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Record W2053083445 · doi:10.1108/17568690910934381

Key energy‐related steps in addressing climate change

2009· article· en· W2053083445 on OpenAlexaff
Marc A. Rosen

Bibliographic record

VenueInternational Journal of Climate Change Strategies and Management · 2009
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGreenhouse gasClimate changeEnvironmental resource managementClimate change mitigationEfficient energy useEnergy (signal processing)Environmental economicsKey (lock)Environmental scienceGlobal warmingBusinessComputer scienceEngineeringEconomicsEcologyComputer security

Abstract

fetched live from OpenAlex

Purpose Efforts to address climate change need to focus on curtailing carbon emissions to the atmosphere, and these efforts need to focus in large part on energy processes and activities, given the significant contributions to carbon emissions of the energy sectors of countries. This paper aims to describe key energy‐related steps needed to address climate change. Design/methodology/approach The key energy‐related steps needed to address climate change are identified, discussed and illustrated. Findings Several key energy‐related steps are identified that need to be addressed to combat climate change. These include: use non‐carbon‐based energy sources; use non‐carbon‐based energy carriers and/or energy carriers that facilitate the use of non‐carbon‐based energy sources; remove and sequester carbon‐based atmospheric emissions; and increase efficiency. Originality/value Given the major contributions of energy processes to climate change, it is anticipated that initiatives to address these steps will allow major advances to be achieved regarding addressing greenhouse gas emissions and climate change. The key energy‐related steps constitute a logical and pragmatic approach that provides a coherent overall structure to guide efforts to address climate change. It is expected that addressing these steps should assist broader efforts to achieve sustainable development.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.696

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 head0.035
GPT teacher head0.273
Teacher spread0.238 · 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 designOther design
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

Citations11
Published2009
Admission routes1
Has abstractyes

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