Assessing the potential for renewable energy systems in remote arctic communities as a means of reducing regional diesel fuel dependence
Bibliographic record
Abstract
The Arctic region faces many challenges related to combating the effects of climate change. Research has suggested that the region is overall more impacted by the rise in greenhouse gas emissions than other regions on Earth. More remote Arctic communities are dependent on fossil fuel sources, such as diesel fuel, due to their need for a reliable energy source and supply. However, energy sources like diesel fuel worsen the climate change situation both within the Arctic as well as globally. Climate models for years have shown how a feedback loop exists between the Arctic and the rest of the Earth’s natural systems and balance. Therefore, a more sustainable and lower-emitting energy technology is needed to replace or significantly displace the diesel fuel use in parts of the Arctic region. This will be an important step towards decarbonizing the region, which will add to the quality of life of the populations living in Arctic regions as well as contribute towards a more sustainable planet overall. This study aims to best inform how to take this first step towards renewable energy technologies becoming more widely implemented in the Arctic, particularly in those remote communities which rely on diesel fuel. This was accomplished through a comprehensive investigation into the existing energy systems in the Arctic, focused solely on the Arctic areas of: Iceland, Norway, Greenland, Canada, Finland, and Sweden. Through expert interviews, key barriers to utilizing renewables in the Arctic were identified and discussed. Following this investigation, a catalog of the current situation in the Arctic was created for use in future Arctic research into how to best implement renewables and to eventually reduce the use of diesel fuel. Interviews with energy industry experts and specialists were performed to better inform this final catalog. These expert interviews were conducted via an online survey sent to individual contacts by email, the data from which was analyzed and used to make the final determinations of this paper and to answer its research questions. The results of both the initial investigation, catalog, and interviews show that the primary concern when planning to implement renewables in the Arctic is reliability, cost, policy and/or regulations, and the potential disturbance of natural areas. The interviews resulted in findings which mirrored the literature review, as well as which elaborated on and were an extension of the full barriers towards renewable energy implementation in the Arctic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".