Exploring the key role of education in achieving green growth: evidence from group of seven countries
Bibliographic record
Abstract
Given the detrimental environmental impacts of anthropogenic activities, achieving a higher green growth is inevitable for the whole world. Therefore, this study examines the role of education to enhance green growth in G7 nations (USA, UK, Germany, Italy, France, Canada, and Japan) while controlling the impact of capital, trade, and FDI. To proceed for regression estimations, unit root tests confirm the stationarity of all variables at first difference, while Westerlund cointegration test shows the existence of cointegration. Findings from the augmented mean group (AMG) method reveal that education capital, and trade have a favorable influence on green growth while FDI has an adverse linkage with green growth. Based on empirical outcomes, the study provides policy suggestions for G7 economies to achieve SDG-4 (quality education), SDG-8 (decent work and economic growth), and SDG-13 (climate action). The study suggests G7 economies to implement eco-friendly trade and investment policies, enhancing education, and incentivize sustainable production to improve green growth and meet SDGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".