Abstract

Physical risks arise from the potential impact that increasingly frequent and severe climate hazards may have on populations, economic sectors and assets materially exposed to climate-related risks. These risks can manifest themselves in two ways: acutely and chronically. Acute impacts refer to more frequently occurring short-duration extreme weather events such as storms and floods. Chronic impacts are longer-term and pervasive risks such as droughts or sea level rise due to global warming, that will deprive economic sectors (e.g., agriculture) in certain regions of the world. The forward-looking nature of climate risks and the inherent uncertainty about future events make it difficult to assess them using standard risk modelling methodologies. Scenario analysis offers a flexible “what-if” methodological framework that is better suited to exploring the risks that could crystallize in different possible futures.
Climate change scenarios are not about predicting the future, but come in the form of projections of what can happen or pathways of how to reach certain goals. Climate scenarios developed, e.g., by the Network for Greening the Financial System (NGFS), would be used to quantify risks. The new G20 Data Gaps Initiative (DGI) includes a recommendation on developing forward-looking physical (and transition) risk indicators to monitor the impact of climate change on the economy and the financial system. Ongoing methodological work being developed by the NGFS and the analytical work by the Financial Stability Board (FSB) related to climate-related financial risks will form the basis for the methodological guidance and reporting template for this recommendation. The IMF is in the lead, and will release a first set of indicators in an upcoming update of the Climate Change Indicators Dashboard, https://climatedata.imf.org/
At the same time, climate data might serve as a source for a bottom-up approach. The acute need for forward-looking, granular, sectorized, and geo-spatial data on physical risks can be filled with climate data vendors. These characterize physical climate risk through exposure (scores) for different climate hazards that are important drivers of business risk. Each climate hazard is comprised of several indicators, the underlying indicators capturing different dimensions of risk. Damage functions, using GDP as exposure proxy for real assets, are used to quantify the effect of specific hazards on the real assets and activities that generate financial flows (bottom-up approach; vs. top-down: estimating the relationship between aggregate economic output and changes in regional temperatures). Aggregate indicators under development, as proof-of-concept, include the percentage of socioeconomic exposure to high risks for the dimensions reflected in the Sendai Framework for Disaster Risk Reduction: the number of people affected by various disasters, the direct economic losses in relation to GDP, and damage to critical infrastructure.
A discussion on data needs for evidence-based policy making concludes.