Bibliographic record
Abstract
Hydrogen production has been dominated by gray hydrogen (hydrogen produced from fossil fuels without carbon capture). Historical data for 2019–2021 show nearly steady global production of and demand for hydrogen, with an annual average of 92 Mt (million tonnes) for each. Both global hydrogen production and demand are expected to grow to 180 Mt in 2030 in compliance with the Net-Zero Emissions by 2050 (NZE) scenario of the International Energy Agency (IEA), which aims to bring CO2 emissions down to net zero by 2050. Recently, green hydrogen (hydrogen produced via water electrolysis using electricity from renewables) has received increased attention, with the 11 countries (Australia, the United States, Spain, Canada, Chile, Egypt, Germany, India, Brazil, Oman, and Morocco) identified as top producers expected to produce 15.9534 Mt altogether in 2030. All of these countries, except Spain, Canada, and Germany, were classified by the global Hydrogen Council as having optimal green hydrogen production potential. Blue hydrogen (hydrogen produced from fossil fuels with carbon capture) and green hydrogen together constitute clean hydrogen. The share of clean hydrogen in global total final energy consumption (TFEC) was less than 0.1% in 2020. In alignment with the 1.5 °C pathway of the International Renewable Energy Agency (IRENA), which aims to limit the global average temperature rise to 1.5 °C above pre-industrial levels, this share should grow to 3% in 2030 and 12% in 2050, with the aim of producing 154 Mt of clean hydrogen and its derivatives in 2030 (and 614 Mt in 2050) compared to only 0.8 Mt in 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".