The Signal Ocean Platform Emissions Monitoring

Signal Ocean’s Emissions estimation algorithm exceeds 90 percent accuracy

Posted by
Signal Ocean Team
December 7, 2022

The Signal Ocean Platform: Vessel & Fleet Emissions Monitoring dashboard

To address the decarbonization initiatives and greenhouse gas emissions monitoring, Signal has included a series of analytics within the Signal Ocean Platform that provides a plethora of statistical measures for vessel pollution, carbon intensity efficiency and alignment with industrial standards’ targets.

The Signal Ocean Platform allows users to compare a vessel's or fleet's performance against the IMO's Carbon Intensity Indicator, Poseidon Principles and Sea Cargo Charter targets. The Platform covers all tankers above 25Kt, dry cargo, and LPG vessels.  We will extend the platform's coverage to include smaller wet later this year, with Container and LNG ships estimates added after that. Signal Ocean will continue to invest in this area and respond to new measures as the IMO introduces them.

Using the Signal Ocean Platform technology to estimate tonnage for a cargo, our customers can consider the CO2 impact when chartering ships alongside their Time Charter Equivalent (TCE) rates. Key influencing factors include not only the emissions during the laden sea passage of a voyage but also ballasting, route deviations and other operations, all in conjunction with technical ship characteristics, age, shipyard/design, use of scrubbers and type of fuel used.

The Signal Ocean Platform estimates vessel emissions, such as CO2, using Signal’s rich voyage structure, AIS data enhanced with a rich proprietary geospatial database. The enhanced AIS data allows the Platform to map a given vessel's movements down to the jetty level.

Overall voyage is converted into a single stream of commercial activity where all stops for bunkering operations, idle times, repairs, loads and discharge operations have been taken into account. Ballast and laden legs and SECA navigation times are clearly defined at sea. Models estimating consumptions for each operation include vessel particulars, country built, year built, scrubber fitting, operational conditions and vessel speeds. Fuel consumption is mapped to different types (e.g., VLSFO, MGO, HSFO) based on the area that vessels have been trading, the information about whether a ship is scrubber fitted, and emissions are derived from consumptions using IMO references.

Vessels operate very differently based on size, which is highly influenced by market levels. They journey in different geographical areas, in different periods, under different speeds, with different designs, consuming different fuels and having different trading strategies. Thus, the algorithm focuses on presenting and breaking down the output emission data in the most detailed manner possible, producing all the essential information that any analyst might need.

Signal emissions models validation

DNV has issued a formal Letter of Professional Opinion that recognizes the validity and robust nature of Signal Ocean's emissions estimates.  

Following a detailed review, Dr George Dimopoulos, Principal Specialist and Head of DNV Maritime's R&D and Advisory Unit, said: 

"After the review, our qualified professional opinion is that the subject Vessel Emissions Algorithm of Signal Ocean meets the required standards detailed in MARPOL's carbon intensity calculation guidelines. DNV invests substantially in research, development, and innovation to provide value to our customers and society. We will continue to actively support initiatives and developments that advance digital smart solutions to tackle decarbonization of the maritime industry."

Link to the DNV press release: 


The maritime community faces an unprecedented challenge in complying with the IMO's targets for decarbonization, but industry stakeholders are working hard to improve emissions performance.  

Robust, consistent, data-driven estimates for vessel emissions performance are the crucial next step in providing greater transparency and allowing market participants to make informed decisions.

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