Determinants of Circular Economy: An Empirical Approach in the Context of the United States of America
Bibliographic record
Abstract
The USA is the world’s largest economy in terms of the consumption of resources. The excessive and irresponsible consumption of resources in the developed countries has jeopardized the stock of global resources. This quantitative study highlighting the importance of the circular economy (CE), has assessed the factors that would support the circular transition in the USA. Time series analysis based on the Autoregressive Distributed Lag (ARDL) model was employed to analyze the impact of Gross Domestic Product (GDP) per capita, Research and Development expenses, and Renewable Energy consumption on circular economy in the US with annual data from 1971 to 2017. While the study indicated the existence of a long-run relationship between the GDP per capita and renewable energy consumption, no relationship was observed between research and development expenses and the circular economy. The study strongly emphasizes the need for policy interventions to enhance the level of awareness regarding circular economy, increase consumption of renewable energies and steering investments in research and development activities to support CE activities in the USA.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".