Accessibility of Energy from Renewable Energy Sources for Inhabitants of Arctic Cities
Bibliographic record
Abstract
The subject of the present research is the assessment of access of residents of Northern cities to energy produced from renewable energy sources (RES). The largest Arctic cities in Russia, Sweden, Norway, Finland, Denmark, the USA and Canada, located above 66 ° 33 ´ North latitude, are analysed. The importance of the study is due to the categorisation of access to RES as a fundamental good in the context of Sustainable Development Goals and fight against climate change. The work uses the index method, followed by ranking cities by the level of access to energy from RES. The following variables constitute the index: variety of operators, variety of types of energy sources, alternatives of energy sources, micro- and macro-generation support. It was found that residents of Kiruna and Tromsø have the best access to energy from renewable sources due to the support of initiatives at all levels, while Utqiagvik has the lowest indicator due to its isolation. Energy from renewable energy sources does not have a significant share in all of the cities under consideration; moreover, the market is often monopolised, which limits the choice and availability of various energy sources. Consequently, it is important to create suitable conditions for developing of RES on all levels, with the focus on micro level (as it makes ordinary people participate actively in the agenda, which is the key to support such remote areas with energy); otherwise it is unlikely to support the cities and territories of the region with energy from RES.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".