Climate change and environmental degradation constitute a growing threat to the stability of societal and economical systems. The observed and anticipated escalation in the frequency and intensity of extreme weather events under future emission scenarios, combined with the projected long-term shifts in climate patterns and consequential impacts on biodiversity, have the potential to significantly affect specific sectors such as insurance and finance leading to significant economic damages on a local to global scale.
In recognition of this challenge climate risk assessments have experienced amplified attention in both the academic and private spheres, leading to initiatives such as the ‘Network for Greening the Financial Sector’ (NGFS) and the ‘Task Force on Climate-Related Financial Disclosure’ (TCFD) and a growth in climate risk services aiming at setting standards and frameworks as well as the provision of comprehensive climate impact information for the private sector and financial institutions.
The need for more adequate risk assessment poses new academic challenges: the accurate representing extreme events and their compounding and cascading effects on high spatial resolution and the integration of non-linearities associated with tipping elements in the climate system to avoid an underestimation of physical climate risks.
Therefore, providing a platform to foster interactions between scientists, economists and financial experts is urgently needed. With the goal of facilitating such dialogue, this session aims at providing a platform for actors from academia and the private sector to exchange information on strategies for assessing climate risk.
The session is organised under three main pillars:
-Physical Climate Risks: Trends, Processes and Modelling
-Identifying and Managing Climate Risks
-Quantifying Damages and Impacts from Climate Risks
We encourage submissions on:
Innovative climate risk modeling for
-Chronic and Acute Climate Risks
-Compound Events and Cascading Impacts
-Model Evaluation of Extreme weather events
AI and Machine learning frameworks for
-Bias adjustment Methods
-Downscaling Methods
-Fast climate models and emulators
Climate hazard indicators and their projections for specific sectors:
-Food, Energy, Insurance, Real Estate
-Supply chains
Impact data collection and empirical damage assessments
Global and local damage functions
Climate – Nature nexus