Consequential life cycle assessment of Brazilian cement industry technology projections for 2050
Bibliographic record
Abstract
Abstract In the upcoming decades, cement production growth is expected to exceed the increase in availability of clinker substitutes. Increased clinker replacement rates in cement and use of alternatives fuels were pointed out as the main alternatives for reducing emissions of the national cement industry, whilst increasing cement production in 2050. Consequential life cycle assessment (CLCA) offers a framework to capture environmental consequences from demand alterations. Modelling the cement production and markets involved is however challenging, given conceptual (only unconstrained markets are considered) and practical modelling limitations (e.g. model granularity compatible with CLCA interests). This paper refers to an ongoing work and adopts a two-stage approach to discuss the effects of the change on the average cement production process in Brazil. We first performed a CLCA without formal affected market identification to estimate the potential environmental impacts of the technology change proposed in the Brazilian Cement Technology Roadmap. Secondly, we used a Computable General Equilibrium (CGE) Model of the Brazilian economy to (more) realistically foresee short-term effects induced by such change. The CGE model comprises 102 economic activities, including cement production and its production chain. Our results indicate that (i) increasing the proportion of calcined clay and limestone filler as clinker partial substitutes and (ii) excluding charcoal from the fuel mix composition at the kiln would impact all economic sectors. Our preliminary findings suggest that the increased efficiency in cement production would create some rebound effect that would not invalidate the emission benefits from displacing energy and virgin materials. Additional impact categories and consequences in other economic sectors should be further investigated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".