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Record W2736196886 · doi:10.1109/tem.2017.2714698

Economic, Environmental, and Social Impact of Remanufacturing in a Competitive Setting

2017· article· en· W2736196886 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.

Bibliographic record

VenueIEEE Transactions on Engineering Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsRemanufacturingCannibalizationProfit (economics)Industrial organizationMonopolyBusinessCompetition (biology)Environmental impact assessmentProduct (mathematics)Economic surplusMicroeconomicsEnvironmental economicsEconomicsMarketingEngineeringMarket economy

Abstract

fetched live from OpenAlex

This paper studies the environmental and social trade-offs of remanufacturing for product+service firms under competition. We use an analytical model and a behavioral study that together incorporate demand cannibalization from multiple customer segments across the competing firms' product lines. We measure firms' profits, consumer surpluses, environmental impacts, and environmental costs along the products lifecycles in the resultant equilibria with and without remanufacturing. We show that competition intensifies the tension between increased profit and worsened environmental impact from market expansions caused by remanufacturing identified by prior research in the case of monopoly. However, bringing in the social dimension leads to an overall positive assessment: remanufacturing creates additional consumer surplus, which compensates for the cost of the environmental impact. In other words, we found strong support that remanufacturing is beneficial for the society.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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