A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
Michael Barnett William Brock Lars Peter Hansen Ruimeng Hu Joseph Huang
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.