Energy Sector’s Green Transformation towards Sustainable Development: A Review and Future Directions
Bibliographic record
Abstract
The energy sector’s green transformation recently gained major scientific attention, due to the role of the energy sector in the economy. The energy sector, similarly to the other economic sectors, faces sustainable development (SD) challenges. This review paper’s goal is to explore the areas of the green energy sector transformation towards SD context distinguished in the scientific literature review. The adopted method in this paper is bibliometric research of the scientific publications indexed in Scopus. There were two original queries formulated, and their results were analyzed in the VOSviewer program in the form of bibliometric maps and tables. A comparison of the proposed original queries’ results points to the importance of the journal subject area indexed in the Scopus database. There are publications important for the energy sector green transformation not included in the energy subject area in this database. The vast number of publications dealing with cross-disciplinary subjects revolving around green transformation in the energy sector is the cause of the multiple side topics covering the areas of the SD. The study identifies keyword-specified areas around the topic of green transformation towards SD in the energy sector. In this study, the limitations of the employed methods and the theoretical, methodical, and empirical implications of the research were presented. Presented results can inspire other researchers who are looking for a research gap or describing the state of the art. The future possible research avenues were also addressed.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".