Data quality, screening tools and value-chain evidence

TNFD work can start before the data is good — but not by presenting modelled information as measured fact. This subject grades evidence, states what each screening tool can support, prioritises upstream and downstream interfaces and keeps proxy origins visible as proxies.

Published passport

Current as at 10 August 2026

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

Level 2

Articles in this pillar

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

Decision guide · 7 min TNFD Supplier Questionnaire: What Nature Data to Request Without Overburdening SMEs

A proportionate questionnaire model covering minimum fields, commodity and location priorities, evidence tiers, confidentiality, improvement plans and alignment with ESRS and GRI …

Decision guide · 13 min ENCORE, IBAT and Aqueduct for TNFD: What Each Tool Can and Cannot Do

Screening uses, licences, data versions, resolution and the validation needed before a materiality conclusion

Decision guide · 7 min TNFD Value Chain Assessment: Suppliers, Commodities, Traceability and Data Gaps

A phased approach to prioritising upstream and downstream interfaces without waiting for perfect origin data or treating proxies as verified locations

Decision guide · 10 min

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ESG Reporting Full Stack

There is no standalone LRA course for this framework yet. The Full Stack programme covers the reporting system it sits in — materiality, data, drafting and assurance — with exercises on your own data.

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