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Record W4410890245 · doi:10.5267/j.ijiec.2025.3.003

To cooperate or not? The cooperation conditions of different new energy vehicle manufacturers on power battery under government subsid

2025· article· en· W4410890245 on OpenAlexvenueno aff
Yiwen Zhang, Qi Wang

Bibliographic record

VenueInternational Journal of Industrial Engineering Computations · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
FundersChina Postdoctoral Science Foundation
KeywordsBattery (electricity)Government (linguistics)Power (physics)BusinessAutomotive engineeringNew energyEnvironmental economicsIndustrial organizationTransport engineeringEngineeringEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

To stimulate the new energy vehicle (NEV) market, China has raised the bar for NEV subsidies so that only NEVs with high endurance are eligible for subsidies. As a result, the NEV manufacturers may cooperate on power batteries, which makes their relationship shift from competition to downstream competition and upstream cooperation, i.e. co-opetition. Based on this, this paper investigates the cooperation conditions between the leading NEV manufacturer and the emerging NEV manufacturer on power batteries under the revised subsidy policy. By establishing a Cournot model, we first analyze the optimal decisions of the two manufacturers under government subsidy policy in competition and co-opetition scenarios, respectively. By comparing the profits of NEV manufacturers in these two scenarios, we derive the conditions under which they can cooperate on power batteries. The results show that whether the NEV manufacturers can cooperate depends on the power battery cost of the emerging NEV manufacturer.

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 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 score0.357
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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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