
Acute climate risks in the financial system: Examining the utility of climate model projections
This research examines the effectiveness of global mean temperature projections as a tool for identifying acute climate risks to the financial sector. The study highlights the limitations of current ‘top-down’ approaches and recommends the use of more granular ‘bottom-up’ methods to more accurately estimate regional-level financial risks.
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OVERVIEW
Introduction
The financial sector is exposed to physical climate risks associated with changes in weather and climate. To address them, entities like the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) have developed frameworks. Several methods are employed to evaluate the risks associated with climate change, including integrated assessment models (IAMs) and physical climate models like the ones used by NGFS.
This research examines whether current “top-down” approaches – the use of climate models to translate global mean temperature (GMT) projections onto regional scales, – are an effective way of estimating acute climate risks to the financial sector.
Assessing acute financial risks
The research challenges the efficacy of current “top-down” approaches employed to evaluate climate risks to the financial sector. The study emphasises that predictability of acute financial risks and material risk require a finer level of detail than is currently provided.
Global temperature scenarios and their limitations
Efforts to evaluate climate risk to the financial sector commonly rely on IAMs’ global temperature scenarios. These models bypass the impacts of extreme weather shocks, which are seen as the drivers of material risk. The impact of such shocks is better understood through catastrophe modelling and storylines.
Assumption underlying the NGFS methodology
Climate risk analysts often perceive that IAMs and physical climate models provide insight into how GMT projections will influence risks material to the financial system. For instance, the NGFS integrates physical climate models into ISIMIP but fails to comprehend the apparent limitations of physical climate models at projecting material extremes that translate to acute risks for the financial sector.
Conclusion
This research probes the apparent shortcomings of employing “top-down” climate scenarios to assess acute climate risks to the financial sector. Global temperature scenarios’ limitations underscore the need to consider risks resulting from precise geospatial modelling, catastrophe modelling, and storyline approaches better. Therefore, current approaches cannot adequately address granular level risks and risk modelling should be guided by “bottom-up” methods.