The Relative Cost of Biomass Energy Transport
Bibliographic record
Abstract
Logistics cost, the cost of moving feedstock or products, is a key component of the overall cost of recovering energy from biomass. In this study, we calculate for small- and large-project sizes, the relative cost of transportation by truck, rail, ship, and pipeline for three biomass feedstocks, by truck and pipeline for ethanol, and by transmission line for electrical power. Distance fixed costs (loading and unloading) and distance variable costs (transport, including power losses during transmission), are calculated for each biomass type and mode of transportation. Costs are normalized to a common basis of a giga Joules of biomass. The relative cost of moving products vs feedstock is an approximate measure of the incentive for location of biomass processing at the source of biomass, rather than at the point of ultimate consumption of produced energy. In general, the cost of transporting biomass is more than the cost of transporting its energy products. The gap in cost for transporting biomass vs power is significantly higher than the incremental cost of building and operating a power plant remote from a transmission grid. The cost of power transmission and ethanol transport by pipeline is highly dependent on scale of project. Transport of ethanol by truck has a lower cost than by pipeline up to capacities of 1800 t/d. The high cost of transshipment to a ship precludes shipping from being an economical mode of transport for distances less than 800 km (woodchips) and 1500 km (baled agricultural residues).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".