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Record W4378464633 · doi:10.5281/zenodo.7468316

Context-specific assessment methods for life cycle-related environmental impacts caused by buildings

2023· report· en· W4378464633 on OpenAlexfundno aff
Thomas Lützkendorf, Maria Balouktsi, Rolf Frischknecht, Bruno Peuportier, Harpa Birgisdóttir, Rolf André Bohne, Maurizio Cellura, Maria Anna Cusenza, Nicolas Francart, Vanessa Gomes da Silva, Maristela Gomes da Silva, Juan Carlos Gómez de Cózar, Kyriaki Goulouti, Francesco Guarino, Endrit Hoxha, Sébastien Lasvaux, Carmen Llatas, Sonia Longo, Antonín Lupíšek, Marina Mistretta, Pierryves Padey, Alexander Passer, Lizzie Monique Pulgrossi, Marcella Ruschi Mendes Saade, Zsuzsa Szalay, Soust-Verdaguer Bernardette, Giovanni Tumminia, Xiaojin Zhang, Laetitia Delem, Tove Malmqvist, Alice Moncaster, Marie Nehasilová, Damien Trigaux

Bibliographic record

VenueVBN Forskningsportal (Aalborg Universitet) · 2023
Typereport
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
FundersJunta de AndalucíaEnergistyrelsenBundesamt für EnergieBundesministerium für Wirtschaft und EnergieAgence de l'Environnement et de la Maîtrise de l'EnergieNorges ForskningsrådMitacsÖsterreichische ForschungsförderungsgesellschaftEnergimyndighetenConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean CommissionBundesministerium für Bildung und ForschungUniversità degli Studi di PalermoBundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und TechnologieUniversidad de Sevilla
KeywordsLife-cycle assessmentContext (archaeology)Architectural engineeringEnvironmental impact assessmentEnvironmental resource managementEnvironmental planningEnergy (signal processing)EngineeringEnvironmental scienceGeographyPolitical scienceEconomicsArchaeology

Abstract

fetched live from OpenAlex

This report focuses on methodological issues related to the determination, assessment and presentation of the environmental performance of buildings. The purpose of this report is to provide the foundations to responsible parties for further developing their specific methods to assess the primary energy demand, GHG emissions and further environmental impacts of buildings and to increase the mainstreaming of practice globally. As far as possible, this should lead to a standardization of methods used worldwide. Where this goal cannot be achieved, methodological differences can be at least identified. The specific objectives of this report are to: clarify methodological questions that have been shown as significant but are under-addressed in the analysis of methods currently in use in the Annex 72 participating countries provide a consistent and transparent basis for a methodology and reporting structure for environmental performance assessment in line with international standards to enable comparability and usability of results contribute to the interpretation and supplementation of international standards to improve their applicability and support their dissemination promote long-term and life cycle-based thinking, by encouraging the early consideration of likely future environmental impacts regarding maintenance, repair and replacement as well as of durability and adaptability of building components and the building as a whole contribute to the overall efforts of national governments and standard makers to guide construction and real estate industry on how to respond to climate change promote the application of principles for circular economy by encouraging the early consideration of the deconstructability of buildings and building components and quantification of their reuse, recycling and/or recovery potential enable benchmarking and target-setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.025
GPT teacher head0.308
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2023
Admission routes1
Has abstractyes

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