DATA FLOWS • GOVERNANCE & ECONOMICS
Data Value & Ownership
Who collects sustainability data? Who aggregates it?
Who sells it? Who profits?
In 30 Seconds
Sustainability data has become a $1.5+ billion industry – and growing at 23% annually. But the economics of this data are rarely examined. Smallholder farmers generate data that enables billion-dollar ESG ratings. Indigenous communities see their territories monitored by satellites they don't control. Corporations disclose data that becomes products sold back to investors.
Data Originators
Who generates the data.
- • Smallholder farmers
- • Indigenous communities
- • Supply chain workers
- • Disclosing corporations
Data Intermediaries
Who aggregates and sells.
- • ESG data providers
- • Rating agencies
- • Satellite companies
- • Traceability platforms
Value Capture
Who ultimately profits.
- • Global North platforms
- • Financial institutions
- • Technology providers
- • Rarely: originators
The uncomfortable question: Sustainability data flows upward through the system – but does value flow back down? This page explores the economics that most sustainability coverage ignores.
Where This Fits
This page completes the Data Flows section as Pillar 3: Governance & Economics. While other pages address what data flows and how it moves, this page addresses who benefits.
Pillar 1: Evidence Types
MRV, Traceability, Disclosure Data, Impact Evidence – what data proves.
4/4 complete
Pillar 2: Technology Lenses
AI in Data Flows + Digital Infrastructure – how data is handled.
2/2 complete
Pillar 3: Governance & Economics
This Page – who owns data, who captures value, who benefits.
1/1 complete
Why this matters: Data infrastructure without governance consideration risks replicating extractive patterns. Understanding value flows is essential for designing equitable sustainability systems.
The Data Value Chain
Tracing value from origin to profit. Sustainability data passes through multiple hands, with value captured at each stage.
ORIGINATION
Farmers, communities, workers, disclosing companies
Generate raw data
AGGREGATION
Platforms, registries, data providers
Collect & structure
MONETISATION
Ratings, analytics, insights sold
Value captured
Stage 1: Data Origination
Raw sustainability data is generated by those closest to the ground – often with minimal awareness of its ultimate value.
Key Originators
- • Smallholder farmers – Farm-level data (yields, inputs, practices)
- • Indigenous communities – Traditional knowledge, land stewardship data
- • Supply chain workers – Production, labour, safety data
- • Disclosing corporations – Emissions, governance, risk data
- • Project developers – MRV data for credits
Value Position
- • Compensation: Often minimal or none
- • Ownership: Frequently unclear or ceded via contracts
- • Control: Limited visibility into data use
- • Benefits: Indirect at best (market access, compliance)
Stage 2: Data Aggregation
Intermediaries collect, structure, and standardise raw data – transforming it into comparable, actionable information.
Key Aggregators
- • CDP – 24,800+ companies disclosing climate, water, forests data
- • EcoVadis – 130,000+ supplier sustainability ratings
- • Carbon registries – Verra (1.3B+ credits), Gold Standard
- • Satellite providers – Planet Labs, Maxar, Sentinel
- • Traceability platforms – Commodity-specific systems
Value Position
- • Platform economics: Network effects, data moats
- • Registry fees: Issuance, listing, transaction charges
- • Data services: API access, custom analytics
- • Verification fees: Audit and assurance charges
Stage 3: Data Monetisation
Processed data becomes products – ratings, analytics, benchmarks – sold to investors, corporates, and regulators.
Key Monetisers
- • ESG data providers – MSCI, Sustainalytics, Refinitiv
- • Credit rating agencies – Sylvera, BeZero, Calyx Global
- • Financial terminals – Bloomberg, Refinitiv embedded ESG
- • Portfolio tools – Watershed, Persefoni, Clarity AI
Revenue Models
- • Subscriptions: Annual data access fees (often $50K-500K+)
- • Per-entity pricing: Cost per company rated or analysed
- • API access: Premium pricing for real-time data feeds
- • Custom analytics: Bespoke research and benchmarking
Market scale (2025): ESG data and services exceeded $1.5 billion in 2023, growing at 23% annually. The ESG software market alone is projected to reach $5.6 billion by 2033. Sustainalytics leads the SPO market with 18.6% share.
