Introduction

Nature underpins critical ecosystem services, which support human economic activity and societal well-being. Despite its value, nature is being degraded at an alarming rate. Scientific consensus highlights that six out of nine planetary boundaries have been breached, including biodiversity loss, freshwater depletion, and land-system change. These interconnected risks can trigger self-reinforcing feedback loops, such as Amazon dieback impacting rainfall and accelerating global climate instability. Recognising this, central banks and financial supervisors now acknowledge nature-related risks as material economic and financial concerns, necessitating robust forward-looking tools like scenarios.

Developing narratives to assess nature-related financial risks: rationale, challenges and ways forward

Challenges

Developing nature-related risk scenarios is inherently complex due to ecosystems’ interdependencies, non-linear behaviour, and tipping points. For instance, deforestation affects biodiversity, which reduces climate resilience, amplifying cascading risks. Unlike climate change, nature loss cannot be measured with a single metric like CO₂ equivalents, requiring multiple indicators to track ecosystem changes. Nature’s non-substitutability amplifies impacts, as degraded ecosystem services (e.g., pollination, freshwater) cannot be readily replaced by human-made solutions, particularly in the short run.

The “local-global trade-off” remains a significant challenge: granular local data is essential for understanding specific risks, yet scenarios must also aggregate these patterns to capture macrofinancial impacts. For example, a drought in France directly impacts agriculture but has cascading effects through global food supply chains.

Physical risk narratives

Two complementary methods are proposed to develop physical risk narratives:

ESGAP-SESi: The Environmental Sustainability Gap (ESGAP) framework assesses the gap between current ecosystem conditions and sustainability standards across 21 critical functions (e.g., soil erosion, freshwater quality). This provides a proxy for ecosystem vulnerability and identifies sectors at risk. By integrating ESGAP with tools like ENCORE, which maps dependencies between ecosystem services and economic sectors, risks can be translated into sector-specific impacts. For example, water stress in hydropower-dependent economies could severely disrupt energy prices and productivity.

INCAF-Oxford: This approach identifies physical hazards (e.g., droughts, ecosystem collapse) and maps them forward to primary economic receptors (e.g., agriculture, forestry) and backward to the drivers of nature loss. It connects hazards, such as floods disrupting water regulation, to economic impacts like reduced agricultural yields and rising food prices. For example, a drought in France disrupts surface water and air dilution services, impacting agriculture, manufacturing, and public health.

Transition risk narratives

Transition risks arise from policies and socio-economic changes aimed at reducing environmental pressures. Frameworks such as the Kunming-Montreal Global Biodiversity Framework (GBF) highlight transition drivers, such as protecting 30% of land and sea (GBF Target 3), reducing pollution risks (Target 7), and reforming harmful subsidies (Target 18). Policies like these could strand assets, disrupt trade flows, and raise costs across industries, particularly agriculture, forestry, and energy.

To manage the “local-global trade-off,” simple metrics can identify country- and sector-specific exposures. For example:

• Assessing macroeconomic dependencies on agricultural exports when implementing GBF Target 3.
• Removing harmful subsidies could impact value-added sectors reliant on government support.

Future work must develop granular exposure analyses to connect transition policies to sector-specific economic impacts globally.

Review of modelling approaches for nature scenarios

Nature-economy models

Most economic models underestimate nature-related risks due to assumptions of substitutability (e.g., replacing natural capital with technology). A strong sustainability approach, which recognises nature’s limited substitutability, is essential for realistic assessments. Models must account for non-linear ecosystem collapses and indirect value chain impacts to avoid systemic risk underestimation.

Biophysical models

Biophysical models track ecosystem changes but often lack linkages to economic impacts. Integrating biophysical and economic models can improve risk quantification, particularly in representing cascading disruptions through critical ecosystem services.

Using input-output tables and models to understand the propagation of nature-related hazards throughout value chains

Input-output (IO) models are critical for capturing cascading impacts of nature-related hazards across global value chains. For example:

• A drought in France reduces agricultural production, which disrupts downstream sectors like food processing, manufacturing, and trade.
• An EU ban on non-deforestation-free products could significantly impact Brazilian agricultural exports, leading to upstream economic losses and downstream global price shocks.

Such scenarios illustrate how local hazards propagate indirect risks globally through interconnected sectors and economies.

Conclusion and options for central banks and supervisors

To address nature-related financial risks, central banks and supervisors should:

• Develop forward-looking scenarios that balance global and local granularities for both physical and transition risks.
• Prioritise tools that integrate biophysical and economic models to account for non-linear and systemic impacts.
• Focus on data integration to bridge nature-related “data gaps” and improve accuracy in scenario calibration.
• Conduct granular stress tests to assess exposure to policies like the GBF targets and identify vulnerable sectors and economies.
• Foster collaboration with global frameworks like TNFD and expert networks to ensure consistent approaches to nature-related risk analysis.

Addressing these challenges is critical to understanding macrofinancial vulnerabilities and enabling transformative actions to mitigate systemic risks associated with nature loss.