Climate change and related extreme weather events pose significant threats to the viability and business continuity of organisations worldwide. Increasingly frequent and intense weather events such as floods, droughts, windstorms, heatwaves, and coastal storm surges cause local and regional disasters with far-reaching impacts. These physical climate risks are becoming a critical component of the current risk landscape. Understanding climate risks and implementing appropriate adaptation measures can be challenging due to the need for long-term data and analysis. This issue explores climate risks, their impacts, and effective assessment methods.
Understand: Grasping the Bigger Picture
Climate risks are the measurable impacts of events caused by climate elements, known as hazards. Examples include extreme rainfall, hurricanes, heatwaves, and hailstorms. Climate hazards affect systems, such as businesses, in two dimensions: exposure and sensitivity. Exposure refers to the presence of any system in the location of a climate hazard, while sensitivity describes how the hazard impacts the system. For instance, a business might lose revenue due to delayed deliveries from road closures caused by flooding. Climate risks are often measured as an aggregated indicator combining the probability and severity of events into an expected loss outcome.
Recent studies combining climate and economic sciences suggest that the economic impacts of climate-related disasters may have been significantly underestimated. GDP losses ranging from 5% to 25% are possible, particularly when considering cascading and downstream effects, such as increased commodity prices. This holds true even under moderate climate change scenarios, which are currently the most likely.
Analyze: Exploring Relevant Climate Risks and Their Implications
Numerous digital tools promise to measure climate risks, but many fail to capture the full complexity of physical climate risks, often providing only hazard metrics without connecting exposure, sensitivity, and link them into meanigful cause-effect chains. It is essential to conceptually understand the relevant climate risks qualitatively. Engaging external climate risk modeling expertise and building internal capacity for climate risk analysis is important to achive this level of understanding. The international standard ISO-14091:2021-07 offers a useful framework for effective climate risk analysis, aligning with sustainability reporting frameworks like TFCD, CSRD, EU-Taxonomy, and ISSB, thereby ensuring compliance.
The aspect of disasters can be included by considering and exploring extreme weather events and their impacts. Organisations need to collect data on climate hazards, exposure, and sensitivities. Climate hazard data can typically be obtained from open-data sources like the EU-Copernicus Climate Data Store or national weather services, while exposure and sensitivity data come from within the organisations. This data allows for the calculation of meaningful risk indicators, which can be aggregated logically, such as by climate hazard, organisational asset, or business unit. Climate risk analysis is complex, and to manage its complexity, it is advisable to start with a smaller analysis focused on one or two specific climate risks and disaster types. Once capacity and knowledge are built, the methodology can be applied organisation-wide. Digital tools can help guide and document the analysis, ensuring compliance and resource efficiency.
Apply: Anticipating Climate-Related Disasters
One key success factor in disaster management is applying weather forecasting intelligence to the organisational context to anticipate disaster impacts before they occur in order to effectively prepare and reduce material and immaterial losses. Using weather forecasts alone can be challenging and provide little applicable guidance on disaster preparation measures. Basic guidance can be derived from regional or national catastrophe warning information, though this is often not specific enough for organisations. To improve disaster preparedness, organisations should use their data on sensitivities and exposure, applying the organisational risk model to short, medium, and long-range weather forecasts. This technique, called impact forecasting, is increasingly used by disaster response agencies like the Red Cross and can be beneficial for other organisations. The ideal approach involves coupling the organisational risk model with weather forecast data, using software and machine learning, and integrating it into risk and business continuity management. Concrete impact warnings should lead to the establishment of specific plans, measures, and actions to mitigate disaster impacts. These plans should consider the uncertainty of impact forecasts and be adjusted according to forecast lead times.
Improve: Keeping Information Up-to-Date
Both climate risk analysis and impact forecasting tools should follow the PDCA (plan, do, check, act) cycle to continually improve their value. Climate risk analyses should be updated whenever new events occur, new climate science and models become available, or changes in sensitivity arise due to adaptation measures. Updated risk models also enhance the accuracy of impact warnings. An annual workshop to review the relevance and applicability of existing risk analysis and impact forecasting tools is a good practice. Organisations should establish clear guidelines on when to update analyses and impact forecasts.
Adapt: Addressing Climate Risks and Preventing Disasters
The ultimate goal of climate risk analysis and impact forecasting is to identify, monitor, and reduce relevant climate risks while increasing disaster preparedness, known as climate adaptation. Meaningful climate adaptation involves defining and evaluating appropriate organisational and infrastructure measures to reduce vulnerability. Disaster risk strategies that incorporate learning from past events and proactive measures can significantly enhance resilience. Investing in preventative measures early, before disasters strike, offers the highest return on investment compared to costly disaster recovery efforts.