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Record W4391261667 · doi:10.54097/kwqjj477

The Impact of Green Consumerism on The Chinese Commodity Market

2023· article· en· W4391261667 on OpenAlexaff
Jingtian Shi

Bibliographic record

VenueHighlights in Business Economics and Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsumerismCommodityCommodity marketBusinessCommerceEconomicsMarket economyFinance

Abstract

fetched live from OpenAlex

Recently, the idea of sustainable products has gained popularity all around the world. The market for environmental protection products is expanding as a result of the weakening of the economy, the worsening of global environmental issues, and the increase in public awareness of environmental protection. Environmental consciousness is growing in China, and the government has boosted the promotion of green products. The commodity markets in China are being impacted by green consumerism. This report, based on literature research, evaluates the influences of green consumerism on consumer behavior, the second-hand market, and individual firms in China. The findings indicate that China's commodity market has undergone a significant transformation as a result of eco-friendly consumerism. The second-hand market has grown a lot; the green management of companies has accelerated; social media and government policies have played a huge role in promoting the concept of environmentally friendly consumption among individuals and companies. However, the market for sustainable consumption in China is still in its infancy. There are still numerous issues that need to be resolved, such as green premiums and false environmental propaganda. In the future, as environmental consumerism intensifies, the commodity market in China will experience more significant adjustments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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