Bibliographic record
Abstract
This article analyzes Green Jobs (GJs) in the energy sector. GJs are naturally created in the processes related to the implementation of the Sustainable Development Goals (SDGs); this is especially visible in the 7th and 8th SDGs. There is currently a green transition from fossil fuels to renewable energy sources in the energy sector, and this mainly technological change also influences GJ creation. Despite this, there is a research gap related to green self-employment and GJ definitions. The goal of this paper is to explore the scientific literature collected from the Scopus database using a qualitative approach to present areas and keywords related to GJs in the energy sector. The adopted method is a Structured Literature Review (SLR), with the original query Q1. The retrieved data results of the SLR method were analyzed in the form of bibliometric maps of co-occurring keywords generated by the VOSviewer software, together with tables showing clusters of keyword features. As a result, the pivotal keywords and their clusters were identified. In this study, the most important scientific areas of GJ research in the energy sector were also indicated. This paper presents the current state of knowledge and the evolution of the subject of GJs in the energy sector, which can be useful for both researchers and practitioners. In the last section of this paper, possible new directions of future studies on the subject of GJ creation in the energy sector are identified. The limitations of this research and its practical implications are 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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".