The ever-increasing human population, and therefore the ever-increasing need for food, has had a huge impact, both directly and indirectly, on the environment and the climate. In order to lessen the severity of these effects, efforts are being undertaken to cut down on carbon emissions. The estimation of foodstuffs carbon footprint is nuanced and calls for collaboration of all food supply chain stakeholders. A carbon footprint monitoring system for the privacy of associated parties is needed. This paper introduces a recent block chain implementation in food processing and transport stages to track carbon footprints. A system that monitors the effect that food processing and transportation businesses have on the environment has been developed by the proposed system. This system protects the enterprises' confidentiality by using cluster-based data. The proposed carbon footprint chain was completed, and its performance and latency measured under various scenarios. The CFC method can therefore be modified effectively to heterogeneous means of transport. It assumes that trucks have a heavy on-board diagnostics system to record the carbon footprint. It proves that it can deploy block chains without any scalability concerns with a greater number of nodes. © 2023 Elsevier B.V., All rights reserved.