Towards an integrated decision‐making support framework for the sustainable production and use of biomass
Bibliographic record
Abstract
Abstract The decision‐making landscape to maximize the use of sustainable biomass resources, and achieve long‐term environmental and socioeconomic benefits, is complex with a high level of uncertainty in biomass supply and logistics, technical and economic performance of the biorefinery routes, lifecycle performance of the finished products, and other sustainability criteria. Numerous decision‐making support models have been developed but these models usually assess only a few specific aspects of technology, regulations, economic, environment, and society. Decision‐making support models with a limited capability to capture environmental and socioeconomic performance of the biorefining pathways are not able to identify the best available biorefining routes. This study reviews and discusses recent progress on the harmonization, standardization, and integration of the existing decision‐making support models that aim to improve the comparability of the results of these models when different pathways are being assessed and align the decisions made at the strategic, tactical and operational levels. With the growing number of climate‐change policies and greenhouse gas (GHG) emission reduction targets, national and international efforts to harmonize the input databases, the model assumptions and system boundaries, and the integration of the existing models have been increasing. However, the deployment of the integrated frameworks among the bioeconomy stakeholders that are capable of evaluating and identifying the promising biorefining routes with significant economic, social and environmental benefits is still not a common practice. This study proposes an integrated decision‐making support framework to identify cost‐competitive, low‐carbon fuel production pathways that are technically viable and can potentially provide maximum GHG emission reduction.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".