Unveiling Energy Finance Market: A Bibliometric and Content Analysis
Bibliographic record
Abstract
This paper unveils the nexus of the energy finance market and its significant dynamics. The results exhibit potential research areas, dominating research patterns and interlinkages among them. Our sample consists of 927 articles selected from the Scopus database for the sample period of 1972–2024. We present the quantitative performance of top articles, journals, authors, countries, and institutions. The result includes keyword co-occurrence analysis and co-authorship analysis for authors and countries. We include a literature review of the top 20 cited articles and the most followed methodologies. We found five themes, four clusters, and thirty-four future research questions, showing potential areas of research in the energy finance market. Additionally, based on our results, we proposed a theoretical framework of five major independent factors impacting the energy finance market. This novel study provides a comprehensive picture of the energy finance market, covering a vast period using Scopus as a database, underscoring the prevalent research patterns and serving financial practitioners, researchers, and policymakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.181 | 0.179 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".