Experience in the Implementation of Regional Concepts for the Development of the Circular “Green” Economy of the Countries of the World
Bibliographic record
Abstract
The purpose of this study is to determine in which directions the measures of the “green” economy are being implemented in foreign countries. To achieve this goal, the concept of a “green” economy and a “circular” economy was given; national strategies of the “green” economy of foreign countries were studied; the main directions of “green” regional initiatives were identified; measures taken in foreign countries in the selected areas of the “green” economy were compared. The subject of the study is foreign strategies for the development of the “green” economy of the region. The object of research is the “green” economy. The authors apply methods of empirical research (description and comparison); general logical (analysis of information, its synthesis and generalization, as well as abstraction); theoretical cognition (hypotic-deductive; ascent from the abstract to the concrete). The article considers examples of the implementation of foreign national concepts for the development of a “green” economy, which are associated with regional initiatives of Germany, the United Arab Emirates, Canada, Belgium and the United States of America. The analysis is made on the basis of national strategies of the “green” economy, directions and initiatives. It is concluded that these examples of the implementation of the “greening” of the economy should be taken as an example for other countries, since work in this direction can improve the environmental situation, the quality of life of the country and a particular region.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".