Using automatic identification system data to determine the carbon footprint of shipping

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a validated methodology for AIS-based emission modeling that can be extended to other regions and vessel types.

🏢実務担当者:Shipping companies and port authorities can use this approach to monitor carbon intensity, verify CII compliance, and identify operational efficiencies.

🏛政策担当者:Demonstrates the feasibility of AIS-based independent monitoring to strengthen enforcement of IMO regulations and support maritime decarbonization policies.

This paper presents a comprehensive analysis of international regulatory instruments for the decarbonisation of maritime transport developed by the International Maritime Organization (IMO), including the Energy Efficiency Design Index (EEDI), Energy Efficiency Existing Ship Index (EEXI), Carbon Intensity Indicator (CII), Energy Efficiency Operational Index (EEOI), and Greenhouse Gas Fuel Intensity (GFI), with the aim of developing methods and models for the use of Automatic Identification System (AIS) data to determine the carbon footprint of shipping, using the Gulf of Finland in the Baltic Sea as a case study. The study examines the drivers behind the introduction of these instruments, their scope of application, regulatory targets, and calculation principles. Fundamental limitations of the existing regulatory framework are identified, including the design-based and aggregated nature of the indices, the limited range of greenhouse gases considered, the absence of spatial attribution, and reliance on self-reported data. The necessity of supplementing regulatory mechanisms with independent computational methodologies for emission assessment based on AIS data is substantiated. Particular attention is given to the Ship Traffic Emission Assessment Model (STEAM) as an example of a geophysical modelling tool enabling continuous, spatially resolved monitoring of emissions of greenhouse gases (CO 2 , CH 4 , N 2 O), as well as NO x , SO x , particulate matter, black carbon, and volatile organic compounds. The paper presents the results of the development and validation of software for the collection and analysis of AIS data in the Baltic Sea region, including route visualisation and mapping of carbon footprint intensity. A dynamic emission calculation model is proposed, accounting for regional geographical characteristics, hydrodynamic resistance (water, wind, waves, and ice), and the nonlinear dependence of specific fuel consumption on engine load. It is concluded that AIS-based methodologies should be institutionalised as a tool for objective monitoring, verification of reporting, and ensuring environmental safety in maritime areas.

• openalex https://doi.org/10.21821/2309-5180-2026-18-2-171-189first seen 2026-06-23 05:36:31