AI-Powered Carbon Footprint Reporting for Freight Logistics

Use Case:

Predict and report CO₂ emissions per shipment using AI-enhanced models that comply with EN 16258 standards supporting decarbonization goals and customer sustainability reporting.

Outcome:

• Accurate, EN 16258-compliant carbon footprint reports and forecasts;
• Cloud-based API services for real-time prediction and post-shipment reporting;
• Integration with company ERP for automated sustainability tracking;
• Use of physics-based VECTO simulation and regression-based predictive models;
• Business model enabling 10% incremental sales via competitive carbon transparency.

Ecosystem Support:

• StairwAI technical and financial support;
• AI expert partnership with Ideas Forward;
• Integration with HERE Maps and AWS for data enrichment and deployment.

AI Relevance:

• Demonstrates practical AI application in a traditionally low-tech industry via:
• AI-enhanced simulation for real-world logistics problems;
• Carbon-aware predictive modeling to support Scope 3 reporting;
• Low-barrier integration with logistics systems and ERP;
• Support for regulatory compliance and green procurement.

Summary:

In response to increasing customer demand for carbon emission tracking, DS Transport Solution, a freight forwarder operating across Europe, developed the FreightEmissions platform with support from the StairwAI program. The system enables carbon footprint prediction before shipment assignment and reporting after delivery, allowing DS Transport to align with Scope 3 GHG emissions tracking and the EN 16258 standard. The tool uses a hybrid of methods: 1) Physics-based simulation (VECTO) for estimating energy usage of freight trips, 2) Carrier-specific regression models for predicting fuel consumption based on technical parameters (payload mass, engine specs, drag, etc.), and 3) Geo-processing (via HERE Maps) to analyze terrain and road gradients for more accurate energy estimation.

Two cloud-deployed APIs—one for reporting and one for prediction—output detailed metrics in JSON format, including origin/destination, energy consumption (TTW and WTW), and GHG emissions. These APIs are integrated with the company’s ERP system, automating CO₂ reporting during both quote generation and shipment confirmation. The pilot reached TRL 7, and the tool is already contributing to business differentiation. The company forecasts a 10% increase in sales in 2023 from winning tenders requiring environmental disclosures. Moreover, it opens the door to a data-driven pricing strategy based on emission intensity, enhancing both competitiveness and environmental responsibility.