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Record W3194160530

Circular Economy in China: Towards the Progress

2021· preprint· en· W3194160530 on OpenAlexaboutno aff
Haradhan Kumar Mohajan

Bibliographic record

VenueMunich Personal RePEc Archive (Munich University) · 2021
Typepreprint
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsChinaScarcityCircular economyUrbanizationWorld economyPopulationQuarter (Canadian coin)EconomyEnvironmental degradationBusinessWorld populationEconomicsNatural resource economicsDevelopment economicsEconomic growthDeveloping countryGeographyMarket economyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

At present all nations are thinking about the circular economy (CE) in production, circulation, and consumption due to environment pollution and resource scarcity. But implementation of CE policy is yet in its infancy. The CE in the form of waste management policy that is achieved in selected developed countries of the world. China is a developing country in the East Asia. The economy of China has grown with an average 10% per annum during the last 30 years that contributes important impacts on the world economy. At the last quarter of the 20th century and beginning of the first quarter of the 21st century China becomes the largest energy user in the world, as the country rapidly becomes the largest exporter in the world. To produce essential commodities according to the global demand the country mainly depends on coal and fossil fuel to create electricity, consequently it emits maximum CO2. Recently, China has faced various harmful odd situations, such as environmental degradation, human health and social problems due to huge population, and source scarcity for the huge production, rapid continuous unplanned urbanization, and growing economy. Thinking for future sustainable economy and human welfare of the country, China is attracted by the CE. The country has taken various attempts to implement CE at the three levels at a time, namely micro, meso and macro levels.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.202
Teacher spread0.186 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

Explore more

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