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Experience in the Implementation of Regional Concepts for the Development of the Circular “Green” Economy of the Countries of the World

2022· article· en· W4312207918 on OpenAlexaboutno aff
A. N. Alekseeva, Lybov Achba, N. V. Ostrovskaya

Bibliographic record

VenueAdministrative Consulting · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Business Development Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGreen economyEconomyCircular economyEconomic systemWorld economyNational economyBusinessEconomicsPolitical scienceSustainable development

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.008
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.122
GPT teacher head0.338
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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