Understanding Climate Change Risks: A New Approach to Scenario Mapping
Riccardo Rebonato, finance professor at EDEC Business School and scientific director of the EDHEC-Risk Climate Institute, believes that the unprecedented nature of climate change means that we must devise new approaches to complement the traditional statistical tools used to better monitor risk. As global temperatures rise, financial and political planners urgently need a way to forecast the implications. In this article, we will explore the existing method of scenario mapping, its limitations, and the need for a probabilistic approach to assign a probability to the various story/warmup combinations.
The Existing Approach to Scenario Mapping
The existing approach to scenario mapping is a table that sets out five socioeconomic narratives called Shared Socioeconomic Pathways (SSP) on one hand and possible warming scenarios called Representative Pathways of Concentration (RCP) on the other hand. Each option, like “Middle of the Road,” where there is slow progress towards changes in environmental behavior, states the projected impact on factors such as economic growth, population, and technological development. Each narrative should be accompanied by each possible level of end-of-century warming. This coupling is achieved using a model that combines economics and physics modules. The parameters are adjusted to reflect the socioeconomic narrative, and the models calculate the “implicit” carbon tax required to achieve the temperature target.
Limitations of the Existing Approach
Recent scrutiny has highlighted several flaws in this approach. For each “story,” the links between the macrofinancial variables are quite rigid: an assumption about economic growth results in a certain assumed level of population growth, or a particular level of technological development, and so on. This greatly limits the possible results and can create an unwarranted sense of control. There is no attempt to assign a probability to the various story/warmup combinations. In the absence of any guidance, assigning equal probabilities to projected narrations and warm-ups is intuitive, but it is also unwarranted and potentially dangerous.
The Need for a Probabilistic Approach
Assigning probabilities to socioeconomic narratives is very difficult. But if we are interested in their climate consequences, these narratives ultimately translate into pathways for economic growth, emissions, and technological development. We know less about these factors than we would like, but we have some information on economic growth, about how technological barriers limit the speed with which we can reduce emissions, on the fastest rates of decarbonization observed to date, or the link between investment in abatement technology and technological progress.
From this knowledge, however imperfect, we can build analytical tools that keep track of uncertainties and make good use of the information we have. Some interesting possibilities are being explored, such as trying to add a probabilistic dimension to the SSP/RCP framework by combining our degree of ignorance with what we know. These probabilities will never be precise, but being able to say “scenario A is 10 times more likely than scenario B” or “scenario C is much less likely than all the others” would already be a very useful step.
Expanding on the Topic: Making Better Use of Information
To better assess “what climate change may mean” for them, financial planners desperately require a better understanding of the probability of the full range of possible outcomes. They have made extensive use of NGFS scenarios, but few realize that all of these scenarios are offshoots of the “Middle of the Road” narrative. Rare events are missing altogether, and there is no way to measure their probability. As a result, planning is difficult and the risk of complacency is high.
Stock prices hardly seem to reflect the significant investment reallocation required to seriously tackle climate change and the resulting losers and winners in different industry sectors; or the aggregate deterioration of economic production that the lack of adoption of climate measures will entail. A better understanding of the probability of the full range of possible outcomes and what we should really be concerned about could change this picture.
It is crucial to make sure that climate risks are accurately assessed so that the most effective measures can be taken to mitigate impact. Not only should financial and political planners improve the existing approach to scenario mapping to effectively monitor risk, but they should make better use of the information they already have. While it may be difficult to assign probabilities to socioeconomic narratives, combining our degree of ignorance with what we know can help us build analytical tools that track uncertainties and make good use of the information we have. By doing so, we can save resources and gain a better understanding of what measures we should take to mitigate the risks of climate change.
Summary
Financial and political planners need a way to forecast the implications of climate change, but the existing approach to scenario mapping has several limitations. While the socioeconomic narratives and warming scenarios are coupled using a model that combines economics and physics modules, the links between the macrofinancial variables are quite rigid, resulting in a limited range of possible scenarios. The existing approach also doesn’t assign probabilities to the various story/warmup combinations, making planning difficult and increasing the risk of complacency. Instead, scientists and planners need to make better use of available data and combine their degree of ignorance with what they know to build analytical tools that track uncertainties and make good use of existing information. Doing so can help us better understand the probability of possible outcomes and take effective measures to mitigate climate change risks.
