The influence of COVID-19 on the transition to a more circular economy in oil-exporting countries
Bibliographic record
Abstract
The present study has attempted to systematically explore the impact of the COVID-19 pandemic on transitioning to a more circular economy in 15 major oil-exporting countries. These countries are being explored because they deliver the highest environmental impact. Apart from the comprehensive literature review, the authors interviewed the group of 32 individuals having sufficient knowledge on the subject. The respondents provided their opinions on the main challenges that impacted the move to a more circular economy in oil-exporting countries during the COVID-19 pandemic, addressed the reasons for these challenges and suggested ways to respond to them. The consensus among the respondents was that the pandemic has slowed the transition process down and there is an urgent need to resume it. Their opinions on other topics were different, but not contradicting. Also, in addition to the frequently discussed topics, the respondents addressed those usually insufficiently considered, namely the pursuit of a luxurious lifestyle and scepticism towards relevant concepts and policies in many countries under consideration. The paper finishes with a set of recommendations aimed at early resumption and intensification of efforts on transitioning to a more circular economy in oilexporting countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".