Energy Technology Foresight 2030 in Russia: An Outlook for Safer and More Efficient Energy Future
Bibliographic record
Abstract
Russia is one of the key players at the world's energy markets. The country's plans to further research and innovation in the sector impact the world energy outlook. The paper examines the energy block of the Russian S&T Foresight 2030, developed by experts in 2011-2013 and approved by the Prime minister in January 2014. The official document, which covers six areas, including ‘Energy efficiency and energy saving’, defines the key science and technology (S&T) areas Russia has to embark upon in order to boost its competitiveness. The energy part of the study covers global challenges, threats, and opportunities for Russia, prospective innovative markets for its products and services. Moreover, Russia's innovative technologies and products are assessed, including the potential demand and competition aspects and benchmarking against global leaders. The paper features major outcomes of the energy block and puts the exercise in a comparative perspective with similar international studies of Kazakhstan, Germany, Canada, the UK, USA and international organizations. The author concludes that in Russia energy efficiency and energy saving priorities dominate the policy agenda, with relatively little attention to advancing renewable energy technologies. The Foresight horizon is also markedly shorter than that of similar studies in the OECD countries. Following international practice, in 2015 Russia plans to perform a new government-led S&T Foresight for the energy sector alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".