Standards

International open standards for digital devices and their data measuring carbon, biodiversity and nature improvements.

Purpose and scope

The standards will be for the quality and trustability of the data from digital devices, and the robustness of measurement indicators, and scientific evidence behind them. Trustable Credit will co-develop, maintain, improve and publish the standards to help increase the robust quantification and qualification of emission reductions and biodiversity and nature improvements. This will better enable investment in the nature-based solutions that will assist in the journey to net-zero. Reliable decision-grade data – gathered using calibrated and triangulated digital devices – which can, for example, quantify negative impacts across value chains, is less than abundant. This makes the ‘bankable’ case for nature continuously challenging – and the globe’s stock of ‘green’ investors, limited.

The first step is the mapping of data journeys, a second step is to develop a controlled vocabulary – a common language and terms we are using to refer to, or label, concepts and methods. This will help us define agreed standards for digital data from digital devices measuring carbon biodiversity and nature improvements. There is need for an agreed language to make sure we can cross-reference, compare, share and combine data. The vocabulary/standards are also needed to provide a framework for grading the quality and accuracy of answers to:

  • How was the baseline measured? What were the scientific bases of them?
  • What are the data evidence of the validity of when and how of this?
  • Can the meta data verify veracity?
  • Can we share the data in a secure nuanced and governed way with provable permissions with independent approvers?
  • What are the digital proofs of sequestration process / improvement process and measurement against baseline data?
  • What are the scientific bases for measurement processes and mechanisms?
  • How robust and calibrated are the digital measurement tools?
  • Are the data’s attributes precise and complete?
  • Can the data evidence trail (data items) collected through time prove its immutability and trustability through its governance mechanism?
  • Is the data governance mechanism equitable and environmentally sustainable?

Example

We will get to a point where there is a definition for each grade’s rating: e.g.

RatingData item standardsDigital device standards
A
grade
Collected from calibrated and authenticated IoT sensors, drones or satellite data, ag-tech robots or farm management software. Items collected are required by evidence based calculators, based on the the latest scientifically verified & IPCC validated methods. Provenance triangulated via date and time and GPS markers. Collection metrics configured to agreed vocabulary and scientific methods. High levels of coverage, representation, completeness and precision. Recorded and indexed on a verifiable governed /immutable registry which has equitable accessibility and environmental sustainability. Metadata descriptions include tags to agreed vocabulary.
Data follows FAIR principles.
Strongly authenticated as valid source via security certificate or cryptographic public keys tested by API or data oracles. Provenance triangulated via date and time and GPS markers. Collection metrics configured to agreed vocabulary and scientific methods. Recorded and indexed on a verifiable governed / immutable registry which has equitable accessibility and environmental sustainability. Metadata descriptions include tags to agreed vocabulary.
D
grade
Manual data input into unverified calculator. Low levels of coverage, representation, completeness and precision. Recorded and indexed locally. Metadata uses terms outwith vocabulary.
Not FAIR principles compliant.
Unauthenticated devices. Not triangulated.
Recorded and indexed locally. Metadata uses terms outwith vocabulary.
Not FAIR principles compliant.
Example graded standards

The standards will cover data gathering, managing and analysis and governance journeys, and could eventually cover quality of sampling, and aggregation and infrastructure tools and APIs used in dashboards.

Do you have a project that could help us develop and test the standards? Join us!