De-Risking Wood-Based Bioenergy Development in Remote and Indigenous Communities in Canada
Bibliographic record
Abstract
Remote and Indigenous communities in Canada have a unique opportunity to mobilize the vast amount of wood-based biomass to meet their energy needs, while supporting a local economy, and reducing greenhouse gas (GHG) emissions. This study realized in collaboration with five remote and Indigenous communities across Canada investigates the main barriers and potential solutions to developing stable and sustainable wood-based bioenergy systems. Our results highlight that despite the differences in available biomass and geographical context, these communities face common policy, economic, operational, cultural, social, and environmental risks and barriers to developing bioenergy. The communities identified and ranked the biggest barriers as follows; the high initial investment of bioenergy projects, the logistical and operational challenges of developing a sustainable wood supply chain in remote locations, and the limited opportunities for community leadership of bioenergy projects. Environmental risks have been ranked as the least important by all the communities, except for the communities in Manitoba, which ranked it as the second most important risk. However, all the communities agreed that climate change is the main environmental driver disturbing the wood-based bioenergy supply chain. To de-risk the wood-based bioenergy system, we suggest that stable and sustainable supply chains can be implemented by restoring community-based resources management supported by local knowledge and workforce. Using local knowledge can also help reduce the impacts caused by biomass harvesting on the ecosystem and avoid competition with traditional land uses. Including positive externalities to cost benefit analysis, when comparing bioenergy systems to existing energy installation, will likely make bioenergy projects more attractive for the community financially. Alternatively, supporting co-learning between partners and among communities can improve knowledge and innovation sharing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".