EU MRV (Public emissions data) + Machine Learning

Methodology · Reporting year 2024

Fleet Overview — One Graded Line Per Operator

Folding every ship's CII grade and within-type rank into a single operator fleet table — size, rank spread, grade mix, and the CO₂-weighted at-risk share — that drills back down to the vessels behind it.

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The biggest fleets as CO2-weighted grade bars, ordered dirtiest to cleanest
Plain question: for an operator's whole fleet — where does each ship rank, what grade does it hold, and what does the portfolio look like as one line in a table?

Output per operator: fleet size, dominant type, median rank + spread (p25–p75), the A–E grade mix, and the headline at-risk CO₂% (the share of fleet emissions carried by D/E ships).

Coverage: 3,161 operators are folded into one row each, every row drillable back down to its vessels.

How this differs from Operator Score: Operator Score reduces a fleet to a single 0–100 number and finds peer operators. Fleet Overview keeps the fleet legible as a portfolio — the grade mix, the rank spread, and the CO₂-weighted at-risk share, plus a vessel drill-down. Same shared per-ship engine, a different roll-up aimed at a charterer or lender scanning a fleet table.

The big picture — four steps

  • Per-ship facts — grade (A–E) and rank (within type) for every 2024 ship, and attach its operator.
  • Operator table — aggregate ships into one row per operator (size · median rank · grade mix · at-risk COâ‚‚%).
  • Grade bar — picture each big fleet as a COâ‚‚-weighted A→E grade bar.
  • Drill-down — expand any row back into its vessel list, worst-first.

In one sentence: per-ship fleet_rank (within type) + grade A–E (within peer group) → aggregated into one operator fleet table (size · median rank · grade mix · CO₂-weighted at-risk share) → drillable back down to the vessel list.

Step 1 — Two honest per-ship measures, side by side

From the shared 2024 engine each ship carries two distinct, complementary comparisons — both true, both useful:

  • fleet_rank — the percentile position within its whole ship type (from Fleet Rankings); 100 = cleanest of its type.
  • grade A–E — the simplified CII rating within its tight size·age peer group (from Peer Benchmarking); A and E exist in every segment.

Plus the ship's operator (company_imo). The two measures are related but not identical: a ship can rank mid-pack across its whole type yet earn an A for being the cleanest of its own size·age cohort, or vice versa.

Every graded 2024 ship plotted by within-type rank and CII score, coloured by grade
Figure. Every graded 2024 ship plotted by its within-type rank (x) and its 0–100 CII score (y), coloured by grade. The two measures correlate but scatter — they answer different questions, so the fleet table carries both.

Step 2 — The operator fleet table

This is the headline deliverable: one row per operator, carrying what a charterer, lender, or regulator wants on a single line.

Column What it is
fleet_n · dominant_type how many ships, and what kind of fleet it is
median_rank · p25–p75 where the fleet sits within its type, and how consistent
grade_mix (A–E counts) the spread of A–E vessels, e.g. "A2 B5 C9 D3 E1"
at_risk_co2_pct the COâ‚‚-weighted share of fleet emissions carried by D/E ships

Table. The per-operator columns (analytics/fleet_overview.py · compute).

The at-risk headline — and why it's CO₂-weighted:

at-risk CO₂% = Σ CO₂ of the fleet's D/E ships / Σ CO₂ of the whole fleet × 100

the fraction of a company's emissions (not its ship count) sitting in poorly-graded ships.

Why weight by CO₂ and not count? A company's climate exposure is dominated by its biggest, busiest emitters, so the at-risk figure should ask "what share of the carbon comes from dirty ships?", not "what share of the hulls?". One efficient harbour launch can't paper over a fleet of thirsty workhorses — and, conversely, a few large dirty ships make a fleet at-risk even if most of its vessels are clean.
The at-risk CO2 percentage across operators, U-shaped distribution
Figure. The at-risk CO₂% across operators is U-shaped: 52.2% run zero at-risk carbon (small, clean fleets) while 26.5% sit above 80% — the metric cleanly separates clean portfolios from concentrated-risk ones.
Each big fleet's median within-type rank with p25 to p75 whiskers
Figure. Each big fleet's median within-type rank with its p25–p75 whiskers (here, the highest-ranked fleets). A tight whisker is a uniformly clean fleet; a wide one is a mixed portfolio.

