The Role of Renewable Energy in Regional Energy Transitions: An Aggregate Qualitative Analysis for the Partner Regions Bavaria, Georgia, Québec, São Paulo, Shandong, Upper Austria, and Western Cape
Bibliographic record
Abstract
The study aims to identify the strengths, weaknesses, opportunities, and threats (SWOT) of renewable energy deployment in regional energy transitions for the regions of Bavaria, Georgia, Québec, São Paulo, Shandong, Upper Austria, and Western Cape, which comprise a political and scientific network called the Regional Leaders’ Summit (RLS) and RLS-Sciences, respectively. The results classify current renewable energy usage for electricity, heat and fuel production, existing renewable energy potentials, sound legal frameworks to support renewable energy, ongoing research and development activities and expertise in renewable energy conversion and storage as strengths. That fossil fuels still hold a significant share in gross inland energy consumption, energy-intensive industrial structures continue to be supported by fossil fuels and grid access is limited for renewables are identified as weaknesses. The main opportunities are green economies, climate change mitigation and technological innovations. Associated threats are demographic developments, lack of social acceptance and renewable energy resource volatility. We conclude that these regions’ energy systems could potentially enable the realization of a strong integration of renewables and cover partially distributed, decentralized energy systems with embedded energy storage, and the application of smart technologies. Furthermore, we discover that the role of governments in guiding and managing regional energy transitions is highly important.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".