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 mitigation and energy security. Building up on previous productivity and efficiency analyses [1], this study applies the MiscanFor and SalixFor models to evaluate land use energy intensity (LUEI) for Miscanthus (Miscanthus × giganteus) and willow (Salix spp.) feedstock fuel under baseline (1961-1990) and future climate scenarios (B1 and A1FI, up to 2060). In addition, the study assesses the impact of biomass transport on energy use efficiency (EUE) and quantifies soil organic carbon (SOC) sequestration of Miscanthus. Under current conditions, Miscanthus exhibits a higher global mean LUEI (321 ± 179 GJ/ha) compared to willow (164 ± 115.6 GJ/ha), with energy yield hotspots located in tropical and subtropical regions such as South America, Sub-Saharan Africa, and Southeast Asia due to climate suitability. In contrast, colder regions in 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 moderate warming (B1) improving energy return in temperate zones and extreme warming (A1FI) reducing energy in the tropics. Overall, Miscanthus delivers higher energy returns, while willow offers broader temperature adaptability provided sufficient water is available. At baseline, global EUE for Miscanthus declines from 15.73 ± 7.1 to 12.37 ± 5.2 as transport distance increases from 50 km to 500 km, a 21% reduction, with similar trends observed under future climate scenarios. Mean global annual SOC sequestration is estimated at 1.20 ± 1.46 t C/ha, with tropical hotspots reaching up to 4.57 t C/ha. In contrast, some cooler temperate and boreal regions show net SOC losses down to –7.93 t C/ha. Projected gains decline to 0.62 ± 1.11 t C/ha (B1) and 0.49 ± 1.13 t C/ha (A1FI), reflecting strong climate sensitivity and regional variability. These findings underscore the critical need for region-specific biomass supply chains that strategically balance logistical and economic trade-offs with environmental sustainability, ensuring resilient, climate-smart bioenergy systems that maximize carbon gains and net energy returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".