A deep learning analysis of climate change, innovation, and uncertainty

A deep learning analysis of climate change, innovation, and uncertainty
Prof. Ruimeng Hu
18:00 (BST), May 29th, 2024
Abstract
We study the implications of model uncertainty in a climate-economics framework with three types of capital: “dirty” capital that produces carbon emissions when used for production, “clean” capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R&D investment and leads to technological innovation in green sector productivity. To solve our high-dimensional, non-linear model framework, we implement a neural-network-based global solution method. We show there are first-order impacts of model uncertainty on optimal decisions and social valuations in our integrated climate-economic-innovation framework. Accounting for interconnected uncertainty over climate dynamics, economic damages from climate change, and the arrival of a green technological change leads to substantial adjustments to investment in the different capital types in anticipation of technological change and the revelation of climate damage severity.
Our Speaker
Dr. Hu is an assistant professor jointly appointed by the Department of Mathematics, and Department of Applied Probability and Statistics, at the University of California, Santa Barbara (UCSB), USA. Her research includes machine learning, financial mathematics, game theory, and stochastic partial differential equations. Her research has been supported by NSF and the Simons Foundation. She has published 20+ papers in top journals including Mathematical Finance, Notices of AMS, ICML, SIAM Journal on Control and Optimization, and SIAM Journal on Financial Mathematics. She is currently an associated editor of Digital Finance and has co-edited a special issue on machine learning in finance for Mathematical Finance.