Study on the characteristics and prediction of concrete carbon emissions based on a machine learning approach with spatiotemporal heterogeneity analysis
Bibliographic record
Abstract
Although concrete remains indispensable in construction for its compressive strength and durability, its production constitutes a predominant source of building-sector carbon emissions. Current emission accounting methods frequently neglect fine-grained spatiotemporal variations, resulting in prediction inaccuracies that hinder effective decarbonization policy formulation. This study addresses this gap by elucidating how regional and seasonal factors modulate concrete emissions and establishing a high-precision dynamic prediction model. Focusing on Shandong Province, China’s northern hub for construction material production, we quantified disparities in mix proportions, raw material logistics (0–130 km), and production energy consumption intensity across five subregions (East, West, South, Central, and North Shandong). We conducted a systematic comparison of four mainstream machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), XGBoost, and Support Vector Machine (SVM). The GBDT model achieved superior performance (R² = 0.958, RMSE = 20.05 kgCO₂), outperforming alternatives by 12–23 % in accuracy. SHAP analysis revealed cement content, aggregate type, fly ash, and transport distance as dominant predictors. Our findings demonstrate that: (1) Emission heterogeneity across subregions reaches 28.6 % due to localized supply chains and seasonal energy mixes. (2) GBDT’s ensemble learning effectively captures nonlinear interactions in material–process–emission relationships. This work advances low-carbon concrete strategies by providing a spatiotemporally adaptive prediction tool, with implications for lifecycle carbon management in construction. The methodology is transferable to other emission-intensive regions, supporting low-carbon goals through data-driven industrial optimization.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".