Dynamic Heterogeneous Graph Neural Network with Carbon-Sensitive Dual Attention for Lifecycle Carbon Footprint Assessment of Engineering Projects
Bibliographic record
Abstract
Global engineering projects are major carbon emitters with high heterogeneity, but traditional assessment methods (e.g., LCA, IPCC) lack precision, efficiency, and adaptability to dynamic construction. This study proposes a Carbon Footprint-aware Graph Neural Network (CF-GNN) for lifecycle carbon assessment. Its core innovations include: (1) a dynamic heterogeneous graph (entity/attribute nodes) updated via 15-day cycles and milestone triggers; (2) a carbon-sensitive dual attention mechanism prioritizing high-emission nodes/edges; (3) a third-order message passing framework capturing multi-hop carbon flows (up to 5 nodes). Validated on 3.86 million time-series data from 16 projects (residential, bridge, factory, etc.) against 8 baselines (LCA, GAT, TGAT, etc.), results show: CF-GNN achieves an average MAPE of 7.2% (38.9% lower than GAT, 55.8% lower than LCA), with bridge project RMSE at 218 tCO₂ (59.4% lower than LCA). It has 2.0±0.1s inference latency for 1000 nodes and 52±3.1min end-to-end assessment—3375-fold less manual effort than LCA (6 months/bridge). Key node identification matches experts (0.87 Kendall coefficient), with CV<5% (high stability) and 94.2±1.5% coverage for 95% prediction intervals. CF-GNN enables precise, efficient dynamic assessment, supporting low-carbon design/optimization and advancing "dual carbon" goals in construction, transportation, and energy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".