Bioenergy for Community Energy Security in Canada: Challenges in the Business Ecosystem
Bibliographic record
Abstract
Bioenergy represents a viable renewable alternative for the many off-grid remote communities in Northern Canada that rely on diesel-based energy infrastructure. Despite the abundance of forest-based biomass, bioenergy for heat and power in Canada is used primarily in industrial contexts. Community-scale bioenergy, although growing, has been limited. Supply chain challenges, institutional and policy arrangements, and community perspectives indicate a need to better understand the ‘business ecosystem’ for bioenergy in Canada. The ecosystem includes technologies, community contexts, suppliers, developers, and policy makers. In this study, we explore the bioenergy business ecosystem challenges and perspectives from supply-side stakeholders. Interviews were conducted with representatives from the government, industry, and community—all working in bioenergy. The results indicate the following challenges facing the bioenergy ecosystem, with respect to community energy security: lack of cross-jurisdictional consistency in legislation and policies across Canada, structural issues such as subsidized energy and utility ownership, and misdirected support for local capacity building in the bioenergy sector. We also find that the existing support systems are prone to misuse, pointing to efficiency gaps in investment flows. The insights that emerge from this work, especially from industry stakeholders, are meaningful for communities and policy makers alike.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".