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Record W3111883293 · doi:10.1080/0013791x.2020.1853863

Optimal Replacement, Retrofit, and Management of a Fleet of Assets under Regulations of an Emissions Trading System

2020· article· en· W3111883293 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Engineering Economist · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPurchasingGreenhouse gasBusinessEmissions tradingFinanceIndustrial organizationEnvironmental economicsNatural resource economicsCommerceEconomics

Abstract

fetched live from OpenAlex

This paper presents a model for parallel replacement and improvement for a fleet of assets to minimize both the economic costs and greenhouse gas (GHG) emissions where the emissions are limited by an emissions trading system also known as cap-and-trade. The firm which owns the assets has the options of using, storing, improving, or salvaging them. Different technological types and their performances have been considered for the assets. The firm has the option of purchasing new assets from varying technologies and/or improving its existing assets to a higher-performance type. The model considers the possibility of both banking the emission allowances or trading them in the market. The model was applied to data from a fleet of excavators in Ontario, Canada. The model and the findings of this case study could help emitter firms to simultaneously manage the emissions and costs of their assets in a jurisdiction regulated by cap-and-trade.

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 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 score0.251

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 head0.211
Teacher spread0.200 · 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