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Riccardo Rebonato is a finance professor at EDEC Business School and scientific director of the EDHEC-Risk Climate Institute
As global temperatures rise, financial and political planners urgently need a way to forecast the implications. However, the unprecedented nature of climate change means that we must devise new approaches to complement the traditional statistical tools used to better monitor risk.
International bodies, including the Intergovernmental Panel on Climate Change (IPCC) and the Network for the Greening of the Financial System (NGFS), have tried to fill the gap by setting up several different climate warming scenarios. These have provided much-needed guidance for policymakers and financial planners, from investment managers to corporate executives. But they all share conceptual features that limit their usefulness.
Think of the existing approach to scenario mapping as a table, which sets out five socioeconomic narratives (so-called Shared Socioeconomic Pathways, or SSP) on the one hand and possible warming scenarios (Representative pathways of concentrationor CPR) on the other.
Each option, eg “Middle of the Road”, where there is slow progress towards changes in environmental behaviour, states the projected impact on factors such as economic growth, population and technological development. Each narrative should be accompanied by each possible level of end-of-century warming.
Riccardo Rebonato: assigning probabilities to socioeconomic narratives is difficult but more beneficial
This coupling is achieved using a model that combines economics and physics modules. The parameters are adjusted to reflect the socioeconomic narrative, and the models calculate the “implicit”carbon tax” required to achieve the temperature target.
However, recent scrutiny has highlighted several flaws in this approach. For each “story”, the links between the macrofinancial variables are quite rigid: an assumption about economic growth results in a certain assumed level of population growth, or a particular level of technological development, and so on. This greatly limits the possible results and can create an unwarranted sense of control. Weather black swans are shot down before they can take flight.
Health impact: Smoke from wildfires in Canada makes New York’s air quality the worst in the world as of June 2023 © Eduardo Munoz Alvarez/Getty Images
However, there is a deeper problem: there is no attempt to assign a probability to the various story/warmup combinations. There are good reasons to remain probability agnostic. However, a scenario without any associated probability is of little use. Precisely because resources are limited and the scale of a serious reduction push is little short of a war effort, financial and political planners need an idea of which scenarios they should be most concerned with.
In the absence of any guidance to the contrary, assigning equal probabilities to projected narrations and warm-ups is intuitive. But it is also unwarranted and potentially dangerous. For example, one scenario, RCP8.5, has been criticized in the scientific journal Nature Because it is virtually impossible, however, it is one of the most cited scenarios in applied work.
Is there a way out of this dead end? Does the unexplored nature of the problem condemn us to live without probabilities? Not necessarily. Assigning probabilities to socioeconomic narratives is very difficult. But if we are interested in their climate consequences, these narratives ultimately translate into pathways for economic growth, emissions, and technological development.
We know less about these factors than we would like. But we have some information on economic growth; about how technological barriers limit the speed with which we can reduce emissions; on the fastest rates of decarbonization observed to date; or the link between investment in abatement technology and technological progress (what economists call “learning by doing”).
From this knowledge, however imperfect, we can build analytical tools that keep track of uncertainties and make good use of the information we have.
Some interesting possibilities are being explored. Dynamic Bayesian networksFor example, trying to add a probabilistic dimension to the SSP/RCP framework by combining our degree of ignorance with what we know. These probabilities will never be precise, but being able to say “scenario A is 10 times more likely than scenario B” or “scenario C is much less likely than all the others” would already be a very useful step in the right direction.
This article is part of a report on the FT Finance Masters Rankings for 2023, due to be published on Sunday.
This will make the difference. Financial planners desperately want to assess “what climate change may mean” for them. They have made extensive use of NGFS scenarios, but few realize that all of these scenarios are offshoots of the “Middle of the Road” (SSP2) narrative. Not surprisingly, rare events are missing altogether and there is no way to measure their probability. As a result, planning is difficult and the risk of complacency is high.
Stock prices hardly seem to reflect the significant investment reallocation required to seriously tackle climate change and the resulting losers and winners in different industry sectors; or the aggregate deterioration of economic production that the lack of adoption of climate measures will entail.
A better understanding of the probability of the full range of possible outcomes and what we should really be concerned about could change this picture for the better.
https://www.ft.com/content/b03691be-19ef-4164-9a74-7592d7c73457
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