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The Features of the Development of the Green Economy of the People’s Republic of China

2023· article· en· W4388493696 on OpenAlexaboutno aff
Vladyslav A. Varvashenko, Igor Matyushenko

Bibliographic record

VenueBusiness Inform · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsGreen economyChinaGreen developmentGreen growthInvestment (military)BusinessEconomyEconomic systemEconomicsSustainable developmentPolitical sciencePolitics

Abstract

fetched live from OpenAlex

The article is aimed at assessing the current state of development of the green economy of China and providing recommendations for the development of the economy of such kind. As a result of the study, the definition of the «green economy» was analyzed, information was collected on the origins and development of the conception of the green economy, which covers economic, environmental, and social factors. The place of the People’s Republic of China among the selected countries (Canada, the USA, Germany, Japan, Turkmenistan, Ukraine) according to certain indicators of the green economy, in particular: CO2 emissions, the use of renewable energy sources, research and development costs, air pollution called PM2.5, total greenhouse gas emissions were substantiated. The article also compares the indicators of the green economy in the PRC with the average for all countries of the world. Recommendations for further development of the green economy in the PRC are provided, in particular, it concerns: increasing the volume of construction of environmental protection infrastructure and improving the environmental protection system; increasing investment in education and focusing on human capital; strengthening financial support for the green economy; improvement of the mechanism of patenting inventions and transformation of scientific and technological achievements; combination of the economic and the green development; deepening reforms and opening up to improve the quality of foreign investment. Prospects for further research in this direction are the assessment of the green economy of the PRC in the regional context on the basis of entropy along with systematization of existing indicators, as well as the construction of an own system of indices to assess the level of development of the green economy. Based on the calculated results of the assessment, it would be advisable to provide appropriate policy recommendations for each region of China in order to further develop the green economy.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.188
Teacher spread0.171 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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