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Record W4214814806 · doi:10.1080/13675567.2022.2047621

Cooperate or compete? A strategic analysis of formal and informal electric vehicle battery recyclers under government intervention

2022· article· en· W4214814806 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Logistics Research and Applications · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBusinessIntervention (counseling)Government (linguistics)Economic interventionismElectric vehicleBattery (electricity)Industrial organizationEnvironmental economicsOperations managementMarketingPower (physics)EconomicsPoliticsPsychology

Abstract

fetched live from OpenAlex

We propose a dual-channel reverse supply chain consisting of a formal and an informal electric vehicle battery recycler under government intervention with a subsidy-and-penalty policy. By comparing and analyzing the equilibrium results, profits, and environmental impacts, we discuss the two members’ competition and cooperation strategies under government intervention. Analytical results show that government subsidy to the formal recycler (penalty to the informal recycler) increases (decreases) the two members’ recycling prices at differed rates. Government intervention increases (reduces) the recycling quantity in the formal (informal) recycling channel. The two members can reach a consensus on cooperation only if the subsidy and penalty are set at sufficiently high levels. In this case, the government should push them up as high as its budget allows for environmental benefit.

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.

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 score0.409
Threshold uncertainty score0.261

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 science0.0000.000
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 head0.072
GPT teacher head0.366
Teacher spread0.294 · 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