Five recommendations to accelerate sustainable solutions in cement and concrete through partnership
Bibliographic record
Abstract
Though the technical knowledge to make cement and concrete more sustainable already exists, implementation of solutions lags behind the rate needed to mitigate climate change and meet the targets set by the Sustainable Development Goals. Whilst most of the focus around the built environment is on embodied carbon, we stress an important but neglected dimension: partnership (SDG17). Effective partnerships can be powerful enablers to accelerate sustainable solutions in cement and concrete, and let such solutions transfer from academia to the market. This can be achieved through knowledge generation, solution implementation, and policy development, among other routes. In this article, we share five recommendations for how partnerships can address neglected research questions and practical needs: 1) reform Science, Technology, Engineering and Mathematics (STEM) education to train “circular citizens”; 2) map out routes by which cementitious materials can contribute to a “localization” agenda; 3) generate open-access maps for the geographical distribution of primary and secondary raw materials; 4) predict the long-term environmental performance of different solutions for low-CO2 cements in different geographical areas; 5) overhaul standards to be technically and regionally fit for purpose. These approaches have the potential to make a unique and substantial contribution towards achieving collective sustainability goals.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.035 | 0.021 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".