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Record W2137461186 · doi:10.3390/su2010354

Toward Environmentally Sustainable Construction Processes: The U.S. and Canada’s Perspective on Energy Consumption and GHG/CAP Emissions

2010· article· en· W2137461186 on OpenAlexafffundabout
Changbum R. Ahn, Sang Hyun Lee, Feniosky Peña‐Mora, Simaan AbouRizk

Bibliographic record

VenueSustainability · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
FundersDivision of Civil, Mechanical and Manufacturing InnovationNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsGreenhouse gasEnvironmentally friendlyEnergy consumptionIncentiveSustainable developmentConsumption (sociology)Environmental economicsEnvironmental impact assessmentEnvironmental planningBusinessSustainabilityNatural resource economicsEnvironmental resource managementEngineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

In the building and construction sector, most efforts related to sustainable development have concentrated on the environmental performance of the operation of buildings and infrastructure. However, several studies have called for the need to mitigate the considerable environmental impacts, especially air pollutant emissions and energy consumption, generated by construction processes. To provide a point of reference for initiating the development of environmentally sustainable construction processes, this article identifies energy consumption and air emissions resulting from construction activities and examines previous approaches utilized to assess such environmental impact. This research also identifies the opportunities and challenges to mitigate such environmental impact from construction processes, based on the investigation of current technology policies, regulations, incentives, and guidelines.

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)0.001
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 score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.004
GPT teacher head0.220
Teacher spread0.215 · 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

Citations52
Published2010
Admission routes3
Has abstractyes

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