Marine energy transition with LNG and electric batteries: a technological adoption analysis of Norwegian ferries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The article investigates factors associated with the relative success in adopting two specific alternative marine energies (liquefied natural gas [LNG] and electric batteries) in the Norwegian ferry market. This specific market segment is an interesting case study as its national-flagged fleet boasting the largest number of ships using alternative marine energies in comparison with the other countries of the region and the world. Design/methodology/approach A database tracking the yearly deployment of ships using a different combination of LNG and electric batteries was built from shipping lines’ online information and grey literature. The technological adoption approach was used to categorize different groups of users at each step of the adoption process and identify which factors separate the early adopters from the other groups of end-users. The compiled data allow tracing the changing distribution of Norwegian ferry operators along the conceptualized technology adoption curve. Findings Results indicated that the Norwegian ferry market matches required conditions to pass the “chasm” of uncertainties associated with transitioning to new technology. Some disparities between the adoption of LNG and the electric batteries in the Norwegian ferry markets are observed. Originality/value To the authors’ knowledge, no study has explored the adoption of new energies in the maritime industry based on the technology adoption process through a similar perspective. The analysis is helpful to shed light on the barriers associated with a high level of uncertainties when it comes to adopting new marine energies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it