Concurrent 20. Presentation for: Finding winners in the hydrogen hype
Bibliographic record
Abstract
Presented on Wednesday 18 May: Session 20 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. To access the presentation click the link on the right. To read the full paper click here
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".