EU MRV (Public emissions data) + Machine Learning

The Methodology

How the EU MRV data becomes structured insight.
01/

PEER BENCHMARKING

The fundamental problem with comparing ships by raw emissions is that bigger ships emit more — but they also do more work. A fair comparison asks a narrower question: among ships of the same type, size, and age, which emit the least for the work they do.

The methodology uses k-means clustering within each ship type to identify natural peer groups based on size and age. A bulk carrier of 32,000 deadweight tonnes is not compared with a 180,000-tonne vessel; a five-year-old container vessel is not compared with a twenty-year-old one.

Within each peer group, the median CO₂ per tonne per nautical mile is calculated, and each ship’s percentile position against that median produces its rank.

02/

OPERATOR SCORING

A company’s emissions footprint is determined by the ships it operates. The methodology converts each ship’s emission pattern into a percentile score from 0 to 100, then combines all the scores belonging to one operator using a weighted average.

To make the operator comparison fair, the methodology then finds each operator’s genuine peers by building a fleet fingerprint. Cosine similarity identifies which operators run the same kind of fleet.

The final comparison is each operator against the median score of its closest peer operators.

03/

YEAR-ON-YEAR TREND

The methodology tracks each ship’s carbon intensity year by year and calculates how quickly it is improving. This is called the Carbon Annual Intensity Reduction, or CAIR.

That improvement rate is then compared against its peer group’s average. The comparison is what matters — a ship in a slow-moving group can still be a leader if it outruns its peers. A ship in a fast-moving group can still fall behind if it does not keep up.

04/

VOYAGE ANALYSIS

The MRV data reports each ship’s annual CO₂ in four mutually exclusive segments: intra-EU voyages, outbound voyages from EU to non-EU ports, inbound voyages from non-EU to EU ports, and emissions while at berth in port.

The composition matters because where carbon occurs determines what can be done about it.

05/

CII PROJECTION

The EU MRV data provides the inputs for a simplified version of CII: total CO₂ divided by capacity multiplied by distance sailed. The result is compared to a per-type required threshold to produce an A to E rating.

The required threshold drops a few percent each year. A ship that holds its emissions flat moves from C to D to E as the target descends beneath it. The projection traces each ship forward to 2030.

The projection shows when each ship will tip from C to D/E if no improvements are made. It also calculates how much it needs to improve each year to stay compliant.

06/

FLEET OVERVIEW

The fleet overview brings all the per-ship insights together at the operator level. It shows each company’s total emissions and what share of that comes from D and E rated ships.

This is the at-risk percentage CO₂. Weighting matters — a company may have only ten ships rated D or E, but if those ten are its biggest emitters, the percentage will be high. That is where decarbonisation effort really makes a difference.

THE LIMITS

This is independent research, built against the odds with limited resources and a lot of determination. It is shared in that spirit — open, still rough in places, and meant to be improved by others. Corrections, challenges, and contributions are all welcome.

The analysis is built on EU MRV data, which has specific scope and specific gaps. Honesty about these limits is part of the methodology.

The data is European in scope. Ships are required to report only their EU-related emissions — voyages to or from EU ports, and time at berth in EU ports. A vessel that spends most of its year in Asia and only occasionally calls at Rotterdam will have most of its operational reality outside the dataset. The analysis reflects EU-scope behaviour, not global behaviour.

The most recent data covers 2024 emissions, reported in 2025. There is always a delay between operational year and reporting year. The platform reflects what has been published; emerging changes in operations do not appear immediately.

The CII calculation here is simplified. The IMO’s full method includes correction factors for fuel type, ice class, refrigeration, shuttle tankers, and voyage exclusions. The simplified version uses CO₂ equivalent per capacity per nautical mile. For ice-class vessels, LNG-fuelled ships, or refrigerated cargo carriers, the simplified rating reads slightly worse than the official rating would. The simplified version is appropriate for relative comparison and trend analysis; it is not the certified IMO rating.

The capacity divisor uses deadweight tonnage for cargo ships and gross tonnage for non-cargo ships, following standard maritime industry convention. The division produces emissions per unit of carrying capacity, not per unit of cargo actually carried. A vessel sailing half-empty looks the same as one sailing full.

The analysis cannot account for many real-world factors that affect a ship’s measured efficiency. Port stays, anchorage time, frequency of port calls, weather routing, hull condition, crew quality, charterer behaviour, and trade pattern all influence what the numbers show. The peer grouping controls for type, size, and age. It does not control for these operational realities.

This is statistical analysis. It is not a replacement for engineering inspection, technical due diligence, or operational audit. The output is structured insight from public data, intended to inform decisions, not to substitute for direct examination of a vessel or operator.

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