BIM can help decarbonize the construction sector: Primary life cycle evidence from pavement management systems
Bibliographic record
Abstract
Transforming the construction sector is key to reaching net-zero, and many stakeholders expect its decarbonization through digitalization, but no quantified evidence has been brought to date. This article proposes the first environmental quantification of the impact of Building Information Modeling (BIM) in the construction sector. Specifically, the direct and indirect greenhouse gas (GHG) emissions generated by a monofunctional BIM to plan road maintenance – a Pavement Management System (PMS) - are evaluated using field data from France. The related carbon footprints are calculated following a life cycle approach, using different sources of data – including ecoinvent v3.6 – and the IPCC 2013 GWP 100a characterization factors. Three design-build-maintain pavement alternatives are compared: scenario 1 relates to a massive design and surface maintenance, scenario 2 to a progressive design and pre-planned structural maintenance, and scenario 3 to a progressive design and tailored structural maintenance supported by the PMS. First, results show the negligible direct emissions due to the PMS existence – 0.02% of the life cycle emissions of scenario 3's pavement, e.g. 0.52 t CO2eq for 10 km and 30 years. Second, the base case and two complementary sensitivity analyses show that the use of a PMS is climate-positive over the life cycle when pavement subgrade bearing capacity improves over time, neutral for the climate otherwise. The GHG emissions savings using BIM can reach up to 14 and 30% of the life cycle emissions respectively compared to scenario 2 and 1, and resp. 47 and 65% when restraining the scope to maintenance and rehabilitation and excluding original pavement construction. Third, the neutral effect of BIM in case of a deterioration of the bearing capacity of the subgrade may be explained by design practices and safety margins, that could in fact be enhanced using BIM. Fourth, the decarbonization potential of a multifunctional BIM is discussed, and research perspectives are presented.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".