A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty

TL;DR

This paper employs deep learning to analyze how uncertainty in climate-economics models influences optimal investment decisions and social valuations, highlighting the importance of technological change and interconnected uncertainties.

Contribution

It introduces a neural-network-based method to solve a complex climate-economics model with multiple capital types and uncertainty, revealing first-order impacts on policy decisions.

Findings

Uncertainty significantly affects optimal investment in green and dirty capital.

Technological innovation influences climate policy and economic outcomes.

Accounting for multiple uncertainties alters investment strategies in climate models.

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.