The Greece-based METIS Cyberspace Technology is about to launch shipping’s first tool to predict the trade-off between emissions reduction and debt servicing for ships financed under the Poseidon Principles.
To remind, the Poseidon Principles offer a framework for financial institutions to lend in line with International Maritime Organization goals to halve greenhouse gas emissions by 2050.
In fact, 20 institutions have signed up, representing over US$150 billion in loans, more than a third of shipping’s global financing.
Assessing whether ships merit further investment to keep pace with the IMO average efficiency ratio (AER) underpinning the Principles will be key but, to date, exact emissions targets have not been forthcoming.
In light of the situation, Serafeim Katsikas, Chief Technical Officer, METIS says the data analytics specialist has nonetheless created the first viable methodology allowing owners to predict whether their ships would benefit most from investment, a change in operating profile or disposal in response to advancing emissions rules.
Namely, the METIS Poseidon Principles Emissions INDEX aims to help owners outperforming AER seek to lower borrowing costs.
“The owner calculates an individual ship’s standard deviation from the AER target, then considers the impact of investments or operating guidance on overall costs including outstanding debt. These calculations also offer a prediction on the costs of servicing debt in the context of emissions restrictions and therefore an assessment of the ship’s viable lifespan.”
….Serafeim Katsikas added.
Currently, over 250 ships use the METIS cloud platform, which combines automated data acquisition with high-grade analysis for fleet managers, chartering departments and ship personnel.
As explained, an owner facing suboptimal AER can also use the METIS Index to identify the impact of different shortcomings on ship performance. In its next phase METIS would add its widely used scenario-based analysis to predict the impact of individual technology upgrades.
“The machine learning METIS deploys retrains automatically every month and evaluates itself every seven days, so we can find out the correlation between weather, hull fouling, power use, fuel efficiency and so on. This is invaluable for evaluating new technologies, but also for voyage analysis for correcting common errors.”
…Serafeim Katsikas concluded.
