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Record W2782072117 · doi:10.5296/emsd.v7i1.12396

Carbon Sequestering and Green Roof Technology: A Benefit Cost Analysis

2018· article· en· W2782072117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Management and Sustainable Development · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSubsidyGreen roofRoofGovernment (linguistics)Greenhouse gasBusinessValue (mathematics)InstallationNatural resource economicsEnvironmental scienceEnvironmental economicsEconomicsEngineeringCivil engineeringComputer science

Abstract

fetched live from OpenAlex

The installation of a green roof on residential buildings affords the opportunity to sequester carbon from the atmosphere. The cost of incorporating green roofs in the construction of a family home or modifying an existing home is significant and the private benefits are rather small. Carbon reduction does have a value recognized by all levels of government in Canada. In this paper we calculate the cost of installing a green roof on a two vehicle garage in the Province of Ontario using current building costs. Utilizing data on the private costs and private benefits, the estimated NPV of a green roof over a 35 year period is negative. Once the value of carbon sequestering is introduced in the model, the NPV is positive, suggesting that subsidizing green roof construction is an efficient method in any government’s question to encourage a reduction in GHG emission.

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.

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 score0.081
Threshold uncertainty score0.861

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 science0.0000.001
Research integrity0.0000.000
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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