Paving the way for sustainable green growth in G10 economies: Perspectives on green manufacturing employment and renewable energy employment
Bibliographic record
Abstract
This study probed the contributions of green manufacturing (GM) and green energy (GE) employment to green growth (GRG) in the group of ten economies spanning 2000 to 2022. The study also explored the moderating implications of energy, economic, and information and communication technology (ICT) diversification, as well as energy uncertainties. Insights from the method of moments quantile regression estimator underscore notable heterogeneous effects over the distributions of GRG, typifying cross-sectional nuances, and varying degrees of green job adaptations. Findings established that GM employment enhanced GRG more substantially in France, Germany, Netherlands, Switzerland, and the US. In contrast, GE employment contributed to GRG, mainly in Canada, Japan, Sweden, and the US. This underscores the potential of green jobs as a green growth-enhancing factor in G10 countries. Hence, these countries are encouraged to adopt these non-traditional decent jobs to attain sustainable development goals 7 and 13. Improved financing, tax holidays, and other administrative incentives could be extended to all organizations championing this paradigm shift in the work environments. Likewise, these countries should improve the depth of energy, economic, and ICT diversification to harness their full potential for environmental progress. Not least, G10 countries must ensure self-sufficiency in energy supply to reduce the adverse implications of energy uncertainties on green growth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".