Key Components of the Methodology

• Static Emission Factors:

  • The Logistics Carbon Twin utilises static emission factors (measured in kg CO2e/tonne.km) for calculating TTW emissions. These factors serve as a baseline for estimating emissions, particularly in cases where shipment-specific data is limited or unavailable and provide a benchmark for dynamic emissions. The free carbon emission tool (insert link) uses static emission factors as well.

  • Formula:

CO2e=Estimated Distance × Estimated Weight × Static Emission Factor

• These factors provide a baseline for estimating emissions without requiring real-time adjustments.

• The static emission factors are derived from the transport emission intensity values published by the Global Logistics Emissions Council (GLEC) Framework. For maritime transport, a single industry average value is used (assuming 1 TEU = 10 tonne). In contrast, for air transport, different values are applied based on the haul distance: long haul (>1500 km) and short haul (≤1500 km). The land transport factor is calculated using the average TTW values of container-carrying trucks, with biofuels and electric trucks currently excluded from consideration.

• Dynamic Data Integration:

  • The methodology incorporates dynamic data, including actual distances travelled and specific cargo weights, obtained through IoT GPS devices and other monitoring tools. This allows for a more accurate reflection of real-world conditions in the emissions calculations.

  • Example formula with dynamic elements:

CO2e=Actual Distance × Actual Weight × Dynamic Emission Factor

• Dynamic emission factors are adjusted in real-time based on operational data. For land transport, this includes the specifications of the truck and topographic data derived from GPS points. For maritime transport, the annual average carbon intensity of the transporting vessel is used where available. However, for air and rail transport, static values are currently applied.

• IoT and GPS Data:

  • IoT devices and GPS trackers play a critical role in providing precise data on routes and distances covered by transport vehicles. This ensures that emissions are calculated based on actual travel patterns rather than estimates.

  • These devices also monitor cargo conditions and operational parameters, such as load weights and vehicle types, which can significantly impact emissions.

Methodological Steps

1. Data Collection:

   • Collect operational data through IoT devices, GPS, and other monitoring tools.

   • Gather static emission factors from industry databases and regulatory sources.

2. Data Integration:

   • Integrate static and dynamic data to create a comprehensive model that accurately reflects real-world logistics operations.

3. Emission Calculation:

   • Calculate emissions using the integrated data and dynamic emission factors.

Emission calculation of different modes:

Land:

1. Extract the truck model and fuel type along with the shipment's origin and destination.

2. Obtain the truck's specifications.

3. As GPS points are ingested, emissions are calculated incrementally, utilising Google Maps for routing when points are far apart.

4. Road grade is calculated using elevation data from Google Maps.

5. Data is processed through a machine learning model trained on simulated emission data based on EU emission standards. The specifications and emissions for each truck model are publicly accessible.

6. The model produces dynamic emissions.

Sea:

1. Extract the ship's IMO details from the shipment form.

2. Retrieve ship-specific emission factors from the EU ship emission database.

3. Calculate ship-specific emissions based on AIS data streamed from the vessel during shipment.

4. For ships not included in the EU database, GLEC default values are used.

Air and Rail:

1. Due to the current lack of data on open air emission models and operational data, default values from the GLEC are used.

1. Reporting and Optimisation:

   • Generate detailed emissions reports for regulatory compliance and operational insights.

   • Utilise the data to optimise logistics operations and reduce emissions.

Summary

Naxxar Technology's Logistics Carbon Twin methodology combines static and dynamic data to deliver a highly accurate and real-time model for estimating GHG emissions in logistics. By focusing on actual operational data, this approach refines emission estimates and supports comprehensive reporting and optimisation of logistics processes, ultimately enhancing environmental performance. The methodology is designed to evolve over time, incorporating more real-time factors that influence emissions across all transport modes thereby improving the fidelity of the AI carbon twin.