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

• URL: http://arxiv.org/abs/2310.13200v1
• Date: Thu, 19 Oct 2023 23:58:28 GMT
• Title: A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
• Authors: Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, Joseph Huang
• Abstract summary: 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. We show there are first-order impacts of model uncertainty on optimal decisions and social valuations.
• Score: 1.8780554521958965
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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