The role of hydrogen in the decarbonisation of the steel industry : upstream and downstream in the UK and Ontario
Bibliographic record
Abstract
Currently the iron and steel industries are a significant contributor to global carbon emissions due to their reliance on fossil fuel powered processes. Use of technologies exploiting hydrogen as a fuel have gained prominence as a potential route to decarbonise the sector. This research offers a hitherto under-explored understanding of the enablers and barriers to industry adoption of hydrogen technologies, using the context of the steel industry in the UK and Ontario, Canada as case studies. Through thematic analysis of semi-structured interviews with key businesses and stakeholders across the steel network, we build a causal map which explicates the decision-making underpinning adoption of hydrogen technologies in the processing and production of steel. The outcomes will inform priorities for technological development and policy to support decarbonisation of steel manufacturing, a problem of international importance. Understanding the interdependency between decisions, uncertainties and goals are essential for informing effective strategy development in such a socio-technical problem. Causal mapping provides a means to visually represent the cause-effect relationship between relevant factors within a system. We explore issues with goals such as carbon emissions and ‘net-zero’, uncertainties related to carbon taxes, government policy, and hydrogen colour classification, as well as hydrogen embrittlement, costs and technology replacement in relationship to hydrogen adoption. The corresponding policy-facing causal map interprets this understanding into a decision-making tool to assist the journey to net-zero. We adopt an inductive reasoning approach by firstly analysing data gathered from the UK industry, developing a concurring hypothesis and testing this on the Canadian industry. Our paper presents the preliminary data and findings, and argues that the three main barriers to hydrogen technology adoption in the UK steel industry are: (1) Cost; (2) Supply; (3) Knowledge. This project is in collaboration with the National Manufacturing Institute Scotland (NMIS). NMIS have formed a conglomerate of industrial partners from the UK forging industry and furnace companies to develop hydrogen powered furnace technology.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".