MétaCan
Menu
Back to cohort
Record W2517693584 · doi:10.3390/su8080807

Simulation and Assessment of Whole Life-Cycle Carbon Emission Flows from Different Residential Structures

2016· article· en· W2517693584 on OpenAlexaff
Rikun Wen, Shenjun Qi, Ahmad Jrade

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCarbon fibersGreenhouse gasCarbon cycleEnvironmental scienceDemolitionLife-cycle assessmentLand reclamationEnvironmental engineeringEngineeringProduction (economics)Materials scienceCivil engineeringEcologyEconomicsComposite materialEcosystem

Abstract

fetched live from OpenAlex

To explore the differences in carbon emissions over the whole life-cycle of different building structures, the published calculated carbon emissions from residential buildings in China and abroad were normalized. Embodied carbon emission flows, operations stage carbon emission flows, demolition and reclamation stage carbon emission flows and total life-cycle carbon emission flows from concrete, steel, and wood structures were obtained. This study is based on the theory of the social cost of carbon, with an adequately demonstrated social cost of carbon and social discount rate. Taking into consideration both static and dynamic situations and using a social discount rate of 3.5%, the total life-cycle carbon emission flows, absolute carbon emission and building carbon costs were calculated and assessed. The results indicated that concrete structures had the highest embodied carbon emission flows and negative carbon emission flows in the waste and reclamation stage. Wood structures that started the life-cycle with stored carbon had the lowest carbon emission flows in the operations stage and relatively high negative carbon emission flows in the reclamation stage. Wood structures present the smallest carbon footprints for residential buildings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.281
Teacher spread0.275 · 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 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

Citations30
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicEnvironmental Impact and SustainabilityFrench-language works237,207