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).
Climate Risk Assessment
Bardia Monavari, Alice Insights