Corporate sustainability reports focus on company risk and miss cumulative environmental pressures; a prioritized set of mandatory disclosures and an open, audited environmental-data repository would strengthen oversight and unlock AI-enabled risk models. Treating environmental data with financial-grade standards would improve comparability, reduce greenwashing, and let investors and regulators better price systemic environmental risks.

Main Finding

Corporate sustainability reporting, as currently practiced, fails to provide the scientifically relevant, standardized, and open data needed to manage increasing nature- and climate-related systemic risks. Making a targeted set of environmentally material disclosures mandatory, science‑informed, spatially explicit (where, what, how much), and publicly available in an open repository would greatly improve investor decision-making, corporate accountability, and the alignment of capital with sustainability goals.

Key Points

• Market failure in ESG data:
  • ESG ratings are proprietary, non‑transparent, and diverge widely because underlying corporate disclosures are non-standardized and often voluntary. This creates information asymmetry between companies and investors.
• Financial vs environmental materiality mismatch:
  • Current reporting focuses on what is financially material to individual firms; this excludes many nature-related pressures that are cumulative, spatially heterogeneous, and not immediately financialized (unpriced externalities).
  • Carbon disclosures are an exception where financial and environmental materiality overlap; most other pressures (land use, water abstraction, pollutants, biodiversity impacts) are poorly captured.
• Relative vs absolute measures:
  • Reporting emphasizes intensity/relative metrics (e.g., emissions per revenue) rather than absolute totals, masking aggregate contributions to planetary limits.
  • Absolute totals are necessary to assess cumulative impacts and progress against global targets and planetary boundaries.
• Three core requirements proposed:
• Disclose environmentally material information that is scientifically identified (focus on “where, what, how much”).
• Make non‑financial disclosures mandatory and extend coverage across supply chains.
• Make disclosures transparent and publicly accessible via an open‑access repository, with data treated with the same rigor as financial reporting.
• Policy context and threat of dilution:
  • EU CSRD represented major progress but faces rollbacks/simplifications that risk limiting disclosure scope (e.g., raising employee thresholds), undermining data availability for impact and risk assessment.
• Institutional and market effects:
  • Mandatory, structured reporting would catalyze internal controls, governance changes, and enable market discipline via investor scrutiny.
  • An open, standardized disclosure infrastructure would reduce dependence on opaque third‑party ratings and enable reproducible analyses.

Data & Methods

• Nature of the paper: Perspective / synthesis piece (conceptual analysis and literature review), not an original empirical study.
• Sources: Synthesizes academic literature (on planetary boundaries, biodiversity loss, ESG rating divergence), policy and regulatory documents (CSRD, ESRS, TNFD, ISSB, UK and EU regulations), and market analyses.
• Illustrative examples:
  • Cites empirical literature documenting divergences in ESG ratings and limitations of current ESG data.
  • Reviews materiality assessments of eight leading European companies across priority sectors to illustrate variability in what firms deem material (example used to show inconsistency in biodiversity prioritization).
• Conceptual framing: Proposes operational criteria for “environmentally material information” (where, what, how much) and contrasts absolute vs. relative metrics.
• No new quantitative dataset or econometric estimation is presented; recommendations derive from synthesis of evidence and policy analysis.

Implications for AI Economics

• Data availability and quality
  • Standardized, mandatory, open disclosures would create high‑quality, auditable training/feature datasets for AI models used in finance, risk assessment, and policy evaluation. This would reduce noise from proprietary, inconsistent ESG inputs and improve model comparability and reproducibility.
  • Spatially explicit “where, what, how much” data enable richer geospatial and causal modeling (e.g., linking firm activity to local ecosystem services, supply‑chain exposure, physical climate risk).
• Model design and evaluation
  • AI/ML economics models should move from using black‑box ESG scores toward inputs that include absolute measures, location, and activity type. This improves ability to model systemic, cumulative risks and stress‑test portfolios under planetary boundary constraints.
  • Researchers must account for new forms of missingness and reporting incentives (selective disclosure, greenwashing); robust methods (causal inference under selection, sensitivity analyses, generative models for missing-data imputation) will be needed.
• Market structure and strategy
  • Greater disclosure standardization will reshape business opportunities: proprietary ESG rating firms may lose some informational rents, while AI firms providing analytics on standardized open data can scale more transparently.
  • Asset managers using AI for allocation, hedging, or stewardship will be able to embed nature‑related risk factors into optimization and factor models, potentially changing capital flows and sectoral valuations.
• Systemic risk modeling and macroprudential policy
  • Open, firm‑level environmental data allows macro/financial‑stability models (network contagion, stress tests) to incorporate nature‑related exposures and feedbacks between real economy shocks (e.g., biodiversity collapse, water scarcity) and financial outcomes.
  • AI-driven scenario analysis and counterfactual simulations become more credible with standardized absolute measures and spatial granularity.
• Practical research directions & cautions
  • Empirical researchers should: (a) test how mandatory disclosure scenarios affect asset prices and capital allocation (event studies, difference‑in‑differences), (b) simulate portfolio-level exposure to cumulative nature risks, and (c) develop spatially explicit valuation models combining remote sensing and firm disclosures.
  • Governance and fairness: open data reduces some opaqueness but introduces risks (privacy, competitive harm, uneven reporting capacity). AI economists should study distributional impacts (which firms/regions are more likely to be penalized) and design models that mitigate bias from unequal disclosure capacity.
  • Validation & auditability: Treating environmental data like financial data implies audit standards; AI models built on these data should be auditable and documented (model cards, provenance), enabling regulatory and public scrutiny.
• Operational opportunities for AI:
  • Use AI to harmonize heterogeneous historical disclosures, link supply‑chain nodes, and fuse remote sensing with corporate reports to estimate unreported pressures.
  • Develop explainable models for investors and regulators to interpret nature‑related exposures and policy impacts.

Overall, the paper’s call for mandatory, science‑based, spatially explicit, absolute environmental disclosures is directly relevant to AI economics: it would materially improve input data for economic and financial ML models, enable more credible systemic risk and policy analysis, reframe market incentives, and require careful methodological responses to disclosure incentives and data governance.