Feed-in-tariff is key to Japan’s current biomass power’s viability, even with environmental externalities
Bibliographic record
Abstract
Abstract Bioenergy is increasingly recognized as an effective tool for removing carbon dioxide from the atmosphere. However, its economic feasibility remains underexplored, particularly when accounting for environmental impacts. This study proposes a quantitative assessment framework to calculate the cost-benefit ratio of biomass power generation and to assess the sustainability of its supporting policy tools, such as feed-in-tariffs (FIT). The framework accounts for benefits through electricity generation and environmental externalities, namely emissions from feedstock production and procurement, such as the transportation of biomass materials. This allows for quantification and a detailed discussion of multiple environmental burdens of biomass energy and economic costs. As a case study, this framework was applied to a hypothetical biomass plant in Japan, which has the fifth-largest biomass market globally. We prepare several scenarios to consider diverse conditions within the Japanese biomass industry, including the types of biomass materials used (pellets versus chips), their sources (domestic versus international), and the biomass technologies employed. The results show that using pellets, predominantly imported, significantly increases biomass energy costs. The increase in cost is directly proportional to the quantity of utilized pellets and their transportation distances. However, pellet production location —whether in Vietnam or Canada—doesn’t significantly change the overall cost calculations in our study. Our result is consistent across various biomass technologies, showing that the high selling price under the feed-in-tariff system, rather than material type, supply origin, or transportation mode, plays the most critical role in economic feasibility, even when accounting for environmental externalities. Thus, decision-makers must reevaluate the efficacy of FIT policies for wood biomass powers, where fuel costs share a substantial portion. We also discuss its synergies with local industries and trade-offs with other land-use objectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".