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Carbon-ML = Product and Service Carbon Understood.

Open Source. Extensible. Global Schema.

Standardize carbon declarations for any product or service

Track & trace the why, what, who, where, when, and how of carbon

Measure emissions at the product and service level

Report embodied carbon along supply chains, regulators or at ports of entry and transfer

Supports and re-uses your existing product and services schemas and taxonomies

Vision

The Problem: Tracking, Measuring, Reporting Carbon Emissions is fragmented and uses unstructured data preventing carbon information scale.

The Vision: To develop an extensible open-source global taxonomy and schema that provides a trusted and visible report of the carbon embodied in each product at every point along the supply chain.

Objective

To have an extensible open ecosystem and related schema developed for the measurement of embodied carbon that is trusted and visible, open-source, adaptable for ease of implementation globally, and is technology agnostic.

To have the ecosystem produce a trackable measurement of embodied carbon associated with every product at every point along supply chains – upstream and downstream.

To have ecosystem users develop related solutions such as displaying a product’s embodied carbon on the product’s label so that companies, consumers, suppliers, governments, etc. can make more informed choices.

Schema

  • <CaRML>: An open extensible markup language supporting a collection of extensible schema to facilitate structured machine communications and declarations about the carbon CO2e associated with all economic activity at the individual product and/or service level.
  • The <CaRML> extensible schema is user driven evolution extending as a framework, not being fully proscriptive of any one solution or interpretation.
  • <CaRML> will be transparent with machine readable CO2e signaling in order to accelerate reporting across supply chains, creating awareness of carbon and then enable efforts to mitigate and reduce CO2e to create new higher valued products and services, and with <CaRML> become more certain financially, reputationally and from regulatory reporting perspectives.
  • There is no <CaRML> schema end point solution. <CaRML> is extensible and designed such that a only subset of the schema need be implemented to be useful.
  • Early <CaRML> schemas to include shaping and input from the key stakeholders within the ecosystem and build on schemas and taxonomies in other areas that are already developed.

Principles

As Carbon-ML combines declaring the measuring, tracking and tracing embodied carbon within any product with the development of an extensible taxonomy and schema utilizing open-source technical code.  The guiding principles use the principles from climate, product, sustainability, and technology taxonomies.

A main guiding principle is the ability for the ecosystem and schema to be adaptable for local, regional, and country based norms.


And, for the ecosystem and schema to evolve,

as other related and customized

ecosystems, products, supply chains evolve.

Basically tracking and tracing embodied carbon at each branching point….

for each tree as each branch changes.

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.