Layer 01 · Data ingestion
Measurement, without the lock-in.
Every certificate starts with a real number, and Nset is MRV-agnostic. Performance enters the rail as primary Tier 3 data through our measurement partner Hyphen, or through any provider you already use — normalized into one canonical schema, so the legal instrument never depends on a single black-box model.
01 Two ways in
Primary data, or bring your own.
Stream A · Primary data · Hyphen aMRV
Tier 3, via Hyphen’s automated MRV
For the highest-resolution number, Nset natively integrates Hyphen aMRV deployed in your program to capture primary Tier 3 data at the source — eddy-covariance flux towers reading CO₂, CH₄, and N₂O continuously, upscaled across your boundaries.
• Physics-based measurement, not modeled estimates
• Continuous, quantified uncertainty attached to every reading
• Aligned to GHG Protocol, ISO 14064, and IPCC Tier 3
Stream B · BYOM · MRV adapter
Bring-your-own measurement
Already working with a remote-sensing MRV provider? Keep them. A provider-agnostic adapter ingests their outputs into the rail’s canonical schema, so the instrument is built on your evidence — without vendor lock-in.
• Any provider submits into one canonical MRV object
• No rewriting the instrument per provider
• No dependence on any single black-box model
02 How Hyphen aMRV measures
Physics at the tower. Coverage across the shed.
Hyphen’s atmospheric MRV (aMRV) reads the land continuously with eddy-covariance flux towers, then extrapolates that measured signal across the entire supply shed — validated by a second, independent tower, so every upscaled value carries a quantified uncertainty bar.
Hardware · Eddy covariance
Flux towers reading the land directly
Eddy-covariance towers measure the CO₂, CH₄, and N₂O exchanged between land and atmosphere continuously — a physics-based method, not an assumption.
Sampling rate
10–20 Hz, continuous
Spatial upscaling
footprint → landscape
Tower footprint
→
Upscaled, full supply shed
Tower-derived fluxes link to satellite and environmental layers, extending direct measurement across every acre — not just the area under the mast.
Uncertainty quantification
90% confidence
Estimate ± band
Independent tower points
<10% of independently measured values fall outside the 90% confidence interval — error is reported with every estimate, so the claim is defensible.
Optional · N₂O soil probes
Automated soil-flux chambers isolate nitrous oxide directly — deployed as continuous monitoring paired with a tower, or a 1–2 year campaign to refine emission factors.
Scales without redesign
A continuous Tier 3 monitoring network across the landscape.
Add towers, sites, and regions without rebuilding workflows — the same architecture grows from a single farm to a whole sourcing footprint.
24/7 /365
Continuous flux, not an annual snapshot.
Tier 3
Direct-measurement inventory — above default and country factors.
03 Your baseline
Your baseline is your inventory — the reference you measure progress against.
Your base year serves as a like-for-like reference point against which to measure progress. Each reporting period is compared back to that base year — so the credibility of every reduction claim rests on the base year being built from real data, not averages. Measurement is what makes the reference hold.
Base year · the reference
A like-for-like point to measure against
Your base year is the starting inventory you compare every reporting period against — not a forecast or a business-as-usual scenario, but your own footprint, held steady enough to make progress visible.
Reductions · observed
Measured data makes the comparison real
Supplier-specific measurement shows whether emissions changed in the supply chain, not just in the factor library. When practices change and emissions fall, it counts as progress — not a model update.
Eddy covariance
· physics-based
UN-endorsed
· supports NDC reporting
GHG Protocol
· Land Sector, aligned
ISO 14064
· quantification
04 The BYOM adapter
One evidence layer, many providers.
Remote-sensing outputs vary in format, resolution, model logic, and confidence intervals. The adapter normalizes them into a canonical object so the rail — and the legal instrument — stays provider-independent.
Source
MRV provider
Remote-sensing or field provider produces observations.
→
Adapter
Normalized evidence
Mapped into the canonical MRV object.
→
Logic
Verifier / registry
Project and methodology logic applied.
→
Settle
Issuance
Instrument payout, issuance, settlement.
05 The canonical MRV object
Every observation, the same shape.
Providers submit into one schema, not vendor-specific payloads. Each field is what makes a claim auditable rather than a black box.
project_boundary
Spatial boundary of the project or supply shed
coordinates · timestamp
Where and when the observation was acquired
sensor · source
Which instrument or dataset produced it
methodology_mapping
Reference to the methodology it satisfies
model_version
Exact version of the model that produced the estimate
confidence_interval
Reported interval around the estimate
uncertainty_treatment
How uncertainty is handled and discounted
qa_flags
Quality-assurance status and exceptions
supporting_artifacts
Underlying imagery or derived outputs, where available
provider
The submitting provider, for provenance
A canonical object lets the rail ingest outputs from many remote-sensing MRV providers — established or new — without rewriting the instrument each time.
06 What the adapter guarantees
Four properties that keep the instrument honest.
01 · Normalization
One object, not many payloads
Providers vary in format, resolution, model logic, and confidence intervals. The rail needs a canonical MRV object, not vendor-specific payloads — so downstream logic never has to know who produced the data.
02 · Provenance
Every claim carries its source
Provider, sensor or data source, acquisition and processing dates, model version, methodology reference, spatial boundary, confidence interval, and whether underlying imagery or only derived outputs are available.
03 · Version control
Real change vs. model drift
An estimate can change because the land changed, the dataset changed, or the model changed. The rail distinguishes them — so a certificate never reprices on model drift rather than real-world performance.
04 · Dispute & override
Rules for when sources disagree
Defined logic for what happens when Provider A and Provider B disagree, a project developer disputes a value, or a registry or verifier rejects the data — resolved by rule, not by argument.
Built for governance
Modular, interoperable infrastructure — with attention to governance, transaction integrity, information security, data interoperability, and digital MRV. The point is a legal instrument that does not inherit the failure mode of any single model.
Frequently asked
Measurement, answered.
Is Nset tied to a single measurement provider?
No. Nset is MRV-agnostic. Performance can enter as primary Tier 3 data through our measurement partner Hyphen, or through BYOM, where any provider submits observations into a canonical schema. The legal instrument never depends on a single black-box model.
What is Bring-Your-Own Measurement?
A provider-agnostic MRV adapter. Each provider submits observations into Nset’s canonical MRV object, so the rail can ingest many providers’ outputs without rewriting the instrument and without vendor lock-in.
How does Nset stop model drift from repricing an instrument?
Every MRV claim carries version metadata. When an estimate changes, the rail distinguishes whether the real world changed, the dataset changed, or the model changed — so a certificate reprices on genuine performance, not on model drift.
Next layer
02 · Allocation engine
→
Put the rails under your supply.
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