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Record W4396509123 · doi:10.22215/etd/2024-15903

Assessing the Environmental Impact: Office Building Reuse as a Sustainable Alternative to Demolition

2024· dissertation· en· W4396509123 on OpenAlexaff
Taylor Rose Quibell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsCarleton University
Fundersnot available
KeywordsReuseDemolitionSustainabilityKey (lock)IncentiveDemolition wasteArchitectural engineeringBuilt environmentEngineeringEnvironmental planningEnvironmental resource managementCivil engineeringEnvironmental economicsComputer scienceEnvironmental scienceWaste managementEconomics

Abstract

fetched live from OpenAlex

Building reuse is key to sustainability, given its potential to conserve resources, reduce waste, and foster sustainable development.However, determining the precise environmental impact of existing buildings through the quantification of embodied carbon in structures remains a challenge.To further our understanding of this impact's magnitude, this research quantifies the embodied carbon in an existing office building by examining three scenarios: "doing nothing," "building reuse," and "new construction."Through a combination of manual and tool-based calculations, the study compares the limitations of methods and the reliability and accuracy of results.Despite the variability of results, the findings still reveal that choosing the path of building reuse over demolition and reconstruction can mitigate the release of significant quantities of carbon into the atmosphere.These findings highlight the environmental implications of building reuse while advocating for standardized methodology, policies, and incentives. Initial Embodied CarbonThe sourcing, manufacturing, and transportation of materials (CLF).

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.008
GPT teacher head0.308
Teacher spread0.300 · 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
Published2024
Admission routes1
Has abstractyes

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