The ESG Data Provider Economy
A consolidating oligopoly. A handful of data providers dominate sustainability information flows, with significant implications for market dynamics.
Business Model Anatomy
Revenue Streams
- • Data subscriptions: Core ESG ratings and scores
- • Analytics platforms: Portfolio screening, benchmarking tools
- • Custom research: Bespoke analysis for large clients
- • Indices and benchmarks: Licensing for financial products
- • API access: Premium pricing for integration
Key Players
- • MSCI – ESG ratings, indices, analytics
- • Sustainalytics (Morningstar) – 18.6% SPO market share
- • Refinitiv (LSEG) – ESG data in financial terminals
- • S&P Global – Corporate Sustainability Assessment
- • ISS ESG – Governance and ESG ratings
The input-output paradox: Companies disclose data voluntarily (or under regulation), then pay substantial fees to access benchmarks and ratings built from their own disclosures. The data they provide becomes a product sold back to them.
The CDP Model: Non-Profit Data Intermediary
CDP operates as a non-profit running the world's largest environmental disclosure system. Its funding model differs from commercial providers – but still raises questions about data flows.
Revenue Sources
- • Administrative fees: Companies pay to submit disclosures
- • Investor requests: Investors request company disclosure via CDP
- • Supply chain programme: Corporates request supplier disclosure
- • Grants and philanthropy: Foundation and government funding
Data Usage
- • Disclosure platform: Public scores, questionnaire data
- • Investor access: 25%+ of global assets use CDP data
- • Benchmark reports: A-List, sector analyses
- • Research: Academic and policy use
Carbon Credit Rating Agencies
A new category of data intermediaries has emerged to rate carbon credit quality – with significant influence on market prices and project viability.
Key Players
- • Sylvera – AI-driven credit ratings (A-D scale)
- • BeZero Carbon – Credit risk ratings (AAA-D)
- • Calyx Global – SDG co-benefit ratings
- • Renoster – Permanence risk assessment
Market Impact
- • Price premium: High-rated credits trade at 30%+ premium
- • Project access: Low ratings can exclude projects from buyers
- • Methodology power: Rating approaches shape market standards
- • Consolidation: Buyers increasingly require ratings
The gatekeeping question: Rating agencies assess projects often located in the Global South, using methodologies developed in the Global North. Their ratings can determine whether carbon revenue flows to local communities or not.
Smallholder Data Economics
The farmer's perspective. Smallholders generate vast amounts of sustainability data – but rarely benefit from its value.
The Data Extraction Pattern
Agricultural data flows from farms to platforms to investors – often with minimal return to the farmers who generate it.
What Farmers Generate
- • Production data: Yields, inputs, practices, timing
- • Location data: GPS coordinates, plot boundaries
- • Soil and water: Conditions, usage, quality
- • Certification data: Organic, fair trade, deforestation-free
- • Financial data: Income, costs, creditworthiness
Who Captures Value
- • Agtech platforms: Sell analytics, insurance products
- • Input companies: Target marketing, product development
- • Financial institutions: Credit scoring, risk assessment
- • Commodity traders: Market intelligence, pricing power
- • Certification bodies: Audit fees, premium access
Platform Revenue Distribution
How agricultural data platforms typically monetise farmer-generated data:
35%
Agri-insurance
Risk modelling, fraud detection
30%
Input firms
Targeted marketing
20%
Financial services
Credit assessment
15%
Other services
Research, government
The subsidy model: Platforms often provide "free" services to farmers – advisory, market prices, weather – funded by selling their data to third parties. The farmer is the product, not the customer.
Farmer Data Rights Movement
A growing movement advocates for farmer ownership and control of agricultural data. Policy frameworks are emerging, but implementation remains patchy.
