Making embodied carbon mainstream: a framework for cities to leverage waste, equity, and preservation policy to reduce embodied emissions in buildings
Bibliographic record
Abstract
Abstract With anticipation building around embodied carbon as a “new frontier” of climate policy, it may appear that cities need to develop a whole suite of dedicated institutions and mechanisms to support its implementation. However, to do so risks placing an undue burden on already overstretched local and regional governments. Instead, embodied carbon policy can build on existing priorities that already galvanize resources and attention and have benefited from decades of policy development. Making strong links to a larger urban agenda offers a way to forge buy-in from a wide range of stakeholders. Current visions for embodied carbon policy broadly fall into two categories: (1) material substitution strategies, or technical solutions that incrementally reduce emissions, and (2) demand reduction strategies, more transformative solutions that avoid emissions. Both of these areas have strong ties to existing urban strategies for waste, equity, and preservation. Foundations in waste policy include increasing waste diversion, expanding green demolition, and increasing material efficiencies. Foundations in equity-oriented policy include retrofitting affordable housing, workforce development for deconstruction, and building lower carbon, lower cost housing. Foundations in preservation policy include incentivizing building reuse, supporting the use of low carbon materials for retrofits, and encouraging vertical infill. Amplifying existing policy efforts can bring substantive embodied carbon reductions to the forefront, leapfrogging a long technical start-up phase for implementing stand-alone embodied carbon policy.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".