Five recommendations to accelerate sustainable solutions in cement and concrete through partnership
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it