Business Models for Carbon Capture, Utilization and Storage Technologies in the Steel Sector: A Qualitative Multi-Method Study
Bibliographic record
Abstract
Carbon capture, utilization, and storage (CCUS) is a combination of technologies capable of achieving large-scale reductions in carbon dioxide emissions across a variety of industries. Its application to date has however been mostly limited to the power sector, despite emissions from other industrial sectors accounting for around 30% of global anthropogenic CO2 emissions. This paper explores the challenges of and requirements for implementing CCUS in non-power industrial sectors in general, and in the steel sector in particular, to identify drivers for the technology’s commercialization. To do so we first conducted a comprehensive literature review of business models of existing large-scale CCUS projects. We then collected primary qualitative data through a survey questionnaire and semi-structured interviews with global CCUS experts from industry, academia, government, and consultancies. Our results reveal that the revenue model is the most critical element to building successful CCUS business models, around which the following elements are structured: funding sources, capital & ownership structure, and risk management/allocation. One promising mechanism to subsidize the additional costs associated with the introduction of CCUS to industry is the creation of a ‘low-carbon product market’, while the creation of clear risk-allocation systems along the full CCUS chain is particularly highlighted. The application of CCUS as an enabling emission reduction technology is further shown to be a factor of consumer and shareholder pressures, pressing environmental standards, ethical resourcing, resource efficiency, and first-mover advantages in an emerging market. This paper addresses the knowledge gap which exists in identifying viable CCUS business models in the industrial sector which, with the exception of a few industry reports, remains poorly explored in the academic literature.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".