Mitigating Portland Cement CO2 Emissions Using Alkali-Activated Materials: System Dynamics Model
Bibliographic record
Abstract
While alkali-activated materials (AAMs) have been hailed as a very promising solution to mitigate colossal CO2 emissions from world portland cement production, there is lack of robust models that can demonstrate this claim. This paper pioneers a novel system dynamics model that captures the system complexity of this problem and addresses it in a holistic manner. This paper reports on this object-oriented modeling paradigm to develop a cogent prognostic model for predicting CO2 emissions from cement production. The model accounts for the type of AAM precursor and activator, the service life of concrete structures, carbonation of concrete, AAM market share, and policy implementation period. Using the new model developed in this study, strategies for reducing CO2 emissions from cement production have been identified, and future challenges facing wider AAM implementation have been outlined. The novelty of the model consists in its ability to consider the CO2 emission problem as a system of systems, treating it in a holistic manner, and allowing the user to test diverse policy scenarios, with inherent flexibility and modular architecture. The practical relevance of the model is that it facilitates the decision-making process and policy making regarding the use of AAMs to mitigate CO2 emissions from cement production at low computational cost.
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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) | 0.001 | 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) | 0.001 | 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".