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Record W4242046804 · doi:10.32920/ryerson.14660499

Achieving an 80% reduction in Ontario residential heating energy consumption by 2030: a tiered framework and preliminary implementation strategy

2021· preprint· en· W4242046804 on OpenAlexaffabout
Amanda Jacqueline Yip

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBaseline (sea)Environmental economicsEnergy consumptionConsumption (sociology)Work (physics)Sample (material)Efficient energy usePayback periodReduction (mathematics)BusinessOperations managementProduction (economics)EngineeringEconomics

Abstract

fetched live from OpenAlex

The increasing prevalence of climate change impacts and rising energy prices has highlighted the need to achieve deep energy savings now. To accomplish this, stricter prescriptive performance requirements for residential buildings are needed. The intent of this work is to develop a framework and policy implementation strategy to achieve an 80% reduction in Ontario residential heating energy consumption by 2030. A tiered framework of consumption targets was developed using OBC 2012 SB-12 requirements as a baseline and sample compliance packages created for each tier. Construction costs for the baseline and each tier compliance package were estimated and simple payback periods determined. Impacts of fuel escalation rates on payback periods were also considered. Significant cost premiums were found between the baseline consumption and overall 80% heating energy reduction target. Lack of experience and perceived risk were found to be the greatest barriers to achieving the overall energy reduction target. A preliminary strategy and supporting policy tools was developed, taking into consideration the observed barriers to adoption.

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)
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 score0.386
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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