The problem with net zero building policy: reflecting on the making, and future, of a global discourse
Bibliographic record
Abstract
Abstract The Paris Agreement made net zero emissions a global target. In response, net zero carbon building standards have proliferated, making net zero a popular target for buildings. But to meaningfully contribute to global decarbonization efforts, net zero standards and the organizations who promote them, must be deemed legitimate. Given the building industry’s reputation for being highly fragmented and slow to change, how has this legitimacy been constructed? What are the implications of this legitimation process? This article seeks to answer these questions by exploring the narratives used by the World Green Building Council (WGBC) to legitimate Net Zero Carbon Buildings (NZCB) from 2015 to 2021. Our analysis is based on over 100 documents produced by the WGBC and 22 interviews with WGBC and Green Building Council representatives, policymakers, and industry actors. Results reveal six main storylines adopted by the WGBC to extend the legitimacy of sustainable green building movement actors to the new net zero governance space. This legitimation process allows the WGBC to develop and implement net zero standards quickly, but also creates tensions between efficiency and procedural integrity, potential and proven results, corporate and collective value. While NZCB are here to stay, these tensions highlight barriers to their wide-scale adoption and question their ability to deliver an economically viable, socially just, environmental, net zero transition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.092 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".