Proposed Rights
- • Right to access: All data generated on their farm
- • Right to control: Consent for data sharing and reuse
- • Right to benefit: Share in value from third-party use
- • Right to portability: Move data between platforms
- • Protection from profiling: Limits on discriminatory use
Policy Developments
- • US (USDA 2024): Farmers retain ownership of programme data
- • EU (CAP 2027+): Farmer-centred data governance planned
- • India (AgriStack): Framework under development
- • Canada: Calls for data trusts, farmer-first governance
Data Sovereignty & Rights
Who controls sustainability data? Indigenous data sovereignty, national data boundaries, and emerging rights frameworks are challenging traditional data flows.
Indigenous Data Sovereignty
Indigenous communities are asserting rights over data generated from their lands and traditional knowledge – challenging extractive research and monitoring practices.
CARE Principles
- • Collective Benefit: Data should benefit Indigenous peoples
- • Authority to Control: Rights to govern data collection and use
- • Responsibility: Those using data have responsibilities
- • Ethics: Rights and wellbeing of Indigenous peoples paramount
Implementation Examples
- • Canada 2023-2026: Indigenous data strategy, TAID programme
- • Tribal census tools: Community-controlled enumeration
- • UNDRIP alignment: Free, prior, informed consent for data
- • Genetic heritage: Sovereignty over traditional knowledge data
The satellite question: Satellite imagery of Indigenous territories is captured and sold without consent. Forest monitoring for carbon credits, deforestation detection, and biodiversity assessment all rely on this data – raising questions about who should benefit from its use.
National Data Boundaries
Nations are increasingly asserting sovereignty over environmental and economic data – with implications for cross-border sustainability reporting.
Emerging Frameworks
- • Data localisation: Requirements to store data domestically
- • Transfer restrictions: Limits on cross-border data flows
- • National registries: Country-level carbon and biodiversity tracking
- • Bilateral agreements: Negotiated data sharing frameworks
Sustainability Implications
- • Supply chain visibility: May require local data partners
- • Carbon markets: National vs international registry interoperability
- • Reporting: Different data access for different jurisdictions
- • Verification: Local vs international assurance providers
Open vs Proprietary Data
The tension at the heart of sustainability data. Open data enables accountability and innovation. Proprietary data funds the infrastructure. The balance matters.
| Dimension | Open Data (e.g., Climate TRACE) | Proprietary (e.g., MSCI, Sustainalytics) |
|---|---|---|
| Accessibility | Free, public, universal access | Paid subscriptions, enterprise clients |
| Coverage | 352M+ assets (Climate TRACE) | Self-reported, often gaps in emerging markets |
| Timeliness | Monthly updates, near-real-time | Annual or quarterly cycles |
| Methodology | Independent (satellite + AI), transparent | Self-reported, methodology often opaque |
| Funding Model | Philanthropy, public grants | Subscription revenue, sustainable business |
| Gap Coverage | Includes unreported emissions | Misses "invisible" emissions |
The Open Data Case
- • Accountability: Anyone can verify claims
- • Innovation: Enables new applications, research
- • Equity: Accessible to resource-constrained actors
- • Public good: Climate data as shared infrastructure
- • Example: Climate TRACE, Global Forest Watch, OS-Climate
The Proprietary Case
- • Sustainability: Revenue funds ongoing operations
- • Quality: Investment in methodology, validation
- • Integration: Enterprise features, support, SLAs
- • Incentives: Market pressure drives improvement
- • Example: MSCI, Bloomberg, Sustainalytics
The hybrid reality: The most effective sustainability data ecosystem likely combines both – open data for baseline accountability, commercial providers for enterprise integration and specialised analytics. The question is getting the balance right.
EUDR: Who Pays for Traceability?
The cost burden question. The EU Deforestation Regulation mandates traceability data – but the economics of who pays remain contentious.
EUDR Data Requirements
What Must Be Collected
- • Geolocation: GPS coordinates of production plots
- • Deforestation status: Proof of no deforestation after Dec 2020
- • Legal compliance: Production country law adherence
- • Supply chain traceability: Chain of custody documentation
- • Risk assessment: Due diligence analysis
Timeline
- • Dec 2026: Large operators must comply
- • June 2027: SMEs must comply
- • April 2026: Simplification review due
- • Penalties: Up to 4% of EU sales, product confiscation
The Cost Distribution Problem
EU operators bear legal responsibility – but costs flow upstream to producers who often have the least capacity to absorb them.
