Introduction

Nature provides essential ecosystem services but its degradation is accelerating due to land cover change, overexploitation, pollution, climate change, and invasive species. This NGFS information note provides an overview of available nature-related data resources and methods to integrate them into risk assessment.

Range and use cases of nature indicators and metrics

Frameworks from NGFS, OECD, IMF, and TNFD address nature-related risks in distinct yet complementary ways. The TNFD emphasises corporate disclosure, the NGFS focuses on macro-prudential analysis, the IMF links to macroeconomic vulnerabilities, and the OECD stresses policy design. Six use cases for nature-related data are identified: micro-prudential supervision, macro-prudential oversight, monetary policy, statistical analysis and research, non-supervisory engagement, and portfolio management.

Measuring nature-related risks is more complex than climate risks due to their multidimensional, non-linear, and location-specific features.

Exploring metrics for nature risks and opportunities

The TNFD developed a total of 82 global core and additional disclosure metrics (p.16). Using five criteria — land-use change, the climate-nature nexus, multidimensionality, geographical location of impacts and dependencies, and data availability — this note identifies 50 metrics from the 146 TNFD indicators (p.18) as an entry point into the broader TNFD data landscape.

Case studies for integrating nature indicators in risk analysis

Four case studies illustrate how nature-related indicators can be integrated into risk analysis.

A joint World Bank and Bank Negara Malaysia report examined financial risks linked to deforestation, pollution, and overextraction in Malaysia, recommending mandatory nature-related data disclosure and the development of a centralised environmental database.

An environmental disaster in the Oder River in August 2022 resulted in the death of 360 tonnes of fish, approximately half the river’s stock (p.32). Real-time monitoring and genomic tools were identified as essential for risk assessment, and inter-institutional collaboration was found to enhance data synthesis.

The ECB’s assessment of euro area banks’ dependency on ecosystem services found data harmonisation challenges from misalignment between NACE and GICS classification systems. Restricted AnaCredit access was also found to limit external research and findings replication.

Research on Milan’s green infrastructure found that extending urban green networks by 25% can potentially decrease building damages and the share of affected population by up to 60% and 50%, respectively (p.34). High-resolution geospatial data was identified as crucial for nature-based solutions assessment.

Reviewing data sources for the financial assessment of nature-related risks

Key data categories include agricultural geospatial data, biodiversity and ecosystem indicators, infrastructure and asset location data, land use and ecosystem mapping, pollution data, and water use and stress mapping. Multiple databases are available but gaps in periodicity and spatial granularity persist.

Challenges for nature-related data

Data quality and availability are constrained by the need for physical sampling and geographic restrictions. Many datasets lack sufficient resolution for asset-level analysis, and standardised reporting frameworks are absent. Machine learning can identify statistical relationships in environmental data but risks oversimplifying complex processes and producing misleading evaluations.

Way forward

Public-private collaboration is needed to implement a data roadmap. Best practices include applying FAIR Data Principles, using open-source databases, and validating biophysical models with field data. To address biodiversity data gaps, recommended actions include statistical imputation approaches, citizen science, ground-based imagery, synthesis of non-English data sources, and targeted investment in data-scarce regions.

Conclusion

Available metrics and indicators can support nature-related risk assessment, but challenges around data quality, availability, and standardisation remain. Improved coordination, methodological innovation, and targeted investment in data infrastructure are essential to close nature-related data gaps.