The role of circular economy in EU entrepreneurship: A deep learning experiment
Bibliographic record
Abstract
Fostering innovation is one of the key roles of the Circular Economy (CE) that applies also to European Union (EU) firms, because entrepreneurs are persistently seeking new ways and means to create values, contributing with significant market opportunities, and depicting large potential for EU sustainable growth. This study explores the effects of firms’ investments in using highly disruptive technologies in the energy sector on the Eurozone (EU-27) in the last two decades (1990–2019). An Artificial Neural Networks (ANNs) experiment through a Deep Learning (DL) approach is implemented to test this hypothesis. The empirical findings show that investments in highly disruptive technologies, especially by large digitally qualified companies, boost economic growth. They are also a crucial driver of digitalization not only because they enhance a wide strategic change implying a radical innovation in business models, but they completely transform markets, from energy to food production, water resources, pollution, connectivity, and plastic waste. These expected benefits represent a possible policy measure to offset the decline in global activity due to the impact of the Russia-Ukraine war on global energy markets. In addition, a positive association between trade and output is confirmed. Finally, promising policy actions are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".