Recovery rate of harvest residues for bioenergy in boreal and temperate forests: A review
Bibliographic record
Abstract
Harvest residues are an attractive woody biomass feedstock for bioenergy production. A portion of the total harvest residues are generally left in the cutblock due to technical and profitability constraints. A better understanding of the factors influencing the variability of residue operational recovery rate is important to inform accurately policy development on sustainable forest biomass procurement practices. We compiled the results of field trials from boreal and temperate forests to quantify the range of variation of residue recovery rates and to identify the main factors explaining this variability. The average recovery rate was 52.2%, with minimum and maximum values of 4.0 and 89.1%, and a near‐normal distribution around the average. The main factor contributing to this variation was country of operations, which encompasses aspects of bioenergy policy and markets, technological learning, and forestry context. A shift in bioenergy policy, a growth in (and a change in access to) bioenergy markets, and upward movements along the technological learning curve could increase residue recovery rates approaching the highest values observed in this study, such as those in Nordic countries (72% residue recovery), or even higher if economic and technological conditions keep improving. However, local stand conditions, especially in North America where natural variability is high among forest stands, may continue to constrain operational recovery of harvest residues. Our results suggest the need for the development of policies that define practices and thresholds based on the ecological suitability of ecosystems, with clear definitions and explicit standards for harvest residue inventory, quantification, and retention. WIREs Energy Environ 2015, 4:429–451. doi: 10.1002/wene.157 This article is categorized under: Bioenergy > Climate and Environment Bioenergy > Science and Materials
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".