EU Operators
- • Due diligence systems
- • Platform subscriptions
- • Audit and verification
- • Compliance staff
Traders & Processors
- • Traceability infrastructure
- • Supplier onboarding
- • Data management
- • Chain of custody
Smallholders
- • GPS devices & training
- • Plot registration
- • Documentation burden
- • Risk of market exclusion
The exclusion risk: Smallholders who cannot meet data requirements risk losing market access entirely. With no EU subsidies for producer country capacity building, the burden falls disproportionately on those least able to bear it.
Emerging Models for Equitable Value
Alternatives to extraction. New models are emerging that aim to distribute data value more equitably – though most remain early-stage.
Data Cooperatives
Collective ownership structures where data originators pool resources and share benefits. Emphasised during UN International Year of Cooperatives 2025.
Key Features
- • Collective bargaining: Negotiate as a group, not individuals
- • Shared infrastructure: Pool costs of data systems
- • Profit sharing: Revenue distributed to members
- • Governance: Democratic decision-making
Examples
- • Farmer cooperatives: Shared data platforms in EU, India
- • Industrial cooperatives: SME data exchange (EESC initiative)
- • Community data trusts: Emerging in Canada, UK
Data Trusts
Legal structures where an independent trustee manages data on behalf of data subjects, with fiduciary duties to act in their interest.
Potential Applications
- • Farmer data: Agricultural data governed by trust
- • Community monitoring: Environmental data from Indigenous lands
- • Supply chain: Worker data with consent governance
- • Research: Sustainability data for public benefit
Challenges
- • Legal complexity: Trust law varies by jurisdiction
- • Funding: How to sustain trustee operations
- • Scale: Limited examples at meaningful scale
- • Enforcement: Cross-border data governance
Benefit Sharing Mechanisms
Direct value transfer to data originators, either through revenue sharing or in-kind benefits.
Current Models
- • Carbon credit revenue: Community benefit sharing from projects
- • Certification premiums: Fair trade, organic price premiums
- • In-kind services: Free advisory, market access in exchange for data
- • Payment for ecosystem services: Direct payments to landholders
Emerging Approaches
- • Data dividends: Share of platform revenue to data originators
- • Tokenisation: Blockchain-based fractional ownership
- • Collective licensing: Cooperative negotiation of data use
- • Public procurement: Government purchasing of open data
The Pandion View
Data value and ownership is the uncomfortable conversation sustainability needs to have. We've built extensive infrastructure for data collection – but far less for ensuring value flows equitably.
Questions Worth Asking
- • Who generates the data that informs your sustainability claims?
- • Who captures value from the data flowing through your supply chain?
- • What rights do data originators have over how their data is used?
- • Are you inadvertently participating in extractive data patterns?
- • How could value flow back to those who generate the data you rely on?
What We're Watching
- • EUDR implementation: How cost burden distribution evolves
- • Indigenous data frameworks: CARE principles moving into practice
- • Farmer data rights: Policy developments in EU, US, India
- • Open data expansion: Climate TRACE and similar initiatives
- • Cooperative models: Data cooperatives proving viability
Our Perspective
Sustainability data infrastructure that replicates extractive patterns isn't sustainable. The organisations that think carefully about data value flows now will be better positioned as stakeholders – from regulators to communities – increasingly demand accountability.
This isn't just an ethical question. It's a strategic one. Data relationships built on fair value exchange are more durable than those built on extraction. And as regulatory attention increases, the cost of getting this wrong will grow.
The question isn't whether to engage with sustainability data economics – it's whether to do so proactively or reactively.
Where To Go Next
Traceability
Supply chain data and EUDR compliance
Digital Infrastructure
Standards, platforms, and registries
Social Sustainability
Just transition, indigenous rights, equity
Carbon Markets
Where data meets credit economics
Impact Evidence
Additionality, permanence, credit quality