RESEARCH

We emphasize and encourage links between academic researchers and practitioners at financial institutions to bring theoretical techniques to bear on realworld issues.

Climate Risk and Sustainable Finance

Financial Decision-Making Under Climate Uncertainty:

A Machine Learning Approach for Quantifying the Physical Impacts of Climate Change

The aim of this project is to create a comprehensive framework for financial-decision makers to better understand the sources of physical climate uncertainty on real assets. An evaluation will be completed of the barriers within the Canadian (and global) financial system for reliable physical climate risk models. In addition, the latest advancements in machine learning, probabilistic modelling, and cloud computing and its applications for quantifying future climate risk will be used. Machine learning and data science methods will be applied to analyze high resolution climate simulations, and generate asset-level climate risk insights for energy (e.g. hydropower in British Columbia) and agriculture (e.g. soybean production in Alberta).

 

THEME

Climate Risk Assessment

PROJECT LEAD:

Bardia Monavari, Alice Insights

 


PUBLICATIONS