The Marine Benchmark Methodology

The Marine Benchmark Methodology

The General Methodology

A common analytical framework for turning complex maritime data into connected, traceable and decision-ready intelligence.

Marine Benchmark is built as one internally developed Data Foundation. Vessel movements, voyages, geography, weather, cargo, capacity, energy, emissions, regulation and risk are handled within a common model rather than brought together from separate legacy products and series of acquisitions.

That matters in practice. It means information can be compared and combined across the platform without first reconciling different underlying definitions, and the analytical context can be retained from an individual vessel or event all the way to fleet, market and global views.

From source data to maritime intelligence

Data is qualified, interpreted and placed in context before it becomes part of the analytical model.

Maritime source data varies widely in coverage, frequency, quality, format and definition. Marine Benchmark therefore validates, reconciles, normalises and contextualises incoming information before it contributes to derived intelligence.

The process spans AIS, vessel and stakeholder information, maritime geography, weather and ocean data, regulatory information, risk data and other specialist sources. Automated processing, advanced analytics and AI are combined with maritime domain expertise to determine what the information represents and how it should be used.

Observed · Reconstructed · Derived · Predicted

A digital model of the maritime world

Vessels are analysed together with the physical, commercial and regulatory environment around them.

Marine Benchmark maintains a structured representation of global maritime geography, including ports, terminals, named berths, anchorages, yards, passages, canals and straits, offshore assets, EEZs, regulatory areas, war and risk zones and other maritime geographies.

These objects are linked to vessels, voyages, events, stakeholders, environmental conditions and regulation. The result is a model that can move naturally between operational detail and aggregated analysis without switching between disconnected data environments.

Global · Market · Fleet · Operator · Vessel · Voyage · Port · Terminal · Berth · Area · Event

High granularity through time

The platform keeps the connection between what happened, where it happened and the conditions at the time.

Marine Benchmark maintains global maritime history back to 2009 and continuously extends the same environment with current and forward-looking intelligence.

Historical context is retained where it matters. Ownership, vessel characteristics, infrastructure, regulation, geography and risk can therefore be interpreted according to the conditions that applied at the time rather than through today's state of the maritime world.

Where the analytical question requires it, models operate at high temporal granularity for individual vessels. Historical information remains active and can be reprocessed as methodologies, reference data and underlying information improve.

Connected intelligence

The value of the platform increases when relevant relationships can be preserved across domains.

Vessel movement can inform voyage intelligence; voyage activity can strengthen capacity and performance analysis; weather and ocean conditions can explain operating conditions; and fleet structure can support emissions, regulation and long-term scenario modelling.

AI and advanced analytical methods help identify and test relationships across the platform. Maritime expertise remains central to deciding which relationships are technically, operationally and economically meaningful.

Energy, fuel & emissions

Vessel-specific modelling at high temporal granularity.

Marine Benchmark combines vessel-specific technical information with observed and reconstructed operating activity to model energy demand, fuel consumption and emissions through time.

Relevant weather and ocean conditions are incorporated where they affect vessel operation, energy demand and performance. Proprietary algorithms then translate the operating context into vessel-level energy and fuel intelligence, while keeping the results connected to the underlying vessel activity.

Established emissions experience on a modern platform

The current models benefit from emissions-modelling knowledge developed by the founders since around 2000, including early work within a European Commission project on CO₂ accounting for shipping. That experience has been substantially developed through new technology platforms, richer datasets, empirical research, regulatory projects and academic collaboration. Relevant methodology has also been independently certified.

CO₂ · CO₂e · CH₄ · N₂O · NOₓ · SOₓ · PM · Black Carbon

NOₓ: regulatory and real-world views

Two complementary approaches provide both rule-based and empirical perspectives.

For regulatory analysis, Marine Benchmark can represent the NOₓ level associated with the applicable rule framework and the technical characteristics of the vessel and engine. This provides a consistent view of the regulatory level - in other words, what is permitted under the relevant requirements.

