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Record W1506800069

Ground Source Heat Pump Systems in Canada: Economics and GHG Reduction Potential

2007· preprint· en· W1506800069 on OpenAlexaboutno aff
Jana Hanova, Hadi Dowlatabadi, Lynn Mueller

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasCapital costNatural resource economicsEnvironmental scienceElectricityEnvironmental economicsCost reductionEconomic impact analysisBusinessEconomicsEnvironmental engineeringAgricultural economicsEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

Climate stabilization requires greenhouse gas reductions (GHG) in excess of 60 percent. Ground source heat pumps (GSHPs) hold the promise of meeting heating and cooling loads much more efficiently than conventional technologies. The economic viability of their widespread adoption depends on the costs of energy. Their impact on GHG reduction depends on fuel choices both in electricity generation and on customers’ premises. In this paper, we provide a systematic assessment of the GHG reduction potential across Canada of GSHPs and the economic cost of achieving this reduction. Using province-level data on household fuel choices and energy use, we find that GSHP systems offer significant GHG reductions, as well as savings in operation and maintenance costs. However, high capital costs continue to limit market diffusion. We conclude with a review of the geological suitability of the five largest urban centers in Canada for GSHP installation. This analysis shows GSHPs to hold significant potential for substantial GHG reductions in Canada at a cost savings relative to conventional alternatives, with time horizons as short as seven years.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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