Step 3 — Each big fleet as a CO₂-weighted grade bar

The table is precise; a picture is faster. For the biggest operators by CO₂ we draw each fleet as one horizontal bar split A→E by share of the operator's CO₂ (green = clean grades, red = D/E) — the same at-risk CO₂% from the table, now visible at a glance.

The biggest fleets as CO2-weighted grade bars, ordered dirtiest to cleanest
Figure. The biggest fleets as COâ‚‚-weighted grade bars, ordered dirtiest to cleanest. A green-heavy bar is a modern, efficient fleet; a wide red band flags an operator whose emissions are concentrated in its worst-graded ships.

Step 4 — Expand one row → the vessels behind it

A fleet table is only trustworthy if you can open a row and see the ships. Each operator row decomposes back into its vessels — type, size, age, fleet_rank, and grade — sorted worst-first, the exact drill-down an API or dashboard serves when a user clicks an operator.

Worked example — one fleet, summarised and opened up

A 12-ship ro-pax operator, as one table line: median rank 26 (p25–p75 16–58), grade mix A1 B3 C1 D1 E6, at-risk 70% of CO₂.

Why the at-risk figure is CO2-weighted: 58% of ships are D/E but they carry 70% of CO2
Figure. Why the at-risk figure is CO₂-weighted: 58% of the fleet is D/E, but those ships carry 70% of its CO₂ — its biggest, busiest ferries are the worst-graded ones.
The drill-down: vessels by 2024 CO2, coloured by grade, worst-first
Figure. The drill-down: the fleet's vessels, 2024 CO₂ coloured by grade, worst-first. The large E-graded ferries dominate the fleet's emissions; the cleaner A/B ships are older and smaller-emitting — exactly the concentration the at-risk headline captures.
Vessel Type Size Age Rank Grade COâ‚‚ kt
1 Ro-pax 18,663 13 15 E 82
2 Ro-pax 18,664 12 16 E 81
3 Ro-pax 29,858 24 27 E 72
4 Ro-pax 29,858 24 25 E 71
5 Ro-pax 10,438 22 17 E 36
6 Ro-pax 10,438 22 16 E 34
7 Ro-ro 13,073 24 6 D 21
8 Ro-pax 32,071 33 75 C 42
9 Ro-pax 27,230 37 77 B 49
10 Ro-pax 15,362 34 43 B 25
11 Ro-ro 23,986 27 53 B 16
12 Ro-pax 29,992 32 93 A 38

Table. The fleet's vessel drill-down, reproduced from mrv.db (worst-first). The grade is the absolute CII rating; the rank is relative position within the Ro-pax type.

Reading it: 7 of 12 ships are D/E by count (58%), but because the large, hard-working ferries are the E-graded ones, they carry 70% of the fleet's carbon. The single table line flags the fleet as high-risk; the drill-down names exactly which six ferries a decarbonisation plan must target.

Outputs, uses, and honest limits

What the insight emits. Per operator: fleet_n, total_co2_kt, dominant_type, median_rank / p25_rank / p75_rank, the n_A…n_E counts, grade_mix, and at_risk_co2_pct. The vessel drill-down reuses the per-ship grade/rank already in the cache.

Why it makes sense and adds value. It is a portfolio view — the grade mix and rank spread keep a fleet legible as a portfolio, not a single number. It is actionable — at-risk CO₂% points straight at the emissions that decarbonisation effort should target. It is auditable — any summary row opens into named ships on the same fair, peer-relative footing.

Honest limits. Operator identity is only published from 2024, so this is a single-year snapshot; a year axis (fleet grade-mix trajectory) arrives once 2025+ files land. Grade and rank are two different lenses (absolute CII vs within-type percentile) — a vessel can look good on one and average on the other, and both are reported on purpose. The table inherits every Peer Benchmarking caveat (CO₂eq basis from 2024, the 3,000nm activity floor, un-rated types excluded from the grade mix). And the code aggregates any operator with ≥1 rated ship, so robust portfolio reading still needs a few ships — which is why the charts focus on larger fleets.

"Ithaca gave you the marvelous journey. Without her you wouldn't have set out. She has nothing left to give you now. And if you find her poor, Ithaca won't have fooled you. Wise as you will have become, so full of experience, you'll have understood by then what these Ithacas mean"

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