Tuesday, February 27th @ 11:00-12:30 PM, via Zoom
More than 56% of today's global population lives in cities, set to reach 70% by 2050. 80% of global GDP is generated in cities. Cities consume two-thirds of global energy and account for more than 70% of greenhouse gas emissions.Much of this urban growth is happening in densely populated and rapidly urbanising river plains and coastlines in developing countries, where 89% of the world’s flood-exposed people live. Cities therefore play an important role in tackling climate change, both due to the efficiencies dense urban environments offer in terms of climate mitigation and adaptation measures, but also due to their exposure to growing disaster risks. Cities also increasingly depend on global, interconnected supply chains, operating in volatile, climate-challenged environments— not to mention the constant risk of earthquakes. These increased risks and interdependencies mean risk events can quickly propagate globally, driving large and sometimes catastrophic losses, and leaving cities with potentially disastrous supply gaps. Starting with (entangled) urban data and usingphysics-based, hybridised machine learning approaches,re/insurers can nowmodel risks like urban flood as well as business interruption risk propagation in supply chains so as to predict, prevent, and manage risks to reduce losses and enable urban resilience.