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Spatio-temporal metabolic rifts in urban construction material circularity

2024· article· en· W4393246442 on OpenAlexaffabout
Thomas Elliot, Marie Vigier, Annie Levasseur

Bibliographic record

VenueResources Conservation and Recycling · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsLife-cycle assessmentEnvironmental impact assessmentOverconsumptionCircular economyNatural resource economicsWork (physics)Resource (disambiguation)Material flow analysisGeographyIndustrial ecologyEnvironmental resource managementEconomic geographyResource efficiencyBusinessEconomicsEngineeringSustainabilityEcologyProduction (economics)

Abstract

fetched live from OpenAlex

Global demand for resources currently exceeds Earth's carrying capacity. Representing a majority of global resource use, and associated environmental burdens, cities must address overconsumption by improving material circularity. This work explores the potential for the construction sector to reduce the indirect environmental impacts connected to increasing material circularity in the coming years. An urban metabolism simulation tool based on system dynamics and life cycle thinking is deployed to estimate the effects of circularization on environmental impacts. Illustrating with a case study of Montréal (Canada), impacts are disaggregated to supplier nations, provinces and territories. As material circularity increases over time, impacts decrease in the sub-national and international regions, but increase in the city due to the activities associated with second life valorisation. In supplier regions, especially Brazil, Mexico, and Norway, environmental impacts decrease between 80 and 100 % in all 18 impact categories by 2050. However, these decreases are found to be shared mostly among Canada's more developed trading partners, revealing an environmental justice risk for circular materiality to disproportionally favour the better-off. Five of the 18 categories did not undergo spatial burden-shifting, improving at all spatial levels in the assessment, while 13 showed decreased environmental impacts remotely at the expense of increased impacts within Montréal.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 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

Citations17
Published2024
Admission routes2
Has abstractyes

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