Program initiatives of public authorities in the field of hydrogenation of the economy in a global perspective, as of the end of 2020
Bibliographic record
Abstract
In the years 2016-2020, there has been a significant acceleration in the development of technologies for the hydrogen energy use and their popularization in practice. The value of the global hydrogen market in 2018 was estimated at US $ 122 billion, predicted that it will increase to US $ 155 billion by the 2022. The appropriate policy framework has a major impact on the development of new technologies, in particular during research, prototype implementations and the initial phase of their commercialization. The spearheading countries intensively involved in the development and dissemination of hydrogen technologies are primarily : Japan, China, South Korea, Germany, France, UK, Scandinavian and Benelux countries, as well as Canada and the USA. The scale of the global development of hydrogen technologies is illustrated by the fact that at the end of 2019, vehicles with hydrogen fuel cells and the publicly accessible hydrogen refuelling stations serving them already operated in 18 countries. An effective use of the incurred expenditures undoubtedly requires the interested states to formulate an appropriate policy (strategy) for the hydrogenation of the economy, including, in addition to precisely defined long-term objectives, e.g. elements of support from public administration, assurance of: stable investment conditions and the necessary regulatory conditions. The article attempts to synthetically present the political framework, i.e. the functioning plans and programs as well as national strategies for the development of hydrogen technology and economy in 19 countries
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".