Assessing the Sustainability of Miscanthus and Willow as Global Bioenergy Crops: Current and Future Climate Conditions (Part 2)
Bibliographic record
Abstract
Land-based bioenergy systems are increasingly promoted for their potential to support climate change mitigation and energy security. Building on previous productivity and efficiency analyses, this study applies the MiscanFor and SalixFor models to evaluate land use energy intensity (LUEI) for Miscanthus (Miscanthus × giganteus) and willow (Salix spp.) under baseline (1961–1990) and future climate scenarios, and Business-as-Usual (B1) and Fossil Intensive (A1FI) scenarios, projected to 2060. The study also assesses the impact of biomass transport on energy use efficiency (EUE) and quantifies soil organic carbon (SOC) sequestration by Miscanthus. Under current conditions, Miscanthus exhibits a higher global mean LUEI (321 ± 179 GJ ha−1) than willow (164 ± 115.6 GJ ha−1) across all regions (p < 0.0001), with energy yield hotspots in tropical and subtropical regions such as South America, Sub-Saharan Africa, and Southeast Asia. Colder regions, such as Europe and Canada, show limited energy potential. By 2060, LUEI is projected to decline by 9–15% for Miscanthus and 8–13% for willow, with B1 improving energy returns in temperate zones and A1FI reducing them in the tropics. Global EUE for Miscanthus declines significantly (p < 0.0001) by 21%, from 15.73 ± 7.1 to 12.37 ± 5.2 as biomass transport distance increases from 50 km to 500 km. Mean SOC sequestration is estimated at 1.20 ± 1.46 t C ha−1, with tropical hotspots reaching up to 4.57 t C ha−1 and some cooler regions exhibiting net losses (–7.93 t C ha−1). Climate change significantly reduces SOC gains compared to baseline (p < 0.0001), although differences between B1 and A1FI are not statistically significant. These findings highlight the importance of region-specific, climate-resilient biomass systems to optimize energy returns and carbon benefits under future climate conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".