (1979-2022)

2024· review· en· W4392232099 on OpenAlexvenueno aff
Noureddine El Moussaoui, Ali Lamkaddem, Yassine El Alami, Yahya El Hammoudani, Sofian Talbi, Mustapha Faraji, Faouzi Lakrad, Tarik Mrabti, Ahmed Faize, Elhadi Baghaz

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEnvironmental scienceNatural resource economicsEnvironmental economicsEngineeringEconomics

Abstract

fetched live from OpenAlex

This paper presents a bibliometric analysis of scientific research on renewable energy in Africa, a rapidly growing field driven by the need for sustainable and accessible energy solutions.It highlights Africa's role as a hub for renewable energy innovation, with local researchers collaborating with global institutions to address the continent's unique energy challenges.This synergy is enhancing energy access in African communities and contributing to the global advancement of renewable technologies.The study meticulously examines 3,109 scientific publications from the Scopus database, spanning from 1979 to 2022.It analyzes the evolution, geographical distribution, and impact of these publications, with a focus on international collaborations and scientific output.Findings indicate that South Africa leads in productivity with 962 publications and hosts the top affiliating institutions in this domain.The study also reveals an exponential increase in renewable energy research, particularly from South Africa, the United States, and the United Kingdom, emphasizing the need for more collaborative efforts and knowledge exchange globally.This analysis provides critical insights into the current landscape of renewable energy research and pinpoints areas ripe for further exploration and development in this vital sector.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.883
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.013
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.105
GPT teacher head0.372
Teacher spread0.266 · 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
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicEnergy and Environment ImpactsFrench-language works237,207