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TNFD Data and Evidence Quality: Primary, Secondary, Proxy and Geospatial Information

A practical hierarchy, confidence method, metadata register and improvement roadmap for nature-related information

Who this is for A 7-minute read for reporting teams working through Data quality, screening tools and value-chain evidence, and for reviewers testing whether the evidence behind it holds.

Published passport

Current as at 10 August 2026
RK Reviewed by Dr Ross KurinkoLinkedIn Strategic ESG Advisor · IFRS S1 & S2 / GRI / ESRS expert GRI Certified Global Trainer · PhD, University of Cambridge · ESG-AI expert 15+ years on FTSE 100 & Fortune Global 500 disclosures Canary Wharf, London LRA educational guidance · Not issued or endorsed by TNFD

Edition written against

TNFD Recommendations v1.0 (September 2023); related guidance as stated

Published

10 Aug 2026

Knowledge Hub guide

Last reviewed

10 Aug 2026

Short answer

The answer, before the reasoning

TNFD work can start before every datapoint is primary or perfect, but the organisation should not disguise proxy or modelled information as measured fact. Primary evidence comes directly from operations, counterparties, sites or affected stakeholders.

Authoritative secondary evidence comes from credible external sources with documented methods. Proxy evidence estimates a missing variable using a representative relationship. Geospatial information can fall into any of these categories depending on the coordinate, geometry, layer and validation. A controlled register should record provenance, edition, period, geography, resolution, licence, confidentiality, coverage, transformations, assumptions, confidence and reviewer. Improvement should focus first on uncertainty that could change a DIRO, priority location, metric, target, financial effect or public claim.

Figure 1. Show the evidence hierarchy.

Quick orientation

At a glance

Applies to
LEAP analysis, priority locations, value chains, models, metrics, targets and assurance evidence
Primary decision
Is the information fit for this decision and how should uncertainty be disclosed?
Key sources
TNFD Recommendations, LEAP and TNFD nature-data roadmap
Common confusion
Primary data is always good; secondary or geospatial data is always weak

Why evidence quality is decision-specific

The same dataset can be suitable for one use and unsuitable for another. A sector proxy can select suppliers for engagement but may not support a site claim. A global protected-area layer can screen a coordinate but cannot establish habitat condition or actual impact. A verified coordinate may be primary location evidence, while the ecological layer overlaid on it is secondary. The team should therefore define the decision and required confidence before grading the source.

In practice

A practical hierarchy

Class Definition Examples — Typical use
Primary / direct Created or supplied directly by the organisation, site, counterparty or affected party Coordinates, meters, permits, site surveys, borrower/investee data, grievances — Material decisions and metrics when controlled and validated
Authoritative secondary Credible external information produced with documented methods Government datasets, conservation layers, peer-reviewed studies, basin models — Screening, context, corroboration and gap filling
Proxy / modelled Representative estimate used because the target variable is unavailable Sector averages, spend models, estimated origins, modelled footprints — Prioritisation and provisional analysis with explicit limits
Unknown / uncontrolled Insufficient provenance, method, rights or review Undated screenshots, copied scores, undocumented transformations — Not suitable for material conclusions until controlled

Primary does not automatically mean reliable

A supplier questionnaire is primary but may be self-reported, incomplete, based on a different period or interpreted under inconsistent definitions. A site coordinate may identify the office entrance rather than the operating footprint. A grievance register may exclude cases held by contractors. Direct evidence still needs population controls, cut-off, ownership, reconciliation and review.

Secondary evidence needs metadata and version discipline

Nature reporting relies on external datasets because organisations cannot independently measure species distribution, ecosystem condition and hydrological risk everywhere. The source register should preserve publisher, dataset name, edition, extraction date, spatial and temporal coverage, resolution, licence, transformations and known limitations. A score separated from its methodology is weak evidence.

Proxy information: visible, governed and temporary where material

A proxy is a deliberate substitute: sector ratings for a borrower with unknown activities, city coordinates for a supplier with no site address, average water use per tonne, or estimated commodity origin. The register should explain why the proxy is representative, how uncertainty is treated, what decision it supports and when it will be replaced or reassessed.

Geospatial quality controls

Coordinate provenance. GPS survey, cadastral record, counterparty submission, public address or automated geocoder.

Geometry. Point, polygon, concession, catchment, supply area or administrative centroid.

Reference system. Coordinate reference system, transformation and spatial-join method.

Layer date and resolution. Dataset vintage, pixel/polygon resolution and suitability for the pressure pathway.

Buffer rationale. Ecological, hydrological, legal or risk basis; not a universal distance rule.

Confidentiality. Sensitive species, communities, borrower sites and commercial locations may require restricted access and aggregation.

