Innovation and Management of Smart Transformation Global Energy Sector: Systematic Literature Review
Bibliographic record
Abstract
The acceleration of globalisation processes and increasing countries’ energy interdependence are required to ensure national energy security and independence. That demands investigating and developing processes and approaches for sustainable transformation of the global energy sector. The article aims to perform a complex review and investigation of the academic environment to analyse the trends and features of scientific publications devoted to new trends and tendencies in the smart energy industry transformation. To provide a categorical and theoretical background on the key scientific publications’ trends, the paper conducted a bibliometric analysis of scientific publications about smart energy management and sustainable energy sector. The subject of investigation is publications on smart energy management and the sustainable energy sector. The article represented the results of bibliometric analysis using the Scopus tools analytics and VOSViewer tools. The investigation answered the central question of the key academic and research tendencies in the smart energy development and sustainable transformation field. Thus, qualitative, and quantitative trends describe the academic tendencies to spread smart and sustainable technologies in the energy industry. Using the Scopus scientometric database, a system of more than 5000 academic texts in the determined area was created from 2001 to 2022. Such countries as India, China, the USA, the UK, Germany, Italy, Canada, South Korea, France represent the analysed scientific area. Describing the key trends and clusters has allowed understanding and systemised the dominant trends in the development of scientific publications in the field of management of sustainable development processes, spreading the IOT processes, and renewable energy.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".