The global hydrogen budget
Bibliographic record
Abstract
Hydrogen (H2) will play a part in decarbonizing the global energy system1. However, hydrogen interacts with methane, ozone, and stratospheric water vapour, leading to an indirect 100-year global warming potential of 11 ± 4 (refs. 2–5). This raises concerns about the climate consequences of increasing H2 use under future hydrogen economies3,5. A comprehensive accounting of H2 sources and sinks is essential for assessing changes and mitigating environmental risks. Here we analyse trends in global H2 sources and sinks from 1990 to 2020 and construct a comprehensive budget for the decade 2010–2020. H2 sources increased from 1990 to 2020, primarily because of the oxidation of methane and anthropogenic non-methane volatile organic compounds, biogenic nitrogen fixation, and leakage from H2 production. Sinks also increased in response to rising atmospheric H2. Estimated global H2 sources and sinks averaged 69.9 ± 9.4 Tg yr−1 and 68.4 ± 18.1 Tg yr−1, respectively, for 2010–2020. Regionally, Africa and South America contained the largest sources and sinks of H2, whereas East Asia and North America contributed the most H2 emissions from fossil fuel combustion. We estimate that rising atmospheric H2 between 2010 and 2020 contributed to an increase in global surface air temperature (GSAT) of 0.02 ± 0.006 °C. GSAT impacts of changing atmospheric H2 in future marker Shared Socioeconomic Pathway scenarios are estimated to remain within 0.01–0.05 °C, depending on H2 usage, leakage rates and CH4 emissions that influence photochemical H2 production. Trends in global H2 sources and sinks are analysed from 1990 to 2020, and a comprehensive budget for the decade 2010–2020 is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".