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Record W4327729697 · doi:10.1787/cbda8bad-en

Home, green home: Policies to decarbonise housing

2023· report· en· W4327729697 on OpenAlexaboutno aff
Peter Hoeller, Volker Ziemann, Boris Cournède, Manuel Betín

Bibliographic record

VenueOECD Economics Department working papers · 2023
Typereport
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BusinessElectricityFossil fuelGreenhouse gasNatural resource economicsEnvironmental economicsFinanceEconomicsWaste managementEngineeringGeography

Abstract

fetched live from OpenAlex

The housing sector is one of the main sources of CO2 emissions in OECD countries, accounting for over a quarter of the total. Robust and rapid action is required to reach the net zero emission target by 2050. Decarbonising housing involves halting the use of fossil fuels in homes, ensuring that electricity is generated from carbon-free sources, using high-energy-efficiency appliances and heating systems, ensuring effective insulation and encouraging behavioural changes. This paper discusses which policy instruments can prompt this transformation of the housing sector, ranging from carbon pricing through energy labelling requirements to green housing finance.

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 score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.248
Teacher spread0.211 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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