Evaluating the Impacts of Supply and Demand-Side Interventions in Northern Remote and Rural Community Energy Systems
Bibliographic record
Abstract
Remote and rural communities located across Canada face several energy system related concerns such as high heating and electricity rates, dependence on imported energy, and low levels of energy security and autonomy.In recent years, significant progress has been made with regards to developing demand and supply-side technology-based interventions that allow remote and rural communities to address these problems in a manner that is both economically viable and environmentally sound.Two prominent interventions that fall within these categories are building-based envelope energy retrofits and biomass driven district heating grids.The former can solve many of these concerns as well as reduce fossil fuel consumption, and improve the communities' housing stock, while the latter is a reliable and dispatchable technology that utilizes a carbon neutral energy source (i.e.biomass) that is abundantly available in heavily forested regions of northern Canada.This research explores the potential benefits and tradeoff of these interventions when implemented in Canada's northern remote and rural communities.The MoCreebec Eeyoud indigenous community of Moose Factory, Ontario is used as the case study in the analysis.Results show that biomass driven district heating grids are an economically attractive alternative for remote community energy systems with reductions in cost of up to 45% relative to conventional diesel power generation.On the other hand, in rural community energy systems, biomass district heating grids are unable to economically outperform conventional grid electricity unless the true cost of the electrical transmission grid is considered.However, from a purely economic standpoint, it is preferable for these communities to invest in building-based demand-side interventions instead of a biomass driven district heating grid.Building-based demand side interventions such as upgraded First and foremost I would like to thank my supervisor Prof. Jean Duquette.Your support and guidance through these last two years have allowed me to develop and grow
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".