In practice

A six-dimension confidence method

Dimension High-confidence characteristics Downgrade trigger
Provenance Named source owner and controlled extraction Unknown origin or copied output
Time Period fits the decision and refresh is defined Old data in a fast-changing context
Spatial fit Coordinate and layer suit the site and pathway Country or centroid proxy used for a site conclusion
Method Definitions and transformations documented Opaque model or composite score
Completeness Population and coverage reconciled Material locations or portfolios omitted
Validation Independent challenge or specialist review No review where the result drives a material decision

Minimum metadata and access fields

Stable evidence ID linked to a DIRO, metric, target or claim.

Publisher/data owner and source contact where relevant.

Document, dataset or model name, version, release and extraction dates.

Geographic coverage, geometry, resolution and coordinate system.

Population, value-chain stage, time period and boundary.

Definitions, units, transformations and calculation method.

Licence, commercial-use and redistribution conditions.

Assumptions, proxy rationale, uncertainty and limitations.

Confidentiality/access class, retention period and reviewer.

Specialist validation and escalation

Specialist input is most important where screening could change a material credit, investment, operating or claim decision; where a legal or ecological designation is uncertain; or where the organisation proposes to describe restoration, net gain, no impact or nature-positive outcomes. Validation may involve ecologists, hydrologists, geospatial specialists, engagement specialists, legal counsel, model-risk teams or data owners.

Four-stage improvement roadmap

Stabilise the register. Inventory current sources, assign IDs and classify evidence.

Prioritise material gaps. Focus on gaps that could change locations, DIROs, metrics, targets or claims.

Improve through counterparties and targeted validation. Request site/activity data and commission specialist work for high-consequence decisions.

Embed controls. Automate metadata, access, versioning, lineage, review, retention and reassessment while preserving judgement.

Hypothetical example

A consumer-products group has 4,000 direct suppliers but exact operating coordinates for only 18%. It maps suppliers by commodity, country and spend, then uses sector and country proxies to prioritise 300 suppliers. It requests coordinates and production data from 80 priority suppliers, applies IBAT and Aqueduct overlays to verified locations, and escalates 22 matches for local evidence. The disclosure reports coverage and confidence bands rather than implying that all suppliers received the same site-level assessment.

Hypothetical scenario

Illustrative wording - adapt to facts

<p>We classify evidence as direct, authoritative secondary or proxy/modelled information and assign confidence based on provenance, period, spatial fit, method, completeness and validation. Geospatial results record coordinate source, geometry, layer version, resolution and spatial-join method. Proxies support prioritisation where direct data is unavailable and are not presented as measured performance. High-priority conclusions receive targeted validation. The current assessment covers [population]; principal limitations are [gaps], with improvements planned by [period].</p>

Illustrative only. It shows how the decision is made, not wording that can be copied or relied on.

Common mistakes

Calling self-reported data verified. Primary describes provenance, not assurance.

Using one confidence score without dimensions. The nature of the weakness becomes invisible.

Losing dataset versions during spatial joins. The result cannot be reproduced or updated.

Treating an address geocode as a facility footprint. The point may not represent the nature interface.

Publishing exact sensitive locations unnecessarily. Transparency should not create ecological, community or security harm.

Keeping proxies indefinitely. Every material proxy needs an owner and improvement trigger.

Readiness

Checklist

  • The decision and required confidence are defined.
  • Source class, owner, edition, period and coverage are recorded.
  • Licences and confidentiality are approved.
  • Coordinate, geometry, resolution and spatial method are documented.
  • Assumptions and proxies are explicit.
  • Material conclusions have proportionate validation.
  • Disclosure states coverage, limitations and improvement actions.

Myth versus reality

Figure 2. Show confidence dimensions and the improvement roadmap.

Myth

TNFD is not credible until the organisation has primary site data across the entire value chain.

Reality

Organisations can start with the best available information and a proportionate scope. Credibility comes from transparent classification, metadata, confidence, validation, limitations and a targeted improvement plan.

Related learning path

Prerequisite: TNFD Explained - Recommendations, LEAP, DIROs and how to start.

Apply next: TNFD LEAP Approach - a step-by-step guide.

Evidence: Geospatial Data for TNFD - coordinates, maps, data quality and confidentiality.

Advanced: TNFD metrics, targets and the limits of composite biodiversity scores.

Rule

AI / FAQ answer

<p>Use the best available TNFD information, but classify it honestly. Primary data is direct evidence from operations, counterparties, sites or stakeholders; secondary data comes from credible external sources; proxy data estimates missing information; and geospatial data can be primary, secondary or proxy depending on the coordinate and layer. Record metadata, assumptions, coverage, confidentiality and confidence, validate material conclusions, and improve the evidence that could change a DIRO, metric, target or claim.</p>

Take it with you

The checklists as a working spreadsheet

Every checklist and table on this page, with empty status, owner and evidence columns for your team to fill in and keep.

Download .xlsx

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