Modelling and Sustainability Index Assessment of Biocomposites Supply Chain Networks Using Bayesian Belief Networks: A Case Study on Hemp-based Pellet Production
Bibliographic record
Abstract
Planning sustainable supply chains (SCs) for emerging biocomposite materials is critical for balancing environmental, economic, and social dimensions in strategic decision-making, towards industrial success. In biocomposite SCs, early-stage pre-processing activities, such as biomass collection and particle size reduction, play a pivotal role in determining downstream sustainability outcomes. However, evaluating the performance of different SC scenarios under changing conditions remains difficult due to the non-linear interdependencies and uncertainty across SC components. This study presents a knowledge-driven Bayesian Belief Network (BBN) framework to support sustainability assessment of a complex hemp-based biocomposite SC case study under uncertainty. A novel metric, the Supply Chain Sustainability Index (SCSI), is introduced to quantify the overall probability of achieving the target sustainability performance level and assess the vulnerability of each underlying indicator. The BNN model integrates both expert insights and empirical relationships through regression-informed Conditional Probability Tables (CPTs) and causal graphs, across 15 proposed criteria of measurement spanning economic (e.g., Net Present Value (NPV), Conditional Value at Risk (CVaR), costs), technical (e.g., product quality, Technology Readiness Level (TRL)), social (e.g., job creation), and environmental (e.g., carcinogenic and ecotoxic impacts) factors. The scenario-based simulation and entropy-based sensitivity analyses are also conducted to identify the most influential factors among the criteria trade-offs. The results showed that the economic and technical factors, in the present case study, have the greatest influence on overall sustainability, while the social and environmental indicators revealed comparatively moderate effects.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".