Embodied carbon in mechanical, electrical, and plumbing systems: A critical literature review
Bibliographic record
Abstract
• Critical reviews life cycle assessment (LCA) studies of mechanical, electrical and plumbing (MEP) systems • Identifies best reporting practices to improve comparability and transparency of LCAs • Summarizes ten key research gaps to improve LCAs of MEP systems • Approx. 50 kgCO 2 e/m² lower bound estimate for product stage GWP impacts of MEP system The environmental impacts of mechanical, electrical and plumbing (MEP) systems have been largely overlooked and are commonly excluded from building-scale life cycle assessments (LCAs). Understanding the impacts and reduction potential of these systems is crucial for decarbonizing retrofits and new buildings. Therefore, we have conducted a critical review of LCA studies on MEP systems in buildings, selected using a systematic method, to identify: 1) estimates for upfront embodied [A1-A5] and replacement [B4] carbon impacts; 2) LCA reporting fundamentals needed to ensure transparency and interpretability of results; 3) future research directions. Since 2016, 54 studies presented sufficient information to investigate presented methodologies and LCA results of MEP systems. The review reinforces the need to report environmental impacts by individual life cycle stages and building or system elements to interpret influencing factors and enable further utility of results. Two studies did not report the impact assessment method nor background dataset used for the assessment rendering their results incomparable with others and are excluded from analysis. Based on the median (and mean) of the reviewed studies, estimates for A1-A3 and A1-A5 of the MEP systems are 40 (49) and 49 (61) k g C O 2 e / m ² , respectively. Additionally, based on reviewed studies, the systems will be replaced at least twice throughout a 60-year reference study period leading to approximately 100 k g C O 2 e / m ² for B4. These values almost certainly underestimate actual impacts due to the incomplete physical scopes spanned by the studies. Studies with more complete scopes generally reported higher values. The variability in scopes covered, reporting practices, and values reported, and the relatively small number of studies found, highlight the need for further investigation and improvement. Ten key research needs are identified, including the impact reduction potential of MEP systems and the influence of system layout and typology on the reported impacts. Additionally, future research should develop system specific benchmarks and reduction strategies while moving away from purely descriptive studies that report environmental impacts using single-point values.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".