Shore power mitigates the prevailing carbon leakage driven by maritime market-based measures: A dynamic system interpretation
Bibliographic record
Abstract
Implementing market-based measures (MBMs) to manage maritime carbon emissions is a policy approach to reduce greenhouse gas (GHG) emissions in the shipping industry. However, as cargo shipping is significantly exposed to international trade, regional carbon pricing could lead to carbon leakage, undermining policy effectiveness. Adopting low-cost alternative energies is one method to mitigate maritime carbon leakage. Shore power, which converts ship auxiliary engines to land-based energy sources, is recognized for its ability to reduce emissions. However, its broader adoption is hindered by high costs. To address the execution risks of maritime MBMs and the investment barriers to energy transition, this study simulates cost control and carbon leakage risk under a regional emission trading system with the participation of shore power. Utilizing the Quebec Emission Trading System as an experimental example, a policy-improved system dynamics model simulates the feedback among government, container shipping companies, and port. The results indicate that shipping companies could achieve stable profits over time with more than a 40% shore power upgrade rate, while GHG emissions would be reduced by at least 30%. Governments and ports should advance shore power coverage at container berths to eliminate carbon leakage risk under a stringent maritime MBM with high emission reduction targets. • A policy-driven port system simulation with maritime carbon pricing was conducted. • Shore power mitigates carbon leakage caused by maritime emission policies. • Ports will receive long-term returns from shore power upgrade. • Fuel price is a key factor affecting the performance of green port system.
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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 | 0.000 |
| 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) | 0.000 | 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".