Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach
Bibliographic record
Abstract
This article explores the ongoing green transition in the energy sector in EU countries. The greening process is brought about by the growth of the Renewable Energy Sources (RES) sector and Green Jobs (GJ). The goal of this paper is to find out how certain factors in the RES sector affect the creation of GJ. This study uses Quantile Regression for Panel Data (QRPD), a method that addresses fixed effects. Based on secondary data from Eurostat and EurObserv’ER reports, the model was made for the EU27 countries for the years 2013–2020. The impact of the adopted variables on GJ generation is heterogeneous. Significantly, the volume of turnover in the RES, across the entire studied cross-section, influences the increase in GJ number. It is also observed that, in the case of economy-wide R&D expenditure, a negative impact on GJ creation is observed. In contrast, interestingly, in the case of R&D expenditure in the business sector, a positive effect on GJ formation is noted. A possible direction for research into the topic of GJ in the RES should be qualitative research, which could provide additional information regarding, for example, the degree of the greening of such jobs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".