Riccardo Rebonato, a finance professor at Edhec Business School and scientific director of the EDHEC-Risk Climate Institute, acknowledges the pressing need for financial and policy planners to forecast the implications of climate change amidst global temperature rises. The unprecedented nature of climate change, however, renders traditional statistical tools insufficient, necessitating new approaches to complement them. International organizations such as the Intergovernmental Panel on Climate Change (IPCC) and the Network for Greening the Financial System (NGFS) have endeavored to address this gap by outlining various climate warming scenarios. While these scenarios provide valuable guidance for policymakers and financial planners alike, they share conceptual features that limit their effectiveness.
The existing approach to scenario mapping, for instance, resembles a table with Shared Socioeconomic Pathways (SSPs) on one side and Representative Concentration Pathways (RCPs) on the other. Each option outlines the likely impact on variables such as economic growth, population, and technological development, coupled with each possible end-of-century warming level. According to Rebonato, this method’s flaw is its rigidity, which restricts possible outcomes and can lead to an unwarranted sense of control that prevents climate black swans from taking flight.
Rebonato argues that the primary issue is the lack of probability assigned to each story/warming combination. Although remaining probability-agnostic may seem reasonable, a scenario without a probability attached to it is of little use. Financial and policy planners require an idea of which scenarios they should prioritize in light of limited resources. Without guidance, assigning equal probabilities to narratives and projected warmings is intuitive but potentially dangerous, as some scenarios, such as the criticized RCP8.5, are unlikely yet extensively quoted.
While assigning probabilities to socio-economic narratives is challenging, Rebonato asserts that these stories ultimately translate into paths for economic growth, emissions, and technological development, and we have some information about these factors. We can build analytical tools that both track uncertainties and make good use of available information, such as Dynamic Bayesian Nets, which introduce a probabilistic dimension into the SSP/RCP framework by combining our degree of ignorance with what we do know. By doing so, we can assess the likelihood of different scenarios, facilitating better planning and risk management.
By gaining a better understanding of the full range of possible outcomes and what we should worry about, financial planners can assess the impact of climate change on equity prices, leading to better-informed investment decisions and a reduction in complacency. Rebonato concludes that probabilistic approaches complement traditional statistical tools in taming risk, particularly concerning the unprecedented challenge of climate change.
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