Alongside this, Marine Benchmark maintains a separate real-world NOₓ model designed to reflect how emissions behave in actual operation. The model has been developed and tested in university and research-institute projects and back-tested against approximately 30,000 sniffer measurements.

Keeping the regulatory and real-world perspectives distinct makes it possible to analyse both compliance-related thresholds and expected operational emissions without presenting one as a substitute for the other.

Lifecycle boundaries and regulatory frameworks

The same vessel-level foundation can be structured for different reporting and compliance requirements.

Marine Benchmark can apply Tank-to-Wake (TtW), Well-to-Tank (WtT) and Well-to-Wake (WtW) boundaries depending on the purpose of the analysis.

The modelling foundation can also support regulatory measures, reconstructed reporting and forward-looking trajectories under frameworks including IMO GHG4, IMO CII, GLEC Framework 3.1 / Chapter 3.2, EU MRV, EU ETS and FuelEU Maritime.

CII & trajectories · EU MRV reconstruction · EU ETS exposure · FuelEU performance · ECA analysis · Regulatory scenarios

One emission. Any relevant dimension.

The model can answer not only how much was emitted, but where, when, by whom and under what conditions.

Because emissions remain connected to the wider Data Foundation, supported emissions can be aggregated, segmented and compared across the dimensions that matter to the question. Several dimensions can be combined in the same analysis.

Vessel · Fleet · Operator · Owner · Vessel type · Technology · Engine · Fuel · Voyage · Activity · Port · Terminal · EEZ · ECA · Trade lane · Geography · Time

The same underlying CO₂, NOₓ or Black Carbon intelligence can therefore support a global emissions view, an operator comparison, a technology assessment, a geographic exposure analysis or a regulatory study.

History, now and what happens next

Operational time horizons are connected within the same analytical environment.

Long-Term Outlooks

Quantify how fleet development, technology and regulation shape emissions over decades.

Marine Benchmark’s Fleet Evolution & Forecast extends analysis up to 25 years by modelling how the world fleet may develop through renewal, accelerated retirement, capacity growth, technology uptake, fuel transition and changing operational patterns.

The same framework can translate those structural changes into long-term energy and emissions trajectories. For an IMO feasibility study concerning a potential Mediterranean Sea NOₓ Emission Control Area (NECA), Marine Benchmark modelled vessel-level NOₓ emissions across a 25-year horizon under alternative scenarios — comparing expected emissions development with and without the introduction of a NECA.

This makes it possible to move beyond forecasting the fleet itself and quantify the long-term impact of regulatory measures, technologies and operational change on emissions at vessel, fleet, regional or global level.

From vessel intelligence to global scenarios

A common analytical structure makes it possible to scale findings.

Technologies, fuels, operational improvements, regulatory measures and other changes can be evaluated from individual vessels and projects through to relevant fleets, operators, trades, geographies, market segments and global shipping.

Combined with fleet evolution modelling, the same foundation can support long-term scenarios across capacity, operations, technology adoption, energy demand, fuels, emissions and regulation.

Traceable by design

Derived intelligence remains connected to the maritime activity behind it.

Marine Benchmark maintains the relationship between analytical results and the vessels, voyages, events, geographies and activity from which those results were developed. Aggregated intelligence can therefore be investigated towards its underlying components.

Traceability is part of the analytical architecture rather than an additional reporting layer.

One Data Foundation. Nine connected domains.

Each domain can be used independently, while the common model provides the connections when a question crosses domains.

Built for enterprise use

Applications, APIs, analytical datasets and systematic data services use the same underlying Data Foundation.

Customers can use Marine Benchmark in direct applications, quantitative workflows, internal systems, AI and model development, regulatory analysis and specialist maritime research without creating separate analytical versions of the maritime world.

Discuss the methodology and Data Foundation

Talk directly with the team behind Marine Benchmark data models.

For enterprise evaluations, quantitative research, regulatory projects or specialist analytical requirements, our team can discuss methodology, validation, certification, coverage and traceability in greater depth.