Optimizing Hybrid Energy Systems for Sustainable Development in the Canadian Arctic: A Case Study of Arviat, Nunavut
Bibliographic record
Abstract
The optimization of hybrid energy systems for sustainable development in remote Arctic communities is crucial to addressing the unique challenges posed by harsh climates, high energy costs, and the environmental impact of traditional energy sources.This study focuses on Arviat, a community in Nunavut, Canada, and utilizes HOMER Pro software to conduct a comprehensive analysis of combined energy solutions.The economic feasibility, environmental impact, and social benefits of integrating renewable energy sources (RES) with existing diesel systems are evaluated.The findings indicate that, while the initial capital investment required for implementing a hybrid microgrid is significantly higher than that for conventional diesel-only systems, the long-term advantages are substantial.These advantages include increased energy resilience, reduced greenhouse gas (GHG) emissions, lower operational and maintenance costs, and the potential for local job creation.Furthermore, the study highlights that hybrid systems, by reducing dependency on fossil fuels, can provide a more reliable energy supply in regions where logistics for fuel delivery are often hindered by extreme weather conditions.The optimization of hybrid energy systems not only aligns with Canada's net-zero emissions targets but also contributes to the economic and social sustainability of remote communities (RCs) by fostering local energy independence and resilience.This research underscores the transformative potential of adopting hybrid microgrids in isolated Arctic regions, advocating for policies and investments that prioritize sustainable and socially responsible energy strategies in line with national and international environmental goals.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".