Building Blocks for Central Banks to Develop Nature Scenarios

In a new report for the Inspire research network, academics explore the economic and financial risks associated with the ongoing degradation of natural systems. The report highlights the urgency for financial supervisors and central banks to deepen their understanding of nature-related financial risks (NRFR), in order to minimise the risk of default in their lending portfolios, and  avoid understating the systemic risks posed by ecological collapse. 

The report emphasises the need for methodologies that take into account nonlinear feedback effects and nature-economy tipping points. Nature-economy tipping points occur when the ecological harms caused by the economic system contribute to “large, abrupt and persistent” disruption of the ecosystem’s structure and ability to support economic activity. The authors also assess existing nature-economy models and identify key criteria for improvement.

While central banks and financial supervisors have already developed models and scenarios that assess climate risks, the report argues that these tools do not sufficiently assess the physical and transition risks resulting from the destruction of ecosystems. The authors call for more comprehensive NRFR assessments that account for broader nature-economy interactions, including  the financial or economic risks posed by degradation of all natural processes, such as climate change, biodiversity loss, water stress and declining soil quality, as well as the climate-nature trade-offs implicated in the low carbon transition, like the mineral mining required for renewable technologies.

The authors conclude that nature-economy models are available but “fragmented”, and there is a “persisting knowledge gap” on how to apply them for financial risk assessments. 

The report identifies five key areas for improving nature-economy modelling and scenarios:

• Usability for finance: To increase useability, the report recommends using more financially relevant indicators, greater granularity, and firm level data, and a unified platform. Financial actors are also encouraged to develop the skills needed to use the models and scenarios best suited to their objectives.
•  Uncertainties: To address uncertainties regarding nature–economy interactions, models must incorporate tipping points, feedback effects and compounding impacts. The report advises central banks to use multimodal ensembles combined with sensitivity analysis, to give a full and weighted understanding of tail impacts.
• Data input: To address the large data input needs of nature-economy models, the report calls for a better understanding of existing data, and advances in data availability, organisation, usability, granularity and quality.
•  Assumptions: To avoid underestimating tail impacts, the report recommends that central banks use models that incorporate feedback effects with the financial sector; as well as using a wider range of models and exploring the contrasts and complementarities between models with varied assumptions.
•  Global v local: To address the trade-offs involved in choosing between global and local scenarios, scenarios must be flexible enough to be used at both levels.