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Record W4405494818 · doi:10.1080/01446193.2024.2436395

The legitimation of private net zero emission building standards in the context of global decarbonization goals

2024· article· en· W4405494818 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConstruction Management and Economics · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLegitimationContext (archaeology)Zero (linguistics)Zero emissionNet (polyhedron)BusinessEnvironmental scienceEngineeringPolitical scienceMathematicsGeologyElectrical engineering

Abstract

fetched live from OpenAlex

Since the Paris Agreement – where nations committed to decarbonizing their economies – private net zero standards have proliferated. Yet, their limited scope and ability to deliver tangible change threatens their legitimacy. For some scholars, this reflects societies’ limited capacity to envision pathways towards a fossil-free future. This raises questions about how private net zero standards are legitimised. Combining insights from the literature on legitimation, discourse analysis, and sociotechnical transitions, we analyse the legitimation of Net Zero Carbon Buildings by the World Green Building Council, the strongest global sustainable construction network. We find that net zero standard legitimation is a dynamic multilevel, multistakeholder process based on three strategies: vertical nesting, ambivalence, and conflict avoidance. This process perpetuates assumptions about growth, authority, and appropriate scales of action. This incremental, emission-focused approach to decarbonization is shielded from critique through claims that it contributes to broader sustainability objectives. By reproducing institutionalized narratives, global standard setters create a sense of consensus. However, this perception of consensus fails to address the complexities of implementation, contextualization, and integration of broader environmental and social concerns. These findings raise doubts in both theory and practice about the capacity of the construction sector to decarbonize through current strategies.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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