Policy making for greening the concrete industry in Canada: a systems thinking approach
Bibliographic record
Abstract
Portland cement production in Canada increased from 1.7 million tonnes in 1946 to more than 13 million tonnes in 2002. Although the industry is a major player in meeting infrastructural needs of Canadians, it is also a major user of natural resources such as water, natural minerals, and aggregates. Moreover, for each tonne of cement clinker produced, 1 t of CO2 is released into the atmosphere. At the same time, Canada produces nearly 5 million tonnes of fly ash each year, and yet the use of fly ash as cement replacement remains dismally low at around 17%. It is believed that through product and process innovations, more fly ash can be used in concrete, thus preserving natural resources, reducing CO2 emissions, and helping Canada meet the requirements of the Kyoto Protocol. Forecasting the future impact of using fly ash in concrete has been based on qualitative and linear estimates, however, without accounting for the complexity of the problem and its dynamic feedbacks. In this paper, a novel application of system dynamics modeling, a feedback-based object-oriented modeling paradigm, is proposed to create a rational model that departs from current approaches used in modeling CO2 emissions of cement production. The model accounts for the various enablers and barriers for using fly ash in concrete, including market dynamics and technology development. It allows the user to test a wide variety of scenarios and policies, its flexible architecture permits coupling it with general economic or service life models, and its modular nature allows expanding its boundaries to include other facets of the holistic CO2 emissions problem in Canada.Key words: concrete, blended cements, CO2 emission, global warming, sustainable development, system dynamics, modeling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".