Finding winners in the hydrogen hype
Bibliographic record
Abstract
The global energy trade is set for its greatest transformation since the 1970s and the rise of OPEC (The Organization of the Petroleum Exporting Countries). Electrification is central to this as countries plough money into renewables to reduce emissions and enhance energy security. But electrification can take the world only so far. With higher carbon prices looming on the horizon, fossil fuel exporters and industrial sectors – as well as heavy-duty trucking, shipping and aviation – need alternatives to decarbonise. Most are looking to electricity-based fuels and feedstocks such as hydrogen, ammonia and methanol to replace hydrocarbons. This will revolutionise energy trade, with total trade declining by as much as 50% and virtually all remaining traded oil gas and coal being either completely decarbonised or backed by offsets. With 147 GWel (giga-watts electrolyser capacity) in announced projects, green hydrogen produced from renewable electricity is ahead of the game. And while its export supply chains are complex, requiring conversion into a ‘product’ to allow delivery, there is no exploration risk as in oil and gas projects. Worldwide, national hydrogen roadmaps are being passed, with virtually all oil and gas companies, utilities and industrials backing at least one hydrogen project. Focus is now shifting to future sources of hydrogen supply. Lenders will be drawn to locations with a proven track record of exporting natural resources, suitable conditions for low-cost renewable electricity and the potential for large-scale carbon capture. A few countries already stand out, but none more so than Australia. Using our proprietary research, we will present a case study evaluating hydrogen supply options from Australia, Saudi Arabia and Canada – delivered into key markets like Japan for different applications. We will also assess when costs will fall across the value chain – production, midstream and downstream – and reach parity to incumbent fuels.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.015 |
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".