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Record W4248134371 · doi:10.32920/ryerson.14645658

Reducing the Carbon Footprint at an Electric Utility Company

2021· preprint· en· W4248134371 on OpenAlexaff
Peter Moore

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGreenhouse gasCarbon footprintEnvironmental economicsBusinessProcess (computing)Corporate social responsibilityNatural resource economicsEnvironmental resource managementEnvironmental scienceEconomicsComputer sciencePolitical scienceEcology

Abstract

fetched live from OpenAlex

The purpose of this thesis is the development of options for the reduction of the carbon footprint at an electric utility company. A case study details a systematic approach to reduce corporate greenhouse gas emissions over the next decade. The study focuses on providing three principle outputs: 1) process maps whereby the company may systematically identify its current carbon footprint, 2) scenario analyses to project its future carbon emissions over a ten year period and, 3) recommended actions to reduce GHG emissions over the next decade. A number of recommendations for emission reductions are made. The findings suggest that the degree to which an organization addresses its GHG emissions is substantially influenced by organization-specific social, regulatory, technological and economic forces. The case study reveals that by perceiving corporate carbon management as a periphery objective, organizations limit their ability to reduce GHG emissions and to improve their corporate performance.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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