Climate Scenario Analysis: Not Crystal Balls, But Ball-Park Figures

Climate Scenario Analysis: Not Crystal Balls, But Ball-Park Figures

Firms around the world are adopting climate scenario analysis for business planning and to disclose in line with the International Sustainability Standards Board (ISSB) and the Taskforce for Climate-related Financial Disclosures (TCFD). Many organizations, however, may create new risks by misreading the underlying models.

A paper published by the Institute and Faculty of Actuaries this month offers a warning to financial institutions rushing into climate scenario analysis without appreciating the limitations. Putting too much faith in models, which commonly underestimate risk due to mathematical restrictions and incorporate over-simplified assumptions, has already led to many incidents. One notable example is the July 2022 crisis in the UK wherein pension funds struggled to meet short-term liquidity needs.

To deal with these limitations, best practices for scenario analysis include:

• Acknowledging the built-in uncertainty and limitations.
  Organizations must first appreciate the limitations of their internal data-gathering, proxy data and climate models. Supply chain data, incorporating supplier locations for physical risk analysis and carbon emissions for transition risk analysis, can be valuable inputs for scenario analysis. However, limited direct data from suppliers and unspecific industry averages hamper accuracy, which impacts scenario analysis outputs. In addition to assessing the dependability of data inputs, leaders should test the reliability of climate models. Here, it is good practice to use historic data on natural catastrophes. However, as climate change is likely to lead to previously unheard-of extreme weather events, this test should be complemented by additional, edge-case-based assessments.

• Running multiple scenarios for comparability and to avoid bias.
  Using a range of different scenarios ensures comparability and diversity. The executives we interviewed who were conducting scenario analysis all used three scenarios. Most scenario methodologies recommend three to four scenarios as an optimal number, to avoid the bias that can occur when using two scenarios at opposite ends of the temperature spectrum, or just relying on one. Due to cognitive biases, the use of two opposite scenarios can result in oversimplification, where one is considered ‘good’ and the other ‘bad’, while using just one often results in overly optimistic planning.

For more details on how to approach scenario analysis, check out our latest report: Best Practices: Climate Scenario Analysis.