DATA FLOWS — PANDION ANALYSIS
Follow the data
Landscape data flows upward through the sustainability reporting system. Value does not flow back.
The data that underpins corporate sustainability reporting, ESG scores, and green financial products originates on farms, estates, and landscapes. The people who generate and steward that data are compensated for their product. They are not compensated for their data. This is a structural inequity — and it has a specific mechanism.
The data supply chain
Sustainability data follows a consistent path from landscape to financial market. At every step, value is added by standardisation, aggregation, and distribution — and captured by the entity performing those functions. The landscape is at the bottom.
Landscape actor
Farmer, estate, indigenous community, smallholder
Generates the primary data. Bears the cost of monitoring, certification, and record-keeping.
Currently receives for produce / service:
Carbon credit, agri-environment payment, certification premium
Earns from the data itself:
Nothing
MRV and monitoring platform
Verification bodies, satellite imagery providers, CDP questionnaire infrastructure
Collects, validates, and standardises data from landscape actors. Creates the commercially legible data asset.
Currently receives for produce / service:
Methodology fees, platform fees, questionnaire administration fees
Earns from the data itself:
Platform and subscription revenue
Registry and aggregation layer
Carbon and biodiversity registries, voluntary framework databases
Aggregates across thousands of organisations. The aggregated dataset is the commercially valuable asset.
Currently receives for produce / service:
Registration fees, audit fees
Earns from the data itself:
Licensing revenue from the aggregated dataset
ESG data vendor
Bloomberg, MSCI, Sustainalytics, S&P Global Trucost
Packages aggregated sustainability data into financial data products sold to institutional investors.
Currently receives for produce / service:
Dataset subscriptions
Earns from the data itself:
Subscription revenue — a $1B+ global market, growing at 15% per year
Financial market
Asset managers, pension funds, insurers, index providers
Uses data for capital allocation and ESG product construction. Data reduces cost of capital.
Currently receives for produce / service:
Data to price risk and construct ESG products
Earns from the data itself:
AUM fees, index licensing, green bond premiums
The pattern: Each layer up the chain earns from the data that originated below it. The layer at the bottom — the landscape actor who generated or stewarded the land the data describes — earns nothing from the data itself. Their compensation ends when the produce is sold or the credit is issued.
Two payments the market treats as one
The market currently conflates two distinct types of compensation. Separating them is the clearest way to see the inequity — and the path to correcting it.
Payment 1 — the sustainability premium
What it compensates: the cost of producing sustainably
When a UK estate sells Pasture for Life-certified beef at a premium, that premium compensates for the genuine additional cost of sustainable production: higher land requirement per unit, lower stocking density, certification fees, additional labour, forgone yield.
The premium makes sustainable production economically viable. That is its entire function. It has nothing to do with data.
Duration: one-off, per transaction. Scales with: volume of produce sold. Goes stale when: the produce is consumed.
Payment 2 — the data payment
What it compensates: the commercial use of information about that production
When a brand uses a UK estate’s supply chain data in their CDP Climate disclosure, that data improves their ESG rating, reduces their cost of capital, enables green bond issuance, and satisfies their own corporate customers’ supply chain requirements.
All of that commercial value flows from the estate’s data. The estate received the premium for the beef. The estate received nothing for the data.
Duration: indefinite — data persists and is reused. Scales with: downstream commercial use. Currently exists: not at all.
The analogy that clarifies it
A musician performs at a venue.
They receive a performance fee — compensation for the time, skill, and cost of performing. The performance is also recorded. The recording is played on radio, streamed commercially, licensed to films, used to train AI music models. For each of those uses, the musician receives royalties — a separate payment for the commercial use of the information captured from their performance.
A farmer produces certified sustainable beef.
They receive a premium — compensation for the additional cost of sustainable production. The farm’s monitoring data flows up through certification databases, ESG data vendors, AI training datasets, and corporate reporting systems. For none of those uses does the farmer receive anything.
The produce premium = the performance fee. The data payment = the royalties. The sustainability data market has the first. It has no royalty system at all.
The infinite reuse problem
Carbon credits expire. Data does not.
A voluntary carbon credit represents a tonne of carbon sequestered over a specific monitoring period. When the period ends, the credit has been issued and used. It is a consumable.
The carbon stock measurements, the satellite imagery, the soil sample results, the forest inventory that underpinned that credit have indefinite useful life. They become the baseline against which future projects are measured, training data for AI carbon stock estimation models, comparison data in academic research, and historical records in national environmental accounting systems.
The landowner was paid once, for the credit period. The data continues generating commercial value in perpetuity. The landowner shares in none of it.
Where monitoring data from a single farm year ends up
Certification body — used to grant the certification and issue the premium
Standard development — cited in methodology updates and benchmark-setting
Academic research — cited in peer-reviewed papers on sustainable systems
AI training datasets — used to train agricultural sustainability models
ESG vendor scores — feeds corporate ratings for any buyer in the supply chain
Corporate annual reports — referenced in Scope 3 disclosure narratives
The farm was paid the premium in Year 1 for the produce. For none of the subsequent uses of the monitoring data has it received anything.
Why this architecture is stable
Three mechanisms keep this structure in place. Understanding them is necessary to understanding where change is — and is not — possible.
1. Legal vacuum
No jurisdiction currently defines landscape environmental data as property. Without a property right, there is nothing to license. The entity that standardises and aggregates the data creates a legally distinct asset — the database — and captures the commercial value of it. The originator of the underlying data has no claim.
2. Voluntary disclosure framing
Voluntary disclosure frameworks ask organisations to submit data freely. This creates the implicit argument that submitters have freely given their data. Once freely given, there is no legal basis for a claim on downstream commercial use. The framing of voluntary disclosure as a public good obscures the private commercial value that flows from it.
3. Derived data protection
Even where raw data access rights are beginning to exist — as with the EU Data Act for IoT-generated farm data — derived insights remain proprietary to the aggregator. The regulation opens the raw data layer; the algorithmically valuable output layer remains enclosed. Farmers may get their tractor telemetry back; they do not get a share of the AI model trained on it.
Where change is coming
The architecture is stable but not immovable. Several developments — legal, technical, and commercial — are creating the conditions for a different model.
EU Data Act — September 2025
In forceThe most significant legal development for agricultural data generators globally. Farmers can now access data generated by connected farm machinery freely and in machine-readable format, and can require third-party data sharing. Database protections cannot block access to a farmer's own monitoring data. It does not create revenue-sharing rights — but it removes technical lock-in, which is the prerequisite for any cooperative or licensing model.
CARE Principles — Indigenous Data Sovereignty
Governance frameworkCollective Benefit, Authority to Control, Responsibility, Ethics. Developed by the Global Indigenous Data Alliance, these establish the ethical governance architecture for data sovereignty. IEEE 2890-2025 (published 2025) is the first global technical standard for indigenous data provenance — machine-readable, embeddable in data pipelines. The principles extend beyond indigenous contexts to landscape-level data more broadly.
GainForest — measure-to-earn
Operational pilotIndigenous communities in Brazil are paid approximately 50 cents per tree measured for ecological data collection. The data belongs to the community that collected it; GainForest provides the payment infrastructure. Small in quantum but significant in principle: micro-payment mechanisms for environmental data collection at community scale are technically feasible.
Savimbo — Colombian Amazon
OperationalThe first approved voluntary biodiversity credit methodology designed for indigenous communities without formal land title. Community members document indicator species monthly; each hectare-month of documented conservation generates a credit priced at $6.50 to $30. Revenue flows directly to communities, not to an intermediary. Community-controlled monitoring; community-captured revenue.
Regen Network
OperationalBlockchain-based ecological credit issuance where land stewards register projects and issue ecological assets directly via the registry. Registry 2.0 (announced 2025) shifts toward broader ecological claims beyond carbon credits — potentially enabling data asset types beyond credits. Transparent provenance for monitoring data is embedded in the infrastructure.
Dairy cooperatives — the existing model
Established precedentDairy Herd Improvement Associations in the US are farmer-owned cooperatives governing their own production data. Members influence how data is used; it is not sold to outside companies without consent. Defensive rather than extractive — data is not commercially licensed externally — but proves that farmer-controlled data governance at scale is achievable. These cooperatives have operated for decades.
What a fair system would look like
A fair model does not require dismantling existing disclosure frameworks. It requires adding a second payment layer that does not currently exist.
Produce premium — unchanged
The sustainability premium on certified produce continues to exist and continues to cover the genuine additional cost of sustainable production. This is negotiated through certification schemes and market relationships and is paid per transaction. Nothing changes here.
Data licence — new
When any organisation uses data derived from a landscape’s management practices — for disclosure, reporting, AI model training, ESG product construction — the landscape actor receives a licence fee proportional to the scale and commercial value of that use. Not per kilogram of produce but per use of data. Managed collectively through a data cooperative or trust. Persistent and annually renewed.
The practical constraint: Individual licensing is impractical at farm scale. The commercially viable path is collective — a data cooperative or trust aggregating landscape-level monitoring data from multiple participants, licensing it collectively, and distributing revenue in proportion to data contributed. The dairy cooperative model proves this works. The question is whether the sustainability data market is ready to pay for what it currently receives free.
The Pandion view
Honest accounting includes data assets.
When we help a client understand their sustainability position — their natural capital, their data flows, their disclosure obligations — that includes understanding what data assets they are generating and whether the current terms of that generation are fair. Most advisers help clients submit data to platforms. We also ask what the client gets for it.
Your monitoring data may be more valuable than your carbon credits.
A voluntary carbon credit represents a tonne of carbon over a defined period. Your soil survey, your habitat baseline, your water quality records represent an information asset with indefinite commercial useful life. Understanding which is which — and who benefits from which — is part of knowing what your land is actually worth to the market.
We are watching this space closely.
The legal and commercial infrastructure for landscape data value capture is early — the EU Data Act, CARE principles, and emerging data cooperative models are forming the conditions for change. We are tracking developments, advising clients on their data rights where legal provision exists, and preparing to help with data asset mapping as a formal service when the market is ready. If you are a land manager, estate, or landscape organisation who wants to understand your data position now, we are interested in that conversation.
This analysis is Pandion’s independent view of how the sustainability data market functions. It is not an endorsement or criticism of any specific disclosure platform or data provider. We work with voluntary frameworks including CDP, and we support clients in using them well.
First published May 2026. A live document — updated as legal frameworks, market models, and precedents develop.
Related thinking
Data Value & Ownership
The broader economics of sustainability data — who controls it, who benefits, and what emerging models look like.
MRV Systems
How landscape measurement actually works — the technical infrastructure beneath the data supply chain.
Traceability
How products are tracked from origin through supply chains — the vertical dimension of landscape data.
Capital Flows
Where data connects to income — carbon credits, biodiversity markets, and green finance.
The Sustainability Framework
Where disclosure frameworks fit in the landscape — and what they demand from landscape data originators.
Digital Infrastructure
The technical architecture — APIs, digital product passports, and the platforms that move sustainability data.