Shore power deployment strategies and policies including alternative fuels
Bibliographic record
Abstract
• Novel framework for analysis of multiple shore power policies. • Integration of alternative fuel adoption within the shore power deployment model. • Test case on the bulk carrier shipping network of the St. Lawrence and Great Lakes. • Best scenario includes shore power usage & zero-emission regulations, and strategic funding. This study presents a novel evaluation framework to compare and optimize shore power deployment policies across shipping networks. The framework considers a wide range of policy instruments, including incentives, emission reduction regulations, and public funding. In addition to assessing the business case for shore power adoption, it incorporates the influence of alternative fuels and their implications for deployment strategies, offering a more holistic approach than previous models have suggested. A test case on the St. Lawrence and Great Lakes dry and liquid cargo shipping network illustrates the framework’s application for policymakers. Under appropriate policies, shore power could cover 30–50% of vessels’ berth energy use. Achieving large-scale adoption would require an estimated government investment of $257 million USD in infrastructure. With projected cost savings of $240 million USD in external costs and a cumulative reduction of 2,556 kilotons of carbon dioxide equivalent by 2040, this scenario represents the most compelling policy option.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".