How can we measure direct and indirect socio-economic impacts of future climate extreme events?

This study developed a pilot approach to assess potential economic vulnerabilities to climate extremes across economic sectors and regions in the EU. By investigating in detail three past climate extreme events, the study has taken an important step forward in gaining knowledge with respect to data availability and data requirements. At the same time, it explored the practical use of modelling techniques in combining Input-Output (IO) models with a Computable General Equilibrium (CGE) model. The economic models are supplemented by narratives and a basic assessment of non-market effects to create a unique comprehensive assessment framework capturing economic as well as social and environmental impacts of climate extreme events.

The study has produced two main deliverables: (1) a final report that documents the study approach and reports on the research findings; and (2) a technical user manual written to offer technical details regarding data, model set-up and execution for experts or scientists interested in working with similar modelling approaches. The technical user manual can be seen as a handbook, allowing the expert or scientist to better understand the assessment framework.

The most important findings and lessons learned from the development of the assessment framework and the pilot case studies can be summarised as follows:

  1. IO and CGE models have the potential to model and forecast disaster impacts. However, further research is required to fine-tune the models, improve data availability and test-run the approach for other types of disasters, regions, etc.;
  2. The modelling results of the three case studies showed that the short-term indirect impacts can represent up to 49% of the direct damages, which is much higher in comparison with the usual assumptions for societal cost benefit analysis: the standard rule of thumb suggests that the indirect impacts fall within 5-15% of the direct damages. This is an important lesson for future policy-making as vulnerable sectors may have to be better protected to limit these impacts in the future.
  3. A lesson learned from the data collection process is that despite creative efforts to collect information on the indirect economic impacts as well as social and environmental impacts of disasters, data beyond direct economic losses appears scattered or non-existing. The lack of data was one of the key barriers to more accurate results in this study. Improvement of data inputs which are disaster specific as well as general data on the appropriate regional level could significantly improve the pilot approach.
  4. The combination of narratives and models increase comprehensibility of the analysis of climate extreme events, especially related to non-market effects. Narratives are also useful tools to communicate the study to the non-expert reader.