PhD : Financial Implications of Uncertainty in Cascading Climate Shocks

All available details can be found here:

The key research question is:
How can we move from traditional, static risk models and develop dynamic frameworks that capture uncertainty in cascading climate shocks?

The successful candidate will develop a novel Agent-Based Model (ABM) coded in Python to simulate how uncertainties from climate-related shocks cascade through the global financial system. You will explore the potential benefits of ABM beyond existing optimization paradigms in terms of incorporating uncertainty in cascading climate shocks. Your research will move beyond mapping cascades emerging from climate shocks and quantify the uncertainty they generate.

You will start by reviewing the literature and existing open-source models (see e.g. ) to identify what further developments are required to integrate data e.g. on supply chains (e.g. FAOSTAT trade matrix) and climate vulnerability (e.g. AgMIP data). You will then explore the uncertainty in a cascading climate shock related to synchronous droughts.
The primary outcome will be a simulation tool that provides a new, dynamic representation of climate-financial uncertainty under cascading shocks. This will enable decision-makers to move from assessing single risks to stress-testing against complex, cascading scenarios currently impossible to model, supporting the design of robust strategies in the face of climate uncertainty.

The project will be co-supervised by Dr. Roger Cremades (main supervisor, University of Leeds) and Dr. Celian Colon (co-supervisor, Advancing Systems Analysis Program, IIASA), providing a unique combination of expertise in complex systems science, financial modelling and cascading climate shocks. The successful candidate will be enrolled and based at Leeds and is expected to engage with IIASA online and through short in-person visits, e.g. via programs like the YSSP and the SSSM.

Students with pre-doctoral experience in the financial sector and a curiosity for complexity science.