This content has been archived. It may no longer be relevant
The views and opinions expressed in this article are those of the thought leaders as individuals, and are not attributed to CeFPro or any particular organization.
By Christel Saab, Unit Chief, Environmental and Social Risk Management, Inter-American Development Bank
Can you provide some examples of identification practices to capture risk in systems?
The capture of risk information in systems is crucial as it allows for risk monitoring, reporting, and understanding of how risks can impact companies and their operations. Because of their nature, some types of risks may pose challenges in the way we integrate them into existing risk managements systems and processes. For instance, the identification and impact assessment of climate change risks and drivers can be done by using qualitative and quantitative alternatives. In qualitative assessments, climate risk screening tools allow entities to identify physical hazards and evaluate variables such as criticality and vulnerability. Moreover, the use of climate scenarios and stress testing analyses are valuable tools to forecast and understand impacts under plausible and/or extreme conditions. Finally, the quantification of risks, either through cost-benefit analyses or climate value at Risk (VaR), also provide valuable insights to assess the financial viability of projects due to impacts of a changing climate.
The Inter-American Development Bank (IDB) introduced in 2018 a new methodology to assess disaster and climate change risk in the projects it finances and reinforced its commitment to address climate change in its Vision 2025. The Disaster and Climate Change Risk Assessment Methodology (DCCRA) provides practical guidance on how to integrate disaster and climate change risk considerations into projects life cycles in a meaningful and relevant way. The methodology takes a phased approach that allocates resources commensurate with the level of project risk. It enables project teams to: (1) classify hazard exposure based on geospatial data, including the use of climate scenarios; (2) adjust the classification based on criticality and vulnerability; and (3) conduct qualitative and quantitative analyses to understand, measure, and model the risk.