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.

研究了气候-经济框架中模型不确定性的影响，以存在三种类型的资本为基础：产生碳排放的“脏”资本、不产生排放但初始生产力较低的“清洁”资本和通过研发投资增加的知识资本，从而推动绿色部门的技术创新。通过基于神经网络的全局解决方法解决了高维非线性模型框架，表明模型不确定性对最优决策和社会估值有一阶影响。考虑气候动力学的相互关联性不确定性、气候变化带来的经济损失和绿色技术变革的到来，将在技术变革和气候损害严重程度揭示前对不同资本类型的投资进行大幅调整。

气候变化、创新和不确定性的深度学习分析