A systematic literature review of the logistics planning for sustainable bioenergy based on Forestry, Agricultural, and municipal solid waste value chains
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
Sustainable bioenergy production is essential for mitigating greenhouse gas emissions and reducing dependence on fossil fuels. The logistics of managing dispersed and low-value biomass from forestry, agricultural, and municipal solid waste value chains pose significant challenges, including high transportation costs, seasonal availability, and storage limitations. This systematic literature review examines the critical operations, including collection, transportation, and preprocessing, necessary to optimize bioenergy supply chains. A central contribution of this paper is an analysis of integrating biomass value chains through collaborative models that leverage shared infrastructure and adaptive logistics to enhance cost efficiency and resource utilization. It also identifies critical gaps in optimization models, particularly the lack of comprehensive multi-biomass value chain integration frameworks and limited consideration of uncertainties in logistics planning. The analysis highlights that while mixed integer linear programming models dominate, they often overlook cross-chain synergies and logistics. By examining 112 articles, we show that integrating forestry, agricultural, and municipal solid waste value chains through shared infrastructure and collaborative planning can significantly reduce transportation costs, enhance supply stability, and improve resource utilization in bioenergy systems.
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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 | 0.000 |
| 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