Carbon Footprint Monitoring in Logistics

Carbon footprint monitoring in logistics is made possible by the rise of the Internet of Things (IoT). IoT technologies open up new possibilities to monitor, analyze and manage the carbon footprint across the supply chain at levels of detail not possible before. Solutions for carbon footprint monitoring in logistics that involve IoT sensor devices offer unprecedented visibility into the environmental impact of the movement of the goods across the supply chain. IoT enabled carbon emissions reporting is calculated based on effective transport data instead of the less accurate planning data. As the sensing devices are driving along with the cargo, companies can analyze emissions on a shipment level and use the collected data to make key business decisions.

Reducing a Company’s Carbon Footprint Through Logistics Optimization | White Paper

Carbon footprint monitoring in logistics
Are you interested in our white paper on ‘Reducing a Company’s Carbon Footprint Through Logistics Optimization’? Learn how to optimize your supply chain processes and eliminate inefficiencies with the help of supply chain visibility solutions and cargo tracking and monitoring.

Listen to our CEO introducing our service

Arviem provides uninterrupted cargo tracking and monitoring solutions in real-time on a pay-per-use basis. Besides carbon footprint monitoring and reporting in logistics; Arviem offers smart insights for supply chain professionals by monitoring the location and condition of cargo in transit.



Arviem solves the challenges of obtaining visible and intelligent trade by offering real-time end-to-end cargo monitoring services. As the only full-service provider for real-time cargo monitoring on the market, Arviem provides exceptionally accurate location and quality condition monitoring of cargo throughout the global supply chain. Our leading-edge solution will provide global supply chain stakeholders with on-demand access to real-time, transport-related data.

European Union

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 885924

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