Project Summary
The project aims to complete Research and Development on an innovative ‘Logistic Carbon Twin’. The focus will be on improving the methodologies from using static to dynamic GHG emission data. GHG emissions are calculated using default estimated emission factors for an entire mode of transport based on industry estimates. Naxxar’s project proposes using an AI model encompassing more accurate data sets via a dynamic estimation approach for cross-modal freight, vastly improving the accuracy of GHG emission factors. The project is at TRL 2 with the aim of achieving TRL 4.
Project Achievements
The project was structured into four main work packages (WP). WP1 focused on R&D for GHG emission calculations using the static Global Logistics Emissions Council ‘GLEC’ model. WP2 involved data collection and the development of a Proof of Concept (PoC) AI model for dynamic GHG estimation. WP3 centred on field testing the AI model to validate its accuracy and refine it based on real-world data, with concurrent iterative User Interface improvements. WP4 dealt with project management and reporting, ensuring adherence to timelines and objectives.
Conclusions
In conclusion, the Naxxar Tech project successfully completed the research and development of the innovative ‘AI Logistic Carbon Twin,’ focusing on enhancing GHG emissions reporting through dynamic data inputs. The wider social, economic and societal benefits would include, social, improve emission data for commercial transport would improve the decarbonisation strategy aimed an improved air quality. This will lead to lower levels of air pollutants like nitrogen oxides (NOx) and particulate matter (PM) especially around cites and ports.
Next Steps
1 Deploying with existing customers rolling out the full new “AI Logistic Carbon Twin” globally. 2. Development of a similar Dynamic AI Model for sea and air enhanced emissions monitoring, reporting and verification. 3. Development of payments and Carbon as a Currency features to empower emissions reduction strategies.
