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Record W4413872354 · doi:10.5267/j.ijiec.2025.6.004

Research on the decision-making of remanufacturing transformation considering the standardization of recycler under carbon tax policy

2025· article· en· W4413872354 on OpenAlexvenueno aff
Tianchen Yang, Bin Hu, Shi Lihua

Bibliographic record

VenueInternational Journal of Industrial Engineering Computations · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
FundersNational Office for Philosophy and Social Sciences
KeywordsRemanufacturingStandardizationTransformation (genetics)BusinessManufacturing engineeringIndustrial organizationAccountingComputer scienceEngineeringChemistry

Abstract

fetched live from OpenAlex

Under the backdrop of “dual carbon” targets, the remanufacturing of waste products has attracted much attention from the government because it contributes to reducing carbon emissions in production and alleviating environmental pollution. This study formulates a four-party evolutionary game model of “government-manufacturer-recycler-consumer” to explore in depth the effects of carbon tax, recycling regulations, and consumer environmental awareness on remanufacturing decisions. The study results show that: The recycling standardization practiced by recyclers positively influences whether manufacturers actively engage in remanufacturing, and manufacturers in different industries have varying sensitivities to recycling standardization; (2) Carbon tax policy is conducive to promoting manufacturers’ active remanufacturing, but manufacturers in different industries have varying sensitivities to carbon taxes; (3) Consumer environmental awareness significantly influences recycling standardization, and the extent of this influence is consistent across different industries; (4) Strict government regulation of recyclers and consumers can incentivize manufacturers, especially those in industries lacking carbon emission advantages, to actively remanufacture under conditions of low-carbon taxation. Based on the above conclusions, this study proposes targeted strategies to offer reference for promoting the low-carbon development of the manufacturing industry.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.351
Teacher spread0.306 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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