Contribution of Biomass Supply Chains for Bioenergy to Sustainable Development Goals
Bibliographic record
Abstract
This work evaluates the relationships between bioenergy and related biomass supply chains and the United Nations Sustainable Development Goals (SDGs). Using Nilsson et al. (2016) seven-point scoring framework, the relationships between biomass supply for bioenergy and the SDGs were evaluated based on existing synthesis papers, modeling studies and empirical analyses, and expert knowledge. To complement this, contributions to SDG targets of 37 best practice case studies from around the world were documented. In reviewing these case studies, it was found that when supply chains are implemented appropriately and integrated with existing systems, they can have overwhelmingly positive contributions. Beyond directly contributing to SDG 7 (Affordable and Clean Energy), at least half of all case studies supported progress toward SDGs 8 (Decent Work and Economic Growth), 9 (Industry, Innovation, and Infrastructure), and 12 (Responsible Production and Consumption); however, the ways in which supply chains contributed often differed. Agricultural biomass supply chains (energy crops and residues) were most likely to contribute to SDGs 2 (Zero Hunger) and 6 (Clean Water and Sanitation), while waste and forest supply chains were most likely to contribute to SDG 15 (Life on Land). The development of bioenergy systems in rural and indigenous communities also indirectly supports societal SDGs such as SDGs 1 (No Poverty), 4 (Quality Education), 5 (Gender Inequality), and 10 (Reduced Inequalities). This work informs how SDGs can be used as a normative framework to guide the implementation of sustainable biomass supply chains, whether it is used for bioenergy or the broader bioeconomy. Recommendations for key stakeholders and topics for future work are also proposed.
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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".