Standardised Processes

For digital devices and their data about biodiversity, carbon sequestration and nature improvement

The standardised processes will clarify the quality and trustability required of the data from digital devices/instruments. They will prescribe how devices prove they are accurately scientifically measuring, according to scientifically agreed processes, as defined by existing and emerging carbon and biodiversity codes. Evidence of device calibration and strong authentification, of data validation and verification and governance will be required. Trustable Credit will co-develop, maintain, improve and publish the standardardised processes covering the journey – from farm to finance – 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 from farm to finance, 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 standardised processes 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 of the methodologies used to get answers to:

• How was the baseline digitally measured? What were the scientific bases of the measurements taken and proof that the measurement process followed these as laid out by codes?
• What are the data evidence of the validity of the when and how of this?
• Can the meta data verify veracity (calibration, authentification)?
• 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 grading of a range of methodologies: e.g.

Rating	Data item standards	Digital 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 Tier 2 or 3 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.

The standardised processes 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!