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Record W3014818974

Effects of Technological Development and Electricity Price Reductions on Adoption of Residential Heat Pumps in Ontario, Canada

2017· article· en· W3014818974 on OpenAlexaffabout
Alex Szekeres, Jack Jeswiet

Bibliographic record

VenueWSEAS TRANSACTIONS ON POWER SYSTEMS · 2017
Typearticle
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectricityGreenhouse gasHeat pumpEnvironmental scienceNatural gasEnvironmental economicsFossil fuelConsumption (sociology)Energy consumptionHeating systemNatural resource economicsWaste managementEnvironmental engineeringEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Home heating accounts for most of residential energy use in Canada. While natural gas, oil-fired furnaces, and electric resistance are the dominant heating system choices, heat pumps have become a viable alternative. Heat pumps with lower minimum operating temperatures and better performance are increasing both their effectiveness and their number of hours of useful service. In this study, we apply System Dynamics to analyze the effects of technological development on the rate at which homeowners adopt residential air source heat pumps. We test the effects of low, moderate and high rates of technological development, as well as reduced electricity and carbon pricing on the predicted rate of adoption in Ontario. From the perspective of the use stage in life cycle assessment, we estimate energy savings and greenhouse gas emissions reductions. We predict that using heat pumps will substantially reduce overall energy consumption, and in Ontario, where electricity is generated with little use of fossil fuels, it will also reduce greenhouse gas emissions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.512

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)0.0000.000

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 head0.200
Teacher spread0.192 · 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 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
Published2017
Admission routes2
Has abstractyes

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