تقرير
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
| العنوان: | A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty |
|---|---|
| المؤلفون: | Barnett, Michael, Brock, William, Hansen, Lars Peter, Hu, Ruimeng, Huang, Joseph |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Finance |
| مصطلحات موضوعية: | Economics - General Economics, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.13200 |
| رقم الأكسشن: | edsarx.2310.13200 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |