Dynamic environmental payback of concrete due to carbonation over centuries
Bibliographic record
Abstract
This research introduces a dynamic life cycle assessment (LCA) based carbonation impact calculator designed to enhance the environmental evaluation of cement-based construction products. The research emphasizes the limitations of static LCAs which fail to capture the time-dependent nature of carbon sequestration by carbonation. We provide an easy-to-use spreadsheet-based LCA carbonation model. The model is available in the supplementary information, and includes a suite of changeable parameters for exploring the effect of alternative environmental conditions and concrete block composition on carbonation. The tool enables use of both a static and dynamic LCA method to calculate the production emissions and carbonation sequestration of a concrete block over a 1000-year time horizon. Carbonation can partially mitigate initial production emissions and adjust radiative forcing over long periods. Using a static attributional LCA approach, carbonation sequesters 6 % of the CO 2 generated from its production emissions. We describe the ratio of carbonation to production emissions as the partial “carbonation payback”, and with dynamic LCA show the variation of this ratio over time. Considering time by applying the dynamic LCA approach, we find this partial “carbonation payback” is split between uptake during the 60-year service life (0.13 kg CO 2 ) and the 940-year end of life period (0.12 kg CO 2 ) in our baseline case. Further scenario analyses illustrate the significant variability in carbonation payback, driven by environmental factors, cement composition, and the use of supplementary cementitious materials. The results highlight the critical role of modelling choices in estimating the carbonation payback. The carbonation calculator developed in this study offers a sophisticated yet user-friendly tool, providing both researchers and practitioners with the ability to dynamically model the sequestration potential of concrete, thereby promoting more sustainable construction practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".