Net-Zero: A Comparative Study on Neural Network Design for Climate-Economic PDEs Under Uncertainty

TL;DR

This paper compares neural network architectures for solving complex, high-dimensional climate-economic models under uncertainty, demonstrating their potential to improve computational efficiency and policy modeling accuracy.

Contribution

It introduces neural network approaches tailored for climate-economic PDEs with uncertainty, highlighting architecture choices that enhance solution accuracy and efficiency.

Findings

Neural networks outperform traditional methods in high-dimensional climate models.

Proper architecture selection improves solution accuracy and computational speed.

Method enables better modeling of climate policy under uncertainty.

Abstract

Climate-economic modeling under uncertainty presents significant computational challenges that may limit policymakers' ability to address climate change effectively. This paper explores neural network-based approaches for solving high-dimensional optimal control problems arising from models that incorporate ambiguity aversion in climate mitigation decisions. We develop a continuous-time endogenous-growth economic model that accounts for multiple mitigation pathways, including emission-free capital and carbon intensity reductions. Given the inherent complexity and high dimensionality of these models, traditional numerical methods become computationally intractable. We benchmark several neural network architectures against finite-difference generated solutions, evaluating their ability to capture the dynamic interactions between uncertainty, technology transitions, and optimal climate policy. Our findings demonstrate that appropriate neural architecture selection significantly impacts both solution accuracy and computational efficiency when modeling climate-economic systems under uncertainty. These methodological advances enable more sophisticated modeling of climate policy decisions, allowing for better representation of technology transitions and uncertainty-critical elements for developing effective mitigation strategies in the face of climate change.