How is net zero whole-life carbon of buildings assessed and implemented?
Bibliographic record
Abstract
Abstract Life Cycle Assessment (LCA) is a widely used methodology for evaluating the environmental impacts of buildings. LCA can be structured in various non-exclusive approaches, including top-down, bottom-up, dynamic, prospective, or hybrid. In the quest for decarbonization, the building, construction and real estate (BCRE) sectors have applied LCA, often voluntarily, to model buildings in an effort to reduce greenhouse gas emissions. However, simplifications in the assessed scope (e.g., life cycle phases, assessed building typologies) create variability, potentially undermining critical emissions data and may result in benchmarks that cannot be aligned with the global carbon budget. In this article, we conduct a Systematic Literature Review (SLR) that examines LCA approaches based on building typology and geographic location. This review includes scientific papers from leading scientific databases and significant grey literature from prominent international agencies. The content of the current literature will be analyzed for the system boundaries, building system inclusion, methodology classification, building type, embodied and operational carbon, carbon credit and balance, uncertainties, resiliency, and future scenario considerations. We aim to identify the most suitable methods for establishing benchmarks and reproducible assessments that align with the global carbon budget necessary for achieving Net-Zero whole-life carbon emissions by 2050. Additionally, we highlight the most appropriate methods for the BCRE industry to provide replicable and transparent results. The findings highlight the methodological and normative requirements for implementing legally binding policies in building whole-life carbon assessments while addressing regional barriers such as economic feasibility and climate change resilience.
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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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".