A CFL-ontology model for carbon footprint reasoning

As the carbon emission becomes a serious problem, a lot of research works now focus on how to monitor and manage carbon footprints. One promising approach is to create a “carbon footprint aware” world to expose people to the carbon footprints associated with the products they buy and the services they use. Carbon footprint labeling (CFL) of products enables the consumers to choose their products not only based on quality and cost, but also based on their carbon footprints. Similarly, carbon footprints of common activities and services can also be labeled to enable informed choices. CFL can impact the supply chain operations as well. With the carbon footprint information, the carbon-footprint-optimal supply chain can be identified to model the supply chains with least carbon emissions. Existing carbon footprint management systems mostly rely on databases to maintain carbon footprint data. But database alone is not sufficient for carbon footprint labeling. In this paper, we develop an ontology model, CFL-ontology, to specify how products are produced, the processes involved in activities and services, and the computation functions to derive the carbon footprints of the products, activities, and services, based on the associated descriptions. With the CFL-ontology, reasoning can be performed to automatically derive the carbon footprint labels for individual products and services.