| dc.description.abstract | This thesis has a purpose which is twofold. Firstly, several retrofit fuel reduction measures
used in the shipping industry are examined. The goal is to compare the actual effects of the
measures with the estimated effects presented in the literature. It is found that most actual
effects are lower than what the literature expects. The second goal of the study is to analyze
the measures in an economical way, seeking to find which, if any, of the measures are costefficient. This is done through calculations of marginal abatement costs and creating marginal
abatement cost curves for sixteen different combinations of factors including vessel type, fuel
cost and the different effects found in the previous part. Weather routing, trim and draft
optimizations and propeller polishing are the measures found to be cost-efficient in all
scenarios. Waste heat recovery, air lubrication, wind propulsion and biofuels are found to be
cost-ineffective in all scenarios.
The results from the marginal abatement cost curves are used to design “No regret”- and
“Zero-cost”-scenarios. A possible global reduction potential of between 77.5m-132m tonnes
of CO2 per year is calculated in the “No regret”-scenario, while the interval for the “Zero cost”-
scenario is 127m-181.5m tonnes of CO2 per year.
Finally, some barriers for implementation of the measures are presented and discussed. The
principal-agent problem, where the shipowner not necessarily reaps the benefit for his/hers
investment is one of the main barriers. The volatile fuel cost causing uncertainties in
investment calculations is also identified as important. Options for shipowners, regulators and
governments in order to reduce these barriers include market-based measures as fuel tax and
CO2-trading schemes, speed reduction measures as virtual arrival and speed limits as well as
energy-rating systems and government incentive schemes.
The thesis is built upon a literature review, a group of interviews and a survey, and does
contain some uncertainties. Firstly, the small sample size may lead to the data being less
generalizable and that single responses may be given too much weight. Secondly, the fact that
a large part of the respondents are headquartered in Norway may lead to biases, if the answers
and effects are varying geographically. Caution must also be taken when analyzing the
marginal abatement cost curves, as the influence of additive effects has not been considered. | en_US |
