Use Cases

  • Carbon-ML supporting GS1 would allow for the “labelling” of approximately 100 million consumer products out of the box and be integrated with many inventory/supply chain systems.
    • Processing managers can compare based on Carbon quality
    • Consumers can make more informed decisions
  • Carbon-ML supporting international trade/customs and border agents, policies such as the EU Carbon Border Adjustment Mechanism (CBAM)
    • More accurate and standardized assessment of reporting of embodied carbon in products in line with customs carbon border policies
  • Carbon-ML supporting Government and State regulators’ understanding of carbon related data, as standardized data allow for better comparability and tracking of embodied carbon within products and services.
    • Better assessment of procurement processes and service provider selections
    • Better assessment of legislation, regulations, and enforcement
  • Carbon-ML supporting financial markets investment decision making by providing more accurate tracing and tracking, and comparable representations of embodied carbon within products and services by companies.
  • Technology multiple integration issue, common framework language, network effects. keep evolving and supporting multiple systems and new products, etc.