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Record W3213678171 · doi:10.1111/itor.13077

Assessing subsidy policies for green products: operational and environmental perspectives

2021· article· en· W3213678171 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

VenueInternational Transactions in Operational Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsSubsidyBudget constraintEconomicsMicroeconomicsSupply chainProduct (mathematics)Constraint (computer-aided design)BusinessEnvironmental economicsIndustrial organizationNatural resource economicsMarketingMathematics

Abstract

fetched live from OpenAlex

Abstract This paper studies the impacts of two government subsidy policies, a fixed amount subsidy and discount subsidy, on the environment and operations of a two‐echelon supply chain, where the supply chain serves the market with either a marginal cost intensive green product (MIGP) or development‐intensive green product (DIGP). We first derive the equilibrium unit greenness level, pricing decisions, and the resulting economic and aggregate environmental performances. Then, we compare the effects of the two subsidy policies for the MIGP and DIGP with and without a total subsidy budget constraint. The main results are as follows: (1) We identify the congruence regions (conflict regions) within which one subsidy policy dominates the other according to all (some) criteria. (2) With the budget constraint, the fixed amount subsidy outperforms the discount subsidy for both MIGP and DIGP in terms of the unit and aggregate greenness levels.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.370
Teacher spread0.301 · 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