Bibliographic record
Abstract
The global energy sector is in a period of transition, during which time it is expected that renewable and low-carbon energy sources, such as wind and solar, will replace traditional fossil fuels, including oil, gas, and coal. The energy transition is happening not only to limit the environmental impact of fossil fuel production and consumption but also to ensure energy security, reliability, access, affordability, and sustainability. The importance of the energy transition has been amplified by recent events, notably the Russian-Ukraine conflict. Economic, financial, and trade sanctions against Russia, and in particular its oil and gas industry, have forced countries to find new suppliers in the short term, but also to investigate new and more sustainable sources to guarantee long-term energy security. Given the importance of energy, it is perhaps not unexpected that there is a considerable body of recent academic literature, particularly over the last 4–5 years, studying what industries, consumers, governments, and markets can do to help bring about a faster energy transition. In this paper, we provide a review of the literature that pertains to the economic aspects of the energy transition. While our initial search of the literature is targeted at uncovering all relevant articles on the subject, we focus most of our discussion on the most influential articles in prominent journals and articles published in this journal—Energies. This review is intended to help identify active topics and potential research gaps and provide future direction, so we hope it will prove useful to the readers and authors interested in this topic.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".