Challenges and opportunities for ports in achieving net-zero emissions in maritime transport
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
• Shipping port activities contribute to global GHG emissions. • The maritime industry is under pressure from stakeholders and the IMO to achieve net-zero. • Challenges include economic, technological and policies to achieve net-zero emissions. • Opportunities and decarbonization pathways for shipping ports include low-emission fuels, and green shipping corridors. Shipping ports are vital nodes in maritime transport networks and play crucial roles in the global economy and international trade. Despite their economic importance ports have adverse effects on the environment. Air pollution and emissions of greenhouse gases (GHGs) are of great concern since the maritime industry accounts for 2–3% of global GHG emissions. The shipping industry is projected to grow on average at 2.1% annually for the next four years and is under enormous pressure from stakeholders and the International Maritime Organization (IMO) to curb GHG emissions to align with the Paris Agreement. The IMO strategy to cut GHG emissions from international shipping aims for a reduction of 20%, by 2030, and 70% by 2040, with respect to 2008 and achieve 100% reduction by 2050 to achieve net-zero emissions. The aim of this study was to investigate the role of shipping ports in overcoming challenges and maximizing opportunities to achieve net-zero emissions in maritime transport. Based on the existing literature from the past decade, this study highlights the magnitude of the problem, the challenges the sector is facing in terms of economic, technological and policy implications in achieving net-zero emissions. This perspective study offers potential solutions and opportunities for ports to achieve net-zero targets by improving infrastructure development, facilitating vessel emissions reduction, adoption of low-emission fuels, renewable energy adoption, and implementing green shipping corridors.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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