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Record W3121140492 · doi:10.5547/01956574.37.4.nriv

Free Riding on Energy Efficiency Subsidies: The Case of Natural Gas Furnaces in Canada

2016· preprint· en· W3121140492 on OpenAlexafffundabout
Nicholas Rivers, Leslie Shiell

Bibliographic record

VenueThe Energy Journal · 2016
Typepreprint
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
FundersNatural Resources CanadaSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsSubsidyDistribution (mathematics)Tax creditEconomicsGreenhouse gasEfficient energy useFree ridingEnergy subsidiesNatural experimentNatural resource economicsAgricultural economicsPublic economicsBusinessEnergy policyRenewable energyMicroeconomicsIncentiveMarket economyEngineering

Abstract

fetched live from OpenAlex

We assess the extent to which subsidies for home energy efficiency improvements in Canada have been paid to households that would have undertaken the improvements anyway—the so-called free rider rate. We focus on forced-air natural gas furnaces, replaced between April 1, 2007 and March 31, 2011, under both federal and provincial subsidy programs as well as the 2009 federal Home Renovation Tax Credit. Our results indicate that around 50 percent of expenditures under the Canadian subsidy and tax credit programs represented free riding. In the long run, our estimates suggest that over 80 percent of grant recipients would have chosen an identical furnace at the time of replacement. We estimate that the cost effectiveness of the programs in terms of greenhouse gas reduced was between $70 and $110/t CO2, depending on the assumptions made. Further, we find that a substantial majority of the grants were received by middle- and high-income households, such that the grant had a regressive effect on the distribution of income. We conclude that such grants are not an optimal way to improve residential energy efficiency.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designTheoretical or conceptual
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
Published2016
Admission routes3
Has abstractyes

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