| Session | ||
Track W8-1: Allocative and Value Effects of ESG
| ||
| Presentations | ||
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty 1Arizona State University; 2University of Wisconsin; 3University of Chicago; 4University of California Santa Barbara; 5University of Pennsylvania 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.
| ||