Transportation planning in multiple bioenergy value chains: A literature review
Bibliographic record
Abstract
Transportation of biomass has long been a significant obstacle to the efficient and large production of bioenergy, as it accounts for a considerable portion of the value chain costs. Over the past two decades, researchers have developed and analyzed suitable supply chains, equipment, and technology for biomass utilization. With state-of-the-art systems now in place, it is possible to optimize the transportation planning efficiency at the operational level to achieve both economic and social objectives. This optimization ensures that the right resource is selected from the right supplier to produce a more sustainable energy source. This article proposes a literature review assessing the current status on biomass transportation and identifying the trends and ideas for improvement of biomass transportation planning, specifically at the operational level. A total of 146 publications from 2009 to 2023 were selected using keywords related to transportation, biomass, and improvement. The papers were reviewed to investigate the methodologies, mechanisms, and key metrics used by the authors to efficiently implement and support transportation planning within the biomass value chain. Our review identified seven transportation efficiency mechanisms (EMs): resource sharing, joint decision making, multimodal integration, transit preparation, financial agreement, information sharing, and local feedstock integration. We also evaluated the consideration of the economic, environmental, and social factors in assessing the performance of these EMs. Our findings revealed a scarcity of research in operational-level transportation planning for biomass that incorporates EMs, particularly the transportation resource sharing EM, even though it has proven to be profitable at the tactical level. There is also a lack of assessment of these EMs considering the three dimensions of sustainability: economic, environmental, and social. • Reviewing articles on enhanced biomass transportation planning to support biomass supply from forestry, agriculture, and municipal waste. • Identifying seven collaborating mechanisms such as resources sharing that significantly influence transportation efficiency. • Describing sustainability indicators -economic, environmental, and social- for evaluating the performance of the mechanisms. • Identifying gaps/challenges that are hindering a comprehensive understanding of the implementation of efficiency mechanisms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".