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Record W4297018400 · doi:10.1088/1748-9326/ac9490

Is crushed concrete carbonation significant enough to be considered as a carbon mitigation strategy?

2022· article· en· W4297018400 on OpenAlexaff
Marcella Ruschi Mendes Saade, Ammar Yahia, Ben Amor

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversité de SherbrookeNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsCarbonationEnvironmental scienceLife-cycle assessmentCarbon sequestrationCrushed stoneCarbon fibersCarbonatationClimate changeCementCarbon dioxideCivil engineeringProduction (economics)Materials scienceEngineeringGeologyChemistryMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract When addressing concrete carbonation as a carbon mitigation option, studies leave out the effect that a temporal difference between the CO2 emissions and uptake happening throughout concrete’s life cycle have on climate change. In this study, the role played by carbonation on concrete’s carbon mitigation potential is investigated through a dynamic life cycle assessment, to properly position CO2 uptake and release. The carbon balance in concrete structures built and demolished from 2018 to 2050 is modelled as a case study. The potential uptake due to crushed concrete carbonation is over 9% of the cumulative global warming effect of concrete manufacturing. It is comparable to the reduction potential of the most promising strategy, namely replacing clinker, totaling 12%. If stimulated in a wide scale, crushed concrete carbonation can push the industry towards meeting carbon mitigation targets faster. Future environmental impact assessments should rely on dynamic models to increasingly consider this phenomenon.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.272
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2022
Admission routes1
Has